see attached
Part I- minimum 4 pages (1000 words double spaced- APA)
Part 2- minimum 4 pages not counting reference page-APA— included 3 articles from the school site as attachments.
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ECO 2302, Principles of Macroeconomics 1
Upon completion of this unit, students should be able to:
7. Illustrate monetary theory using the supply and demand model.
7.1 Identify the three functions of money and six qualities of ideal money.
7.2 Summarize tools of monetary policy that are used by the
.
7.3 Describe contractionary and expansionary monetary policy and when they are used.
Course/Unit
Learning Outcomes
Learning Activity
7.1
Chapter 13
Unit VII Assignment
7.2
Unit Lesson
Chapter 14
Unit VII Assignment
7.3
Unit Lesson
Chapter 15
Unit VII Assignment
Chapter 13:
and the Financial System
Chapter 14: Banking and the Money Supply
Chapter 15: Monetary Theory and Policy
Unit Lesson
Unit VII is the largest unit in this course in terms of the number of textbook chapters that are covered;
however, the material in Unit VII tends to be easier to understand. In this unit, you will be learning about
money and the Federal Reserve. The discussion below focuses first on money itself. This is followed by a
discussion of the Federal Reserve and its functions. Finally, different types of monetary policy are addressed,
including when they are used.
Money
Money is a term that virtually everyone has heard. Dire Straits sang about money for nothing in 1985. Ed
Sheeran sang about not wanting your money in 2019. Even Emily Dickenson wrote about being rich with
money until she was taught what true wealth was in the poem Your Riches Taught Me Poverty. However,
have you ever stopped to think about what money actually is? As you move through this section, think about
your own perception of money. Chances are, the formal definitions of money will closely resemble your
thoughts. For more information, watch the brief video Money. A transcript and closed captioning are available
once you access the video.
UNIT VII STUDY GUIDE
Monetary Policy and Banking
https://c24.page/66kd6pj2e3s3twgarq54kxmuy3
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Before money existed, people used bartering to exchange goods and services. For example, someone may
have had five horses but did not have firewood. Another person may have had an abundance of firewood but
did not have a horse. The person with the horses could trade one for some firewood. This type of exchange is
called a barter.
This sounds like it worked well. However, we would have to ask, how much firewood is worth one horse? The
two people involved would have to negotiate this exchange rate. Now, add in the fact that the person with the
horse may also need corn from someone else, cloth to make clothes from another, and milk from yet another
person. As McEachern (2019) points out, increasing the number of goods that are traded increases the
exchange rates. Further, if the person who has firewood wants to purchase a house, he or she might be hard
pressed to find someone with a house who needs a massive amount of firewood. This last problem is an
example of high transaction costs, and this is what brought about the creation of money.
Money refers to anything that is widely accepted for payment throughout an economy (McEachern, 2019).
However, the functions of money narrow this definition. This definition is focused on the first function of
money, which is as a medium of exchange. When you watch the videos for this section, notice how my dog
accepted a treat in exchange for her ball. In this instance, the treat is the medium of exchange for my dog.
Because the treat is a commodity and also money, McEachern (2019) explains that it is called commodity
money.
The second function of money is as a unit of account. When an economy begins to accept commodity money,
it becomes a unit of account. If there ever comes a time when all the dogs in the world rule, and all dogs are
willing to accept one treat for one ball, prices will have been established. As McEachern (2019) suggests, the
treats will be the yardstick for measuring the value of things that will be purchased. In other words, every item
that is purchased in this dog world will be paid for in dog treats. Maybe it will take 1,000 dog treats to
purchase a dog house, 100 dog treats to purchase a new collar, and 25 dog treats to go for a ride. The point
is that dog treats will be the common denominator for pricing goods and services.
The final function of money is to store value (McEachern, 2019). This means that money holds its value over
time. The longer money can hold its value, the more acceptable it is to serve as, well, money. For example,
my dog will eat a dog treat as soon as it is put in front of her. In this instance, dog treats do not serve the
function of storing value as they are consumed right away. However, coins that were minted 50 years ago can
still be found today and can still be used as money.
The money in our pockets has properties that we inherently know. The first two properties of money relate to
the physical attributes of the coin or bill itself. First, money that we carry around is durable. Going back to dog
treats, they have to be stored properly. If not, these dog treats will become stale and spoil. This is not the
case with money in your pocket. Coins and even paper money have a long useful life. Thus, one
characteristic of ideal money is that it is durable (McEachern, 2019).
Next, money should be easy to carry around. Coins and bills can easily fit into your pocket. That means they
serve the function of being portable.
The third property of ideal money is that it is easily divisible (McEachern, 2019). We see this property every
time we pay cash for a good or service. If the cost of a good is $19.99, and you pay with a $20 bill, you will
receive one penny in change.
Also, money should be of uniform quality. For instance, dog treats used as money would become stale over
time. Newer, fresh dog treats would have the same value as older, stale dog treats, but my dog would begin
to question whether the older, stale dog treats had the same value as newer ones. That suggests dog treats
are not of uniform quality. However, take any two $1 bills out of your wallet—both of them are of uniform
quality.
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For the fifth property of ideal money, we will revisit opportunity costs. In our hypothetical dog world where dog
treats are used as money, my dog could eat the treat or use it to pay for other goods. If my dog chose to
purchase goods with the treat, she would give up eating it. The value of what she gave up is the opportunity
cost of making the purchase (which would be very high for my dog). On the other hand, there are not many
uses other than money for the paper bills or coins in your pocket. That means there is a low opportunity cost
associated with the money you have (McEachern, 2019).
Finally, the value of money should be relatively stable. Unpredictable fluctuations in the supply and demand of
money would result in unpredictable changes in prices. Again, going back to my dog’s treats, a sudden
increase in the demand to eat her dog treats would affect the exchange rate of the treats and reduce the
usefulness of these treats as money. That is why the money we carry around in our pockets is supplied by the
federal government. The federal government’s supply of money is not dependent on uncontrollable forces of
nature, making the value of the bills and coins you carry more stable (McEachern, 2019).
Federal Reserve
Have you ever wondered what happens to a check after your write it to pay for something? The person or
business that receives that check will deposit it in their own bank. That bank will send the check to the
Federal Reserve, which will clear the check and then send it to your bank. The Federal Reserve has far more
duties than to just clear checks, though. The Federal Reserve has the responsibilities of issuing bank notes,
buying and selling government securities, loaning money to member banks, clearing checks in the banking
system, and requiring member banks to hold a specified fraction of their deposits in reserve (McEachern,
2019). This suggests that the Federal Reserve is involved with much of the daily transactions in the economy.
However, the Federal Reserve is involved with much more than the daily transactions of consumers.
The Federal Reserve is in charge of manipulating the money supply in the United States in an attempt to
create price stability and maximum employment. Beyond that, McEachern (2019) explains that the goals of
the Federal Reserve have been expanded to include economic growth, stable interest rates, stable financial
markets, and stable exchange rates. To effectively achieve all these goals, the Federal Reserve attempts to
control inflation and promote economic growth. Below is a discussion of the tools the Federal Reserve has at
its disposal to achieve these goals. To learn more about Federal Reserve responsibilities and activities, view
the video Monetary Policy and the Federal Reserve. A transcript and closed captioning are available once you
access the video.
One tool the Federal Reserve can use to help fight inflation or stimulate economic growth is open market
operations. Open market operations consist of the Federal Reserve either buying or selling government
securities (McEachern, 2019). It is important to understand that buying government securities has a much
different impact than selling government securities. Buying government securities is considered to be
expansionary monetary policy, while selling government securities is considered to be contractionary
monetary policy (Federal Reserve Bank of St. Louis, n.d.-a). As we learned with fiscal policy, expansionary
monetary policy attempts to expand economic growth. On the other hand, contractionary monetary policy
slows down (or contracts) the economy.
When the economy is facing rising inflation, the Federal Reserve may choose to sell government securities in
an attempt to slow the economy down and lower inflation. The reason selling government securities contracts
the economy is that money in the economy is given to the Federal Reserve for these securities. Since this
money is no longer in the economy, the reserve funds that banks have available to lend is decreased (Federal
Reserve Bank of St. Louis, n.d.-a). With less money flowing through the economy, less spending will result.
Lower spending results in a decrease in prices, and inflation is lowered.
https://c24.page/c4e2s23p6b8rfp6rwdb39rgdbx
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Just the opposite occurs when the Federal Reserve choses to purchase government securities. When the
Federal Reserve purchases government securities, the Federal Reserve is giving the economy money for the
securities. More money in the economy means banks have more money to lend (Federal Reserve Bank of St.
Louis, n.d.-a). More loans mean more spending in the economy. More spending in the economy suggests the
economy will be stimulated to grow.
While news reports often do not focus on this tool of the Federal Reserve, open market operations are the
easiest tool the Federal Reserve has in its toolbox. Because of this simplicity and ease of use, open market
operations are the preferred tool of the Federal Reserve (McEachern 2019).
The Federal Reserve does not deal directly with the public. The Federal Reserve can be thought of as the
banks’ bank. The Federal Reserve regulates banks and can even make loans to member banks (McEachern,
2019). Money borrowed from the Federal Reserve by member banks is then loaned to consumers for
purchases. As with any loan, member banks borrowing money from the Federal Reserve will be charged
interest. The interest that is charged to the member bank is called the discount rate (Board of Governors of
the Federal Reserve System, 2020). When these member banks turn the money around and loan it to
consumers and firms, the member banks base the interest rate they will charge borrowers on the discount
rate that is charged by the Federal Reserve. This suggests that the discount rate directly influences the
interest rate for loans, savings accounts, certificates of deposits, and other types of similar accounts.
The Federal Reserve can make changes to the discount rate in an attempt to either stimulate economic
growth or fight inflation. A lower discount rate means that it is cheaper for banks to borrow money from the
Federal Reserve. With member banks having to pay a lower rate for money borrowed from the Federal
Reserve, these member banks can lower the interest rate that borrowers in the economy will pay for loans. At
the same time, these member banks will lower the interest rates paid for savings, certificates of deposit, and
other types of similar accounts. When consumers see these lower interest rates, they are encouraged to
borrow more money and save less. More borrowing and less saving means more money is being used for
purchases in the economy. More purchases in the economy results in a stimulated economy where economic
growth occurs. Again, stimulating economic growth is called expansionary monetary policy.
On the other hand, the Federal Reserve may choose to increase the discount rate. The Federal Reserve may
choose to increase the discount rate because inflation is on the rise and becoming a problem in the economy,
or the Federal Reserve may fear that inflation could rise in the near future. Increasing the discount rate results
in an increased interest rate that is charged by member banks for loans as well as an increased rate paid to
consumers for savings accounts, certificates of deposits, and other types of similar accounts. Increasing
interest rates encourages consumers to save money rather than spend it. Further, increasing interest rates
discourages consumers and firms from borrowing money. Both result in a decrease in money in the economy.
A decreased supply of money in the economy slows down economic growth, which lowers inflation.
Basic logic would suggest that a lower interest rate would result in more spending in the economy. If you were
given $10,000 and told you could put that money in a savings account and earn 0.5% interest over the next
year, you could quickly determine that you could earn $50 over the next year from investing that $10,000.
That kind of return on your savings is not a big incentive to save. However, if interest rates were 10% for
saving that money, you would earn $1,000 in interest from saving that $10,000 over the next year. In this
instance, you would be more inclined to save that money than spend it.
The same logical reasoning is found with interest rates on loans. Let’s say you want a $10,000 loan for your
business so you can invest in improvements like purchasing more machinery or remodeling the store. You
could open a credit card account for the loan or get a small business loan. Let’s take a closer look at each
option.
• Credit Card: The credit card has a 21% interest rate. Opening the credit card account and charging
$10,000 in purchases on that card would result in your having to pay $2,100 in interest back at the
end of the year plus the loan of $10,000. That would mean you have to pay a total of $12,100 at the
end of the year.
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• Small Business Loan: The small business loan, on the other hand, has an associated 2.5% interest
rate. If you accept the small business loan, you will accrue $250 in interest over the entire year. This
would mean you would have to pay back $10,250 in principal plus interest at the end of the year.
If the only option to get the loan were to take the credit card loan, you might decide not to even borrow the
money, as the interest rate is just too high. Not taking this loan would mean you do not purchase that new
machinery or remodel the store. Because you are not going to spend that money, the economy will not see as
much activity. However, the small business loan is a much more attractive offer since the interest rate on the
loan is lower. If you decided to take the small business loan, you would spend money on the new machinery
or on the remodeling of the store. The rest of the economy sees an increase in activity, and the economy
grows. While simplified, the example above shows you how the Federal Reserve can influence the money
supply and economic activity through making changes to the discount rate.
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The final tool the Federal Reserve has at its disposal to manipulate the money supply of the United States is
making changes to reserve requirements of member banks. Reserve requirements represent the minimum
amount of money member banks must hold in reserve to back up deposits (McEachern, 2019). When the
Federal Reserve decreases reserve requirements, they are engaging in expansionary monetary policy
(Federal Reserve Bank of St. Louis, n.d.-b). For example, banks lend money for investment. The money that
is required to be held in reserve cannot be used for these loans. When the reserve requirements are
decreased, member banks have more money available that they are allowed to lend for investment. More
loans being made means more money is flowing through the economy when the investment is made. As more
money flows through the economy, the economy grows.
Reserve requirements can also be used to contract an economy that is growing too fast (i.e., the economy is
facing inflation). If the Federal Reserve wants to fight inflation, the economy will need to contract. Increasing
the reserve requirements of banks can help contract the economy. With increased reserve requirements,
member banks must hold a larger portion of money in reserve. Larger reserves mean that less money is
available to make loans for investment. With less money available for loans, less money is flowing through the
economy. The economy will contract, and inflation will decrease. Learn more about this by viewing the video
Contractionary and Expansionary Monetary Policy. A transcript and closed captioning are available once you
access the video.
https://c24.page/3q7b6hp5p6wywww4s4zjn9v7
https://c24.page/3q7b6hp5p6wywww4s4zjn9v7
ECO 2302, Principles of Macroeconomics 7
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Consumers have a choice when it comes to money—they can save it, or they can spend it. When making this
decision, consumers will weigh the opportunity cost of spending money. The opportunity cost associated with
spending money is the interest that could have been earned if the money were saved.
As mentioned above, a higher interest rate encourages consumers to save money because the opportunity
cost of spending money is higher. A lower interest rate discourages saving because the opportunity cost of
saving money is lower. In other words, consumers will want to hold on to their money (they will demand more
money) for other purposes when interest rates are low. Effectively, consumers are weighing the opportunity
cost of holding money when making decisions of whether to save or spend. As the interest rate increases, the
opportunity cost of holding money increases as well.
Similar to all other demand curves, the demand for money slopes downward and to the right. The quantity of
money is on the horizontal axis, and the interest rate is on the vertical axis. The downward slope of the
demand curve suggests at higher interest rates, less money is demanded. At lower interest rates, more
money is demanded.
The Federal Reserve, through monetary policy, is primarily responsible for determining the supply of money.
At a given point in time, the supply of money is fixed at the level determined by the Federal Reserve.
Regardless of the level of interest rates, the supply of money is fixed, meaning that it is a vertical line
(McEachern, 2019).
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The intersection of the supply of money (Sm) and demand for money (Dm) represents the equilibrium interest
rate (i).
As mentioned above, the Federal Reserve can engage in expansionary or contractionary monetary policy. If
the Federal Reserve engaged in contractionary monetary policy through buying government securities, the
money supply would shift to the left (Sm
’). Equilibrium between the demand for money and the supply of
money would move from Point A to Point B. The result of this movement from Point A to Point B would be a
higher interest rate (i’). The opposite result would occur if the Federal Reserve engaged in expansionary
monetary policy.
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From above, we know that interest rates affect investment. As interest rates decrease, firms are encouraged
to borrow money to make investments in the operations of their business. The investment shifts the aggregate
demand curve to the right (AD1 to AD2). An increase in aggregate demand was the goal of the Federal
Reserve as decreasing interest rates is considered to be expansionary monetary policy. As aggregate
demand shifts to the right, the equilibrium price level in the economy increases from P1 to P2. That means
that expansionary monetary policy will result in some inflation (the increase in aggregate prices).
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If the Federal Reserve wanted to fight inflation, a contractionary monetary policy would be enacted.
Contractionary monetary policy would result in an increase in interest rates. The increase in interest rates
would cause the aggregate demand curve to shift to the left (AD2 to AD1). The shift of the aggregate demand
curve would cause equilibrium to move from Point B to Point A. The end result would be a decrease in
aggregate prices from P2 to P1.
References
Board of Governors of the Federal Reserve System. (2020, February 25). Policy tools: The discount rate.
https://www.federalreserve.gov/monetarypolicy/discountrate.htm
Federal Reserve Bank of St. Louis. (n.d.-a). A closer look at open market operations.
https://www.stlouisfed.org/in-plain-english/a-closer-look-at-open-market-operations
Federal Reserve Bank of St. Louis. (n.d.-b). How monetary policy works. https://www.stlouisfed.org/in-plain-
english/how-monetary-policy-works
McEachern, W. A. (2019). Macro ECON6: Principles of macroeconomics (6th ed.). 4LTR Press.
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Course Learning Outcomes for Unit VII
Required Unit Resources
Unit Lesson
Money
Barter
Functions of Money
Properties of Ideal Money
Federal Reserve
Regulating the Money Supply
Open Market Operations
Discount Rate
Reserve Requirements
Interest Rates and the Demand for Money
Interest Rates, Aggregate Demand, and Aggregate Supply
ECO 2302, Principles of Macroeconomics 1
Upon completion of this unit, students should be able to:
1. Recognize the sources of the central economic problem.
2. Define the role of supply and demand in determining prices and quantities of goods and services.
4. Discuss the effects of unemployment and inflation on the economy.
6. Discuss the interaction of the federal government and the Federal Reserve Bank in controlling the
U.S. economy.
7. Illustrate monetary theory using the supply and demand model.
8. Interpret the international economy through trade interdependences and financial interactions.
8.1 Explain international trade gains and why nations still trade when no country has a comparative
advantage.
8.2 Describe how trade restrictions can harm an economy and why they are still used.
8.3 Discuss how the foreign exchange rate is determined using demand and supply curves.
Course/Unit
Learning Outcomes
Learning Activity
1 Unit VIII Final Project
2 Unit VIII Final Project
4 Unit VIII Final Project
6 Unit VIII Final Project
7 Unit VIII Final Project
8.1
Chapter 17
Article: “The Effects of
and Trade Barriers in CBO’s Projections”
Unit VIII Final Project
8.2
Unit Lesson
Chapter 17
Article: “The Effects of Tariffs and Trade Barriers in CBO’s Projections”
Unit VIII Final Project
8.3
Unit Lesson
Chapter 18
Unit VIII Final Project
Chapter 17: International Trade
Chapter 18:
UNIT VIII STUDY GUIDE
International Trade and Finance
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In order to access the following resource, click the link below.
Fried, D. (2019, August 22). The effects of tariffs and trade barriers in CBO’s projections. Congressional
Budget Office. https://www.cbo.gov/publication/55576
Unit Lesson
As you begin Unit VIII, look at the goods around you. You might be reading this lesson on a computer made
in China. The carpet beneath your feet may have been made in the United States. The shoes on your feet
may have been made in Vietnam. The ability for consumers to purchase such a wide variety of goods at
cheaper prices is due to international trade.
In Chapter 2, you learned about the gains that can be made when nations are allowed to specialize. The
economies of individual nations gain when allowed to specialize, as do all other economies. Recall that the
law of comparative advantage suggests that the individual, firm, or nation with the lowest opportunity cost of
producing a good should specialize in that production (McEachern, 2019). This means that comparative
advantage—one nation, region, or firm producing a good at a lower opportunity cost—is the basis for
international trade.
To explain why comparative advantage is the basis for international trade as well as how nations benefit from
specialization, an examination of the production possibilities frontier is required. Let’s assume that two nations
(the Republic of Ruby and the Kingdom of Luna) can produce two goods, food and automobiles. The Republic
to Ruby has 100,000 workers. The Kingdom of Luna has 500,000 workers. The production possibilities
frontier for both nations are provided below. The information presented is per year, with no trade occurring
between the two countries.
The production possibilities frontier for the Republic of Ruby suggests that workers can produce 7,000 units of
food per year or 3,500 automobiles per year. If the Republic of Ruby wanted to produce both food and
automobiles, workers could be allocated between the two goods to produce at any level on the production
possibilities frontier.
For the Kingdom of Luna, workers can produce 3,000 units of food each year if all the workers are allocated
to food production. Alternatively, the Kingdom of Luna could produce 6,000 automobiles each year if all the
workers were allocated to producing automobiles. Finally, a combination of food and automobiles could be
produced if workers were allocated between the two, but the maximum amount of production would be the
level represented by the production possibilities frontier for the Kingdom of Luna.
https://www.cbo.gov/publication/55576
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The slope of the respective production possibilities frontier represents the opportunity cost of production. The
opportunity cost of producing one more automobile in the Republic of Ruby is 2 units of food (7,000 / 3,500 =
2). The opportunity cost of producing one more automobile in the Kingdom of Luna is 0.5 (3,000 / 6,000 =
0.5). Because the opportunity cost of producing one more automobile in the Kingdom of Luna is lower, the
Kingdom of Luna should specialize in producing automobiles.
Calculating the opportunity cost of producing one more unit of food is done in the same way. For the Republic
of Ruby, the opportunity cost of producing one more unit of food is 0.5 automobiles (3,500 / 7,000 = 0.5). The
opportunity cost of producing one more unit of food for the Kingdom of Luna is 2 automobiles (6,000 / 3,000 =
2). Comparing the two opportunity costs suggests that the Republic of Ruby should specialize in producing
food.
When determining how much each nation should trade, we again have to refer to the opportunity costs. As
long as the Republic of Ruby can get 0.5 automobiles for one unit of food, and the Kingdom of Luna can get
0.5 units of food for each automobile, the two nations would be better off specializing.
International trade occurs because each nation believes it will be better off by trading with other nations. The
information above shows how to calculate the opportunity cost of producing one good over another and how
to determine if specialization should occur. The reasons why the opportunity cost is different for different
goods within a nation or between nations are many and are addressed below.
The availability of resources causes differences in the opportunity cost of production (McEachern, 2019). For
instance, the United States has an abundance of fertile land for growing wheat; whereas, Saudi Arabia has
significantly less such land. Specifically, total acreage planted in wheat in the United States in 2019 was 45.2
million acres (National Agricultural Statistics Service, 2019). Saudi Arabia planted only 215,673.6 acres of
wheat in 2019 (Mousa, 2019).
On the other hand, proven oil reserves in the United States as of the end of 2018 was 43.8 billion barrels
(U.S. Energy Information Administration, 2019). Saudi Arabia had significantly more oil in reserve with 267.03
billion barrels at the end of 2018 (Organization of the Petroleum Exporting Countries, 2019).
The abundance of land suitable for wheat production in the United States suggests that specialization in
wheat production is occurring in the United States. The abundance of oil reserves in Saudi Arabia suggests
specialization in oil production is occurring in Saudi Arabia. Much of this specialization is due to climate and
the concentration of oil resources. If the United States produces more wheat than it consumes, the remainder
can be made available for export. If Saudi Arabia produces more oil than can be consumed domestically, it
can export the remainder of the oil.
When the long-run average cost of production falls as a firm expands operations, economies of scale are
achieved (McEachern, 2019). When nations can achieve economies of scale, specialization occurs within that
nation. When specialization occurs, nations will begin to produce more of a particular good. When countries
are producing at the lowest opportunity cost, they are the most competitive in world markets and can engage
more successfully in international trade.
Consumer tastes differ from one region to the next within a country. For example, in Texas, bar-b-que usually
refers to beef. In Tennessee, bar-b-que refers to pork.
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Once you begin looking at consumer tastes between nations, we find even more differences. Bird’s nest soup
is considered to be a delicacy in China, and fried tarantulas are consumed in Cambodia. Different tastes of
consumers lead to specialization of production. As the world’s population continues to migrate from one
nation to another, the tastes of consumers travel with them. The demand for goods that are customary in a
person’s native country can lead to international trade for those goods.
Think about an economy where you were limited to only the goods that were produced locally. If this were the
case, you would not be able to purchase coffee in Maine, lobster in New Mexico, or rice in North Dakota.
Today, we have a wide variety of goods to purchase in every area because of trade. Domestic trade
increases the selection of goods that are available to purchase by a great amount. Once a nation begins
engaging in international trade, the selection grows even more.
When trade restrictions are discussed, the impacts of these policies are evaluated in terms of consumer and
producer surplus. Producer surplus is defined as the area above the supply curve but below equilibrium price.
Consumer surplus is defined as the area below the demand curve but above equilibrium price (McEachern,
2019).
Tariffs
A tariff is a tax on goods being sold. While tariffs can be applied to exports, the discussion of tariffs in this
section is focused on imports. McEachern (2019) explains that tariffs can be specific, such as a dollar amount
per unit or ad valorem (a percentage of the import price). When a specific tariff is used, the price of the good
is increased by the amount of the tariff. For example, let’s assume the supply and demand for wheat in the
United States is as shown below.
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The world price for wheat is $5 per bushel. This world price is below the equilibrium price for wheat in the
United States. If wheat producers in the United States attempted to sell wheat above the world price,
consumers in the United States would just purchase wheat from another nation that was willing to accept $5
per bushel. Where the world price for wheat intersects the supply curve for wheat on a graph indicates the
amount of wheat that producers in the United States are willing and able to produce. The amount of wheat
produced in the United States is represented by Point a. Where the world price intersects the demand curve
indicates the quantity of wheat demanded in the United States (represented by Point b).
A specific tariff of $0.10 per bushel being imposed on wheat imports into the United States would raise the
world price by $0.10 per bushel in the United States.
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The intersection of the price of wheat in the United States ($5.10) represents the world price of $5 per bushel
plus the specific tariff of $0.10 per bushel. Wheat producers in the United States now see a higher price and
increase their level of production to Point c. Consumers in the United States now see a higher price and
reduce their demand to Point d. Consumer surplus has now decreased as a result of the tariff being imposed,
and producer surplus has increased.
To evaluate the changes to consumer and producer surplus, we need only focus on the area between the
world price and the price in the United States after the tariff was imposed.
Consumer surplus was decreased by the sum of Areas 1, 2, 4, and 5. Revenues of wheat producers in the
United States are increased by the Areas 1, 2, and 3. However, producer surplus is only increased by Area 1.
Revenues associated with Areas 2 and 3 only go to offset higher marginal costs faced by wheat producers in
the United States. Area 2 is a net welfare (producer surplus plus consumer surplus) loss to the economy of
the United States because the additional bushels of wheat could have been imported at $5 per bushel instead
of $5.10 per bushel. The federal government gets to keep revenues equal to Area 4. This is a loss to
consumers. However, McEachern (2019) points out that the government can fund additional services for
consumers or lower taxes. Finally, Area 5 is a loss of consumer surplus and does not get redistributed to any
sector. That means Area 5 is a loss to the economy due to the tariff being imposed on wheat.
Import quotas limit the total amount of a good that can be imported into a nation and are usually focused on
imports from one particular country (McEachern, 2019). While an import quota targets the quantity of a good,
ultimately, consumers end up paying a higher price. Tracing the effects of an import quota requires that we
start with the supply and demand for a good in a nation. Assume that we are examining the effects of a quota
on the wheat industry in the United States. As with the example for tariffs, the hypothetical supply and
demand for wheat is as follows.
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The world price for wheat is $5 per bushel. This world price is below the equilibrium price for wheat in the
United States. If wheat producers in the United States attempted to sell wheat above the world price,
consumers in the United States would just purchase wheat from another nation that was willing to accept $5
per bushel. Where the world price for wheat intersects the supply curve for wheat indicates the amount of
wheat producers in the United States are willing and able to produce. The amount of wheat produced in the
United States is equal to Point a. Where the world price intersects the demand curve indicates the quantity of
wheat demanded in the United States (represented by Point b).
Remember, Point a represents quantity of wheat supplied in the United States by domestic wheat producers.
Point a represents the intersection of the world price of wheat and the domestic supply curve for wheat. The
quota puts a limit on the total amount of wheat that can be imported into the United States. This quota is
represented by the distance between Points a and c. That means Point c is the total amount of wheat that will
be supplied to the United States at the world price.
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Also notice that the supply curve for wheat has shifted with the quota to Supply 2. Supply 2 is horizontal at the
world price until it reaches Point c. From Point c and beyond, the new supply curve with the quota is
horizontal to the original domestic supply curve for wheat (Supply). The point where Supply 2 intersects the
domestic demand curve for wheat (Demand) tells us what the new price for wheat is in the United States with
the quota in place: $5.05 per bushel.
Consumer and producer surplus obviously changed when the quota was enacted. As with a tariff, producer
surplus increases and consumer surplus decreases when a quota is applied.
Focusing on the area between the world price for wheat and the domestic price of wheat tells us what specific
changes have been made to consumer and producer surplus.
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With the quota, producer surplus is increased by Areas 1 plus 2. Consumer surplus is decreased by Areas 1
plus 2 plus 3. The loss of welfare is represented by Area 3 as consumer surplus is lost here but is not gained
by an increase in producer surplus.
Analysis of the use of a quota suggests that consumers end up paying a higher price for goods affected by
the quota. Further, consumer surplus is decreased when a quota is used, and producer surplus is increased.
However, the increase in producer surplus does not offset the decrease in consumer surplus.
When either tariffs or quotas are used, domestic consumers pay a higher price. Also, both tariffs and quotas
reduce consumer surplus, increase producer surplus, and create a net loss to welfare because the increase in
producer surplus does not offset the decrease in consumer surplus. However, there are differences in the
outcomes when tariffs are used versus quotas. A tariff can be viewed as a tax on imports. The revenue
generated from tariffs is paid to the domestic government, while the revenue generated from the use of a
quota goes to the nation that secures the right to sell goods in the domestic market (McEachern, 2019).
Because revenues generated from a tariff can be used by the domestic government to fund other services in
the domestic market, the domestic economy is worse off with a quota.
An example of revenue being redistributed to the economy from the use of tariffs can be found in revenues
generated by the trade war between the United States and China. As of February 21, 2019, $12.194 billion
had been collected in tariff revenue by the United States (Williams et al., 2019). Because farmers in the
United States were economically harmed by this trade war, the federal government of the United States
authorized the Market Facilitation Program, Food Purchase and Distribution Program, and the Agricultural
Trade Promotion Program to assist these producers (U.S. Department of Agriculture, 2019).
Trade restrictions are proven to negatively impact total welfare of an economy. McEachern (2019) suggests
that it would be more efficient to transfer money from domestic consumers to domestic producers. However,
politically, making such a transfer of money would be unpopular.
As such, other arguments are made for supporting trade restrictions. These arguments include imposing trade
restrictions in the name of things such as national defense, protecting infant industries, antidumping, jobs and
income, and protecting declining industries. These arguments are discussed in more detail below.
Some goods are vital to national defense. Relying on other nations to produce these goods could result in a
national security threat. This argument relies on a belief that national defense is more important than
efficiency and equity in a market (McEachern, 2019).
While the national defense argument can be used, McEachern (2019) points out that the federal government
could use trade restrictions and just stockpile the resources. Government subsidies could also be used to
promote the production of these goods by domestic firms. Finally, McEachern (2019) acknowledges that
technology is ever-changing, and some weapons may quickly become obsolete. Protecting domestic
industries that provide resources used in the production of these weapons may be vitally important today but
might not be needed in the very near future.
New (infant) industries usually face an average cost curve that falls as output increases (McEachern, 2019).
For these industries to expand and become competitive, they may need protection. Trade restrictions can
give these new industries time to grow and become efficient enough to compete with international firms.
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The theoretical basis for this argument sounds justified. However, McEachern (2019) asks several thought-
provoking questions that can make it difficult to apply that theory. For instance, the question of which new
industries are important enough to protect may be difficult to answer. In the not-so-distant past, consumers
could drive to a store and rent a movie that was saved on a VCR tape. These consumers would then drive
home with the rented movie and watch it on a VCR player that was connected to their television. If the federal
government believed that the infant VCR taped movies industry was vital to economic growth in the nation,
trade restrictions might have been used to protect this industry. Today, many consumers do not even know
what a VCR tape is, much less understand the wide range of emotions experienced when entering a store
excited to rent the newest movie on the market only to find that all the copies were already rented. Using
trade restrictions would have proven to not be effective as the industry was replaced by streaming videos.
The question also is raised regarding when firms are “old enough” to remove the protection (McEachern,
2019). Further, trade restrictions create inefficiencies in a market. After all, the industry is being protected
from outside competition, thereby creating less of an incentive to become efficient. As a comparison, with this
protection of the infant, the federal government is acting as a helicopter parent to the infant industry. The
lower incentive to become efficient could create a situation where the infant industry cannot survive without
trade restrictions as a protection.
When nations sell a good at a lower price in another country than the price that is charged in the domestic
market, the nation is engaging in dumping (McEachern, 2019). Consumers may greatly enjoy the much lower
prices they will pay when another nation dumps goods on their nation. However, persistent dumping results in
decreases in producer surplus that outweigh the increases in consumer surplus.
To ensure that dumping does not occur, the United States enacted the Trade Agreement Act of 1979
(McEachern, 2018). This agreement allows for tariffs to be applied when goods are sold in the United States
for less than in the home market or for less than the cost of production. More recently, the World Trade
Organization (WTO) allows for tariffs to be used when products are sold for less than their fair market value
and when there is material injury to domestic producers (McEachern, 2019).
Protecting domestic industries in the name of wages and jobs is common. Developed nations can have a
much higher wage rate than developing nations. Availability of technology, education levels, laws, and
differences in standards of living can account for variations in wage rates. One of the biggest problems with
using trade restrictions in the name of protecting jobs and income is that other nations tend to retaliate by
restricting imports into their country to save their jobs (McEachern, 2019). As a result, jobs are lost in the
export markets and gains from trade do not materialize.
As an established domestic industry ages, it faces the potential of becoming obsolete. Lower-priced imports
could hasten the industry being forced out of existence. However, resources in this industry take time to
transition to other industries (e.g., workers may need new training or machinery may not be able to be used in
another industry). When this occurs, McEachern (2019) suggests that trade restrictions could help the
industry stay in operation long enough to transition these resources to other industries. Using trade
restrictions can lessen the shock experienced by the economy from losing the industry.
International Finance
An important part of a nation’s gross domestic product (GDP) is the value of exports and imports. Due to the
importance of this part of the calculation of GDP, accounting for these transactions is crucial. The balance of
payments is how economic transactions between a domestic economy and international economies are
recorded. These transactions could be for goods and services, real and financial assets, or transfer payments
(McEachern, 2019).
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The merchandise trade balance involves subtracting the value of merchandise imports from the value of
merchandise exports (McEachern, 2019). Merchandise trade involves only tangible products such as cheese
made in France, toys made in China, and wheat grown in the United States. When the United States sells and
delivers wheat to another nation, it is considered to be an export. This adds to the merchandise trade
balance. The United States importing cheese from France or toys from China would negatively impact the
merchandise trade balance. When the value of merchandise that is imported is greater than the value of
exported merchandise, the trade balance is in deficit (McEachern, 2019).
As of November, 2019, the merchandise trade balance for the United States for 2019 was -$779.251 billion
with $1.514 trillion in exports being more than offset by $2.293 trillion in imports (United States Census
Bureau, n.d.). Interestingly enough, not all individual industries in the United States show a deficit in the trade
balance. For instance, the agricultural sector of the United States economy showed a trade surplus of $4.542
billion for 2019 through the month of November, with $124.835 billion in exports and $120.293 billion in
imports (Economic Research Service, 2019).
Merchandise trade is only one component of net exports. Remember, net exports are used in the calculation
of GDP. Services are also imported and exported. These services could include “transportation, insurance,
banking, education, consulting, and tourism” (McEachern, 2019, p. 325). The balance on goods and services
is calculated by subtracting the value of goods and services imported from the value of goods and services
exported.
Investment income can occur for residents of the United States who own foreign assets as well as for
residents of foreign nations who own assets in the United States. Income earned from United States residents
owning assets in foreign nations adds to the balance of payments. Income earned from foreign residents
owning assets in the United States deducts from the balance of payments.
The federal government records money that is sent to other nations’ governments or citizens that does not
result in the purchase of a good or service as a unilateral transfer. Examples of these unilateral transfers
would be the United States federal government providing foreign aid, money sent from workers in the United
States to family members in other nations, or donations to foreign charities, just to name a few. Monies sent
from the United States to foreign nations are subtracted from the monies received from foreign nations to
arrive at the net unilateral transfers (McEachern, 2019).
If you have ever traveled outside your home nation, you have experienced foreign exchange rates. For
instance, the average currency exchange rate between the United States dollar (USD) and Mexican peso
(MXN) was 1 USD to 19.246 MXN in 2019 (Internal Revenue Service, 2020). This means that, on average,
when your plane landed in Mexico and you disembarked to begin your vacation, you could exchange 100
USD for 1,924.6 MXN. Then you will go to a vendor to purchase a souvenir and be quoted a price in United
States dollars (where you would more than likely take all the Mexican pesos out of your pocket and hold them
out to the vendor as if to say you had no idea what they were asking you to pay).
Your confusion here was based on not being able to quickly convert the exchange rate between United States
dollars and Mexican pesos. Foreign exchange rates show how much a nation’s currency is worth compared to
another nation’s currency (McEachern, 2019). These exchange rates are determined by the market
participants (households, private financial institutions, and governments, for example) that buy and sell
foreign currency based on consideration of the supply and demand for the currencies in question.
Foreign exchange rates are constantly changing because the supply and demand for currencies are
constantly changing. When residents of the United States travel to Mexico, they must purchase Mexican
pesos from the foreign exchange market. The supply of foreign currency is determined by how much foreign
residents desire another currency (McEachern, 2019). Residents of Mexico want United States dollars to
purchase goods and services in the United States. The Mexican residents will pay for these United States
dollars with pesos. Together, the demand and supply for foreign currency determines the exchange rate.
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Currently exchange rates and the fluctuations of those rates can have a large impact on international trade.
For instance, assume that the current exchange rate between our two fictional nations, the Republic of Ruby
and the Kingdom of Luna, is 1 Republic of Ruby dollar equals 20 Kingdom of Luna dollars.
At that exchange rate, the Republic of Ruby purchases 200 rowboats for their limited navy at a total cost of 50
Republic of Ruby dollars (which is equal to 1,000 Kingdom of Luna dollars). Now, assume that next year, the
economy of the Kingdom of Luna has grown, and the value of the Kingdom of Luna dollars has increased
relative to The Republic of Ruby dollars. In the second year, 1 Republic of Ruby dollar is worth 10 Kingdom of
Luna dollars. The Republic of Ruby would now have to pay 100 Republic of Ruby Dollars to purchase the
same 200 rowboats worth 1,000 Kingdom of Luna dollars. The reason for the increased cost is because the
exchange rate between the Republic of Ruby and the Kingdom of Luna changed.
Fluctuations in currency exchange rates can occur for many reasons. Exchange rates may change because
of financial reasons. However, political events can also affect exchange rates (Mahapatra & Bhaduri, 2019).
These political events could be war, elections that could alter the policy stance of a nation, changes to current
monetary or fiscal policies, and so on. When it comes to firms engaging in international business, Reynolds
(2017) suggests that fluctuations in exchange rates is one of the most challenging aspects.
References
Economic Research Service. (2019). U.S. agricultural trade data update: Total value of U.S. agricultural trade
and trade balance, monthly. United States Department of Agriculture. Retrieved December, 2019,
from https://www.ers.usda.gov/data-products/foreign-agricultural-trade-of-the-united-states-fatus/us-
agricultural-trade-data-update
Internal Revenue Service. (2020, January 10). Yearly average currency exchange rates. Retrieved on
January 30, 2020, from https://www.irs.gov/individuals/international-taxpayers/yearly-average-
currency-exchange-rates
Mahapatra, S., & Bhaduri, S. N. (2019, March). Dynamics of the impact of currency fluctuations on stock
markets in India: Assessing the pricing of exchange rate risks. Borsa Istanbul Review, 19(1), 15–23.
https://doi.org/10.1016/j.bir.2018.04.004
McEachern, W. A. (2019). Macro ECON6: Principles of macroeconomics (6th ed.). 4LTR Press.
Mousa, H. (2019, April 4). Saudi Arabia: Grain and feed annual 2019 (GAIN Report No. SA1902). United
States Department of Agriculture Foreign Agricultural Service.
https://apps.fas.usda.gov/newgainapi/api/report/downloadreportbyfilename?filename=Grain%20and%
20Feed%20Annual_Riyadh_Saudi%20Arabia_4-4-2019
National Agricultural Statistics Service. (2019, September 30). All wheat acres, United States [Graphic].
United States Department of Agriculture.
https://www.nass.usda.gov/Charts_and_Maps/graphics/awac
Organization of the Petroleum Exporting Countries. (2019). OPEC share of world crude oil reserves, 2018.
https://www.opec.org/opec_web/en/data_graphs/330.htm
Reynolds, K. (2017, January 6). 11 biggest challenges of international business in 2017. Hult International
Business School. https://www.hult.edu/blog/international-business-challenges/#currency-rates
United States Census Bureau. (n.d.). Trade in goods with world, seasonally adjusted. Retrieved January 30,
2020, from https://www.census.gov/foreign-trade/balance/c0004.html
U.S. Department of Agriculture. (2019, May 23). USDA announces support for farmers impacted by unjustified
retaliation and trade disruption [Press release]. https://www.usda.gov/media/press-
releases/2019/05/23/usda-announces-support-farmers-impacted-unjustified-retaliation-and
ECO 2302, Principles of Macroeconomics 13
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U.S. Energy Information Administration. (2019, December 13). U.S. crude oil and natural gas proved
reserves, year-end 2018. https://www.eia.gov/naturalgas/crudeoilreserves/
Williams, B. R., Cimino-Isaacs, C. D., Fefer, R. F., Hammond, K. E., Jones, V. C., Morrison, W. M., &
Schwarzenberg, A. B. (2019, February 22). Trump administration tariff actions (Sections 201, 232,
and 301): Frequently asked questions (CRS Report No. R45529). Congressional Research Service.
https://crsreports.congress.gov/product/pdf/R/R45529
-
Course Learning Outcomes for Unit VIII
Required Unit Resources
Unit Lesson
Why International Specialization Occurs
Differences in Available Resources
Economies of Scale
Taste Differences
Variety
Trade Restrictions
Tariffs
Import Quotas
Comparing Tariffs and Quotas
Arguments for Trade Restrictions
National Defense
Infant Industries
Antidumping
Jobs and Income
Declining Industries
International Finance
Foreign Exchange Rates
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2020, vol
.
11, no. 2(22), pp. 305–326 DOI: https://doi.org/10.15388/omee.2020.11.35
Macroeconomic Effects of Trade Tariffs:
A Case Study of the U.S.-China Trade War
Effects on the Economy of the United States
Sandra Žemaitytė (corresponding author)
Vilnius University, Lithuania
san.zemaityte96@gmail.com
https://orcid.org/0000-0003-2900-4999
Laimutė Urbšienė
Vilnius University, Lithuania
laima.urbsiene@gmail.com
https://orcid.org/0000-0001-9024-1
323
Abstract. This paper explores the macroeconomic effects of trade tariffs in the context of the recent
trade conflict between the United States and China. The focus is laid on two trade war scenarios, and
one of them takes into account the effects of the COVID-19 pandemic on the global trade flows. After
deploying the partial equilibrium SMART model, the authors conclude that solely due to the trade war
with China, in 2020, the US total trade balance will improve by 41,020 million USD (0.21% of real
GDP), while 43,777 million USD (0.22% of real GDP) of the US imports will have to be sourced
from other countries. The US trade intensity with China and welfare will decline. However, our study
has found that the potential economic consequences of COVID-19 will reduce the relative effects of the
trade war. The study has revealed that the United States economy will benefit from the trade war, which
can be explained by a relatively weak China’s retaliatory response. Nevertheless, the US agriculture and
automotive sectors will suffer most.
Keywords: trade tariffs, US-China trade war, trade balance, welfare.
1. Introduction
Despite generally diverse scholars’ views on global issues, the majority of economists
tend to agree that international trade should be free. This perspective dates back to at
Received: 8/9/2019. Accepted: 11/11/2020
Copyright © 2020 Sandra Žemaitytė, Laimutė Urbšienė. Published by Vilnius University Press. This is an Open Access article
distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and
reproduction in any medium, provided the original author and source are credited.
Contents lists available at Vilnius University Press
http://www.om.evaf.vu.lt/
https://doi.org/10.15388/omee.2020.11.35
mailto:san.zemaityte96@gmail.com
https://orcid.org/0000-0003-2900-4999
mailto:laima.urbsiene@gmail.com
https://orcid.org/0000-0001-9024-1323
https://www.vu.lt/leidyba/
https://creativecommons.org/licenses/by/4.0/
https://www.vu.lt/leidyba/
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least Adam Smith’s “Wealth of Nations” (1776). Today, the idea of an open interna-
tional trading system has also been advocated for by the World Trade Organization
(WTO). Over the period of the first 25 years following the Second World War, the
tariffs on manufactured goods fell to as low as 5% in the industrial countries boosting
the growth of the world economy and trade by 5% and 8%, respectively (WTO, 2005).
The data indicates a link between the free international trade and the economic growth,
backed by a simple theory of a comparative advantage introduced by a classical econo-
mist David Ricardo. In general, a free flow of goods and services increases competition,
innovation, an efficient allocation of resources, and allows maximizing the output by
directing resources to their most productive uses. But what happens when the restric-
tions for free trade such as trade tariffs are put in place?
At first, it would seem that the imposition of a trade tariff should create a stimulus
to the domestic economy by promoting production and employment (Reitz & Slopek,
2005). Apart from that, the governments enforce import tariffs not only to protect the
selected industries but also to raise their revenues or to exert their political and eco-
nomic leverage over another country. For example, in 1980, the US tariffs on Italian
wine and French cheese persuaded the European Union (EU) to start negotiations in
terms of the Common Agricultural Policy (Fetzer & Schwarz, 2019). Unfortunately,
the tariffs can have the unintended side effects. These barriers to free trade trigger the
inefficiency of domestic producers, evasion, and reduction in welfare. Most important-
ly, increased protectionism provokes retaliation resulting in elevated prices, reduced
output, and a substantial shock to supply chains, as trades have to be redirected to avoid
tariff costs (Furceri et al., 2019).
The economic effects of trade tariffs have always been of primary concern to the so-
ciety, therefore the US-China trade war has gained a lot of attention. Numerous studies
have examined this trade war, yet most of them seem to be theoretical and calibrating
different scenarios models or limited to micro-level analysis. The effects of trade war
receive even less scholarly attention in the times of the global COVID-19 pandemic.
The absence of the empirical evidence on the economic effects of the US-China trade
war based on the most recent data was the main stimulus of this paper.
In this study, the authors aim to ascertain whether and how the bilateral tariffs im-
posed between the United States and China in 2018 and 2019 have affected the main
economic indicators of the US, such as trade diversion from China, trade balance, wel-
fare, and trade intensity. The SMART model calibration results have shown that in 2020,
the US total trade balance will improve by 0.21% of real GDP, while the US imports
worth 0.22% of real GDP will have to be sourced from other countries. The US trade
intensity with China will consequently decline. Moreover, the US welfare will decrease
by 0.011% of real GDP. The findings of this research are of particular importance not
only to the policymakers in the United States, but also to other countries in identifying
the potential effects that the initiated trade war can have on the US economy or on any
economy who will opt to impose trade tariffs on its trading partners.
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The paper is structured as follows: literature review, an analysis of the current situa-
tion of the US-China trade war, a description of research methodology, the results ob-
tained after applying the SMART model to two trade war scenarios, the interpretation
of the results and the comparison with other scientific papers, the robustness analysis
based on the sensitivity to changes in elasticity parameters, and concluding remarks.
2. Literature review
Many studies about the effects of trade tariffs are microeconomic in nature and focus on
individual industries. This makes sense as tariffs have gradually decreased since World
War II and later were targeted to specific industries only. However, the US-China trade
war calls for the assessment of the macroeconomic effects.
The import imposition increases the price of the imported goods in the domestic
market and reduces the demand for imports. If the home economy is big, this reduced
demand could even decrease the world prices of such imports (Metzler, 1949). If im-
ports have a perfectly elastic supply, and the supply curve of the foreign country exports
is horizontal, the tariff increase has no effect on foreign prices and is fully observed by
home consumers. Macera and Divino (2015) employed a dynamic stochastic general
equilibrium (DSGE) model to study the case of Brazil, where from 2010 to 2013 high
import tariffs were imposed on more than 100 products, and found that 20% of import
tariff shock increased the domestic price of the imported goods by 15%. This indicates
an incomplete pass-through from import tariffs to domestic prices.
Trade tariffs are also found to reduce real GDP. Kawasaki (2018) used a Computa-
ble General Equilibrium (CGE) model to estimate the economic impact of the US im-
port tariffs on steel and aluminium and concluded that the US real GDP would shrink
by 0.2%. Bollen and Rojas-Romagosa (2018) employed a WorldScan model to esti-
mate the effects of the full-scale US-China trade war scenario. Their results suggested
that China and the US would experience 1.2% and 0.3% real GDP losses, respectively.
When import tariffs are imposed, a loss in deadweight and welfare occurs due to
a shift to less efficient market outcomes as the participants are forced to waste or un-
derutilize resources (Evans, 2019). Theoretically, an optimal tariff could result in an
increased nation’s welfare when the domestic economy is large and has monopsony
power in the market, which forces the exporters to reduce their prices to maintain the
same export size and allows the importing countries to capture the revenue that the ex-
porters previously received (Irwin, 2014). In such a case, the marginal gain in terms of
trade is equal to the marginal loss from the distortion of consumption and production
(Krugman & Obstfeld, 2009). According to Amiti et al. (2019), the domestic econ-
omy benefits from the import tariff imposition when gains in terms of trade exceed
welfare loss. Tu et al. (2020) used a Single Market Partial Equilibrium Simulation Tool
(SMART) model and estimated that the US-China trade war could generate welfare
losses of 1,437 and 2,193 million USD in the US and China, respectively.
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The theory does not deliver any strong conclusions of what would be the effect of
trade tariff imposition on the trade balance. Under the income-expenditure approach
(Ostry & Rose, 1992), the tariff will switch expenditure from foreign to domestic
goods, which in turn will improve the trade balance. However, this approach assumes
that foreigners do not retaliate against the tariff. If they did, the effects on the trade bal-
ance, in general, would be ambiguous. An alternative approach is a monetary approach
to the balance of payments introduced by Mussa (1974). It assumes that the economy’s
long-run equilibrium is given by the Heckscher-Ohlin model. In this model, import tar-
iffs for certain products increase the relative prices of domestic goods that used to com-
pete with them. The acceleration in the prices of domestic goods results in an increased
domestic production and fallen consumption of imported goods. Consequently, under
the monetary approach, tariffs reduce both the volume of imports and exports but the
total trade balance is unaffected (Mussa, 1974). Lastly, the intertemporal approach sug-
gested by Razin and Svensson (1983) assumes that the effects of trade tariffs depend on
whether the tariff is temporary or permanent. Temporary tariffs affect the intertempo-
ral relative prices and make the consumers cut spending in the present for the increased
spending in the future. Permanent tariffs do not lead to an intertemporal consumption
substitution and thus the tariffs have negligible effects on the trade balance.
Kawasaki (2018) concluded that the US import tariffs on steel and aluminium
would improve the US metal trade balance by 59.4 billion USD. However, exports of
automobiles, electronics, and other machines would drop by 3.4%, 5.1%, and 5.8%, re-
spectively and thus, the overall improvement of the trade balance would be relatively
small (1.3 billion USD). Tu et al. (2020) suggested that the US-China trade war should
result in the reduction of the US imports from China and China’s imports from the US
by 91,459 and 36,706 million USD, respectively. Also, they found that the trade tariffs
increased trade diversion. A total of 36,783 million USD US imports and 17,207 mil-
lion USD of Chinese imports were estimated to be diverted from China or the US to
other trading partners, respectively.
Researchers found that the effects of tariffs significantly depended on many eco-
nomic factors. Furceri et al. (2019) demonstrated how large economies that chose to
impose import tariffs faced more extreme decreases in the domestic output and labour
productivity when compared to the emerging markets and the developing economies.
Furthermore, Furceri et al. (2019) found that the rise of tariffs impacted the medi-
um-term losses in output and labour productivity which tended to be more sizeable
when the economy was in an upturn rather than in a recession. The United States is a
large economy that according to the National Bureau of Economic Research (NBER),
reached its peak in February 2020 and is in decline now. Consequently, it might experi-
ence less severe effects from the trade war with China than it would have experienced if
the global COVID-19 pandemic had not hit the economy.
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of Trade Tariffs: A Case Study of the U.S.-China Trade War Effects on the Economy of the United States
3. Analysis of the situation
During his campaign rally in 2016, Donald Trump accused China of the greatest theft
in the world and promised to fight unfair trading partners’ practices if he was elected.
After Donald Trump took the office, Section 232 of the Trade Expansion Act of 1962
investigations were launched in June 2017 that concluded that the imported steel and
aluminium “were threatening to impair the national security” and recommended to
“take action to protect a long-term viability of the nation’s steel and aluminium indus-
tries” (Presidential Memorandum, 2018). The method selected to fight unfair trading
partners’ practices – import tariffs – breached global trading rules and put the idea of
free trade on a test. The import tariffs on all steel and aluminium imports amounting to
nearly 18 billion USD were implemented in March 2018. The first China-specific tariffs
were imposed after the Section 301 of the Trade Expansion Act of 1974 investigation
on July 6, 2018.
FIGURE 1. Real trade balance of the U.S. with the world and
China
Source: compiled by authors, based on the data of the U.S. Bureau of Economic Analysis and International
Monetary Fund data.
Overall, the economists see the current trade deficit (Figure 1) as a weak argument
for the US to start a trade war. The study prepared by Oxford Economics (2017) to
the US-China Business Council demonstrated that the negative aspects of trade with
China were overstated. First of all, despite huge efforts to move up the value-added
chain, China still uses roughly 50% of the intermediate goods in computer equipment,
electronics, and electrical machinery assembly imported from foreign countries. Con-
sequently, the US trade deficit with China adjusted for the value-added content is twice
lower. Also, despite the trade deficit in goods, the US actually experiences a significant
and growing trade surplus in services. The intention to get Beijing to widely open its
market for American goods and services to provide American companies with more
favourable conditions and curb the Chinese high-tech sectors that are backed by the
state via the “Made in China 2025” strategy is seen as a more realistic rationale to start
the trade war (Ibrahim & Benjamin, 2019).
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The import tariffs imposed by the US are summarized in Table 1. The products that
were targeted first belong to such strategic Chinese sectors as machinery, mechanical
appliances, electrical equipment, information technology, robotics, and high-technolo-
gy medicine. Later, intermediate outputs and consumer goods were targeted. China re-
sponded with a kind of tit-for-tat retaliation, targeting not necessarily the same (but also
strategic) industries and products, and smaller amounts of total imports from the US.
TABLE 1. The U.S. import tariffs imposed under Sections 232 and 301
Investigation Effective Date Imposed Tariffs
Section 232 March 23, 2018 Tariffs on all imports of steel (25%) and aluminium (10%).
Section 301
July 6, 2018 25% tariffs on $34 billion of Chinese goods. List 1.
August 8, 2018 25% tariffs on $16 billion of Chinese goods. List 2.
September 18,
2018 10% tariffs on $200 billion of Chinese goods. List 3.
May 13, 2019 Tariffs increased to 25% for List 3 ($200 billion worth of
products).
Section 301
September 1,
2019
Part of the remaining goods get 15% tariff. List 4A starts
($112 billion worth of products).
December 15,
2019*
15% tariffs on the remaining List 4B goods (*suspended
indefinitely).
February 14,
2020 Tariff rate was reduced from 15% to 7.5% for List 4A goods.
Source: compiled by authors, data retrieved from Li, M. (2018) CARD Trade War Tariffs Database. Ac-
cessed on 25th of April 2020.
Both countries have already experienced the increasingly negative effects of the
trade war that particularly hit the farmers – a very important political constituency for
President Trump. For that reason, a subs idy program was created under which the
farmers received 8.5 and 14.3 billion USD for 2018 and 2019, respectively. No trade-re-
lated subsidies were paid out for 2020. In the context of the forthcoming U.S. pres-
idential election campaign, the pressure for the President to reach a consensus with
China increased, and the negotiations to start phasing out the import tariffs started. On
January 15, 2020, the US and China signed Phase 1 deal that cut the US tariffs for List
4A1 goods by half in exchange for China’s pledge to increase the purchases of American
products and services by at least 200 billion USD over the next two years. The tariffs on
List 4B2 that were scheduled to come into effect on December 15, 2019 were suspend-
ed indefinitely as well as China’s retaliatory actions to them.
1 List 4A consists of 3,230 Harmonized Tariff Schedule of the US (HTSUS) subheadings, mainly iron and
steel products, consumer electronics, motor vehicle products, telecommunications equipment, beverages,
chemicals, clothing, and footwear.
2 List 4B consists of 540 HTSUS subheadings, mainly clothing, toys, chemicals, cell phones, laptop computers,
small appliances, watches, and sports equipment.
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of Trade Tariffs: A Case Study of the U.S.-China Trade War Effects on the Economy of the United States
The main features of the trade war make it a great natural experiment for measuring
the economic effects of tariffs. First of all, an increase in import tariffs was highly unan-
ticipated, as most observers believed that Hillary Clinton, with a supposedly different
approach to the trade policy, would win the election. In addition, import tariffs exten-
sively vary across products and time. This specific feature simplifies the measurement of
import tariff economic effects using conventional datasets (Amiti et al., 2019).
COVID-19 economic effects cannot be overlooked when talking about the trade
war and calls for the additional assessment. Compared to the “regular” recessions, the
2020 global downturn is extraordinary in a way that it originated not from the financial
markets. McKibbin and Fernando (2020) used a hybrid global intertemporal general
equilibrium model to find that the global real GDP in 2020 would shrink by 2.4 trillion
USD and 9 trillion USD in case of a low-end and serious pandemic outbreak, respec-
tively. International trade is one of the leading mechanisms through which the virus
damages domestic economies. According to Baldwin and Weder di Mauro (2020), the
COVID-19 pandemic is a supply shock (factory closures, border closings) that will re-
duce exports and a demand shock that will reduce imports. Boone (2020) suggested
that world trade would be substantially weakened: under the best-case scenario (with
the epidemic contained in China with limited clusters elsewhere) global trade would
shrink by 0.9% in 2020, while under the downside scenario it could decline by around
3.75% in 2020. Baldwin and Tomiura (2020) suggested relying on the historical lessons
from global trade shocks such as the dotcom bubble in 2001, when the US imports
fell by 2.8%, or the financial crisis of 2009, when the global imports fell by 11.79% and
the imports in the US dropped by 13.08%. Based on those conclusions, the authors
assumed that global trade during 2020 might shrink between 5% to 15%.
4. Methodology of the research
The effects of trade tariffs can be empirically assessed with either equilibrium or regres-
sion models. The general equilibrium (GE) and partial equilibrium (PE) models are
used more widely. According to Rosyadi and Widodo (2018), the main advantage of
such models is that they provide an economy-wide examination of the trade policy and
more theoretically sound results in comparison to econometric estimations. The PE
models are vulnerable to criticism because they are said not to be able to capture the
economy-wide effects of the trade policy changes ( Jammes & Olareagga, 2005). The GE
models can take into account the second-round effects such as the exchange rate and the
inter-industry effects, all the markets are modelled simultaneously and interact. Howev-
er, the GE models are significantly dependent on the extensive underlying assumptions,
and their results are very sensitive to the changes in these assumptions. In addition, the
GE models work with large inter-industry aggregates, thus are not able to capture the in-
tra-industry details. Contrary to that, the partial equilibrium models have the advantage
of working at a very fine level of detail and thus avoiding this aggregation bias.
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The Single Market Partial Equilibrium Simulation Tool (SMART) developed by the
World Bank and UNCTAD was selected for further analysis of this paper due to its
ability to trace the complex effects of multilateral trade conflicts on a very detailed level.
The authors accept the shortcoming of the PE model – its sensitivity to elasticity values,
which will be eliminated by checking the model results with different elasticity values
(robustness analysis).
Even though the consumers in the home country can choose to buy products from
all over the world, and all the countries export at the same price worldwide, the prices
that the domestic consumers pay can vary due to the differences in tariffs. The model
relies on Armington’s (Armington, 1969) assumption of an imperfect substitution be-
tween the different import sources. Therefore, the consumers, when making consump-
tion decisions, will not necessarily choose import goods from the cheapest markets
only and will follow Armington’s two-step optimization process. Another important
assumption of the model is a perfect competition, which means that the increases or
cuts of tariff rates will be fully reflected in the prices paid by the consumers.
The SMART model requires three main variables as an input: trade tariff rates, trade
values, and elasticities.
Trade tariff rates. During the US-China trade war, import tariffs were imposed
in several waves across 2018 and 2019. The lists of currently effective trade tariffs are
lengthy and difficult to interpret. Li (2019) created a harmonized database – CARD
Trade War Tariffs Database – by including all the tariff increases during the US-Chi-
na trade war. This dataset provides raw tariff increase data collected from the official
sources aggregated to the six-digit Harmonized System (HS) codes. In addition to the
US-China trade tariffs, the model requires current duty rates for all the United States
trading partners on a six-digit HS product codes level. The information about the im-
port tariff rates that were applicable in 2019 was extracted from the United Nations’
UNCTAD’s TRAINS database.
Trade values. All the trade values between the US and its trading partners were
extracted from the UN Comtrade database. For the simulations around COVID-19
scenario, those values were reduced according to the findings in the Situation analysis
and inserted into the SMART model under the “SMART with User’s Data” function.
Elasticities. The SMART model incorporates three kinds of elasticities: import
demand, export supply, and import substitution. The import demand elasticity shows
how demand responds to a shift in import price. It varies between the different HS
6-digit product codes in the SMART model. The export supply elasticity is considered
infinite (i.e. 99). This means that the export supply curves are flat, and the world prices
for each product are given exogenously – the price taker assumption. The import sub-
stitution elasticity is set to 1.5 by default in SMART following Armington’s (Arming-
ton, 1969) assumption, and it indicates that similar goods from different countries are
not perfect substitutes.
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Sandra Žemaitytė, Laimutė Urbšienė. Macroeconomic Effects
of Trade Tariffs: A Case Study of the U.S.-China Trade War Effects on the Economy of the United States
After estimating the trade tariff effects on the selected macroeconomic variables with
default elasticities, the sensitivity analysis was carried out to check the robustness of the
results. The substitution elasticity was halved and then doubled according to Table 2.
TABLE 2. Elasticities used in the sensitivity analysis
Elasticity Lower bound Base case Upper bound Best case
Substitution elasticity 0.75 1.5 2.25 3
Export supply elasticity 99 99 99 99
Source: compiled by authors, according to Tu et al. (2020).
The empirical research focused on assessing the trade war effects on four US mac-
roeconomic indicators: trade diversion from China, trade balance, welfare, and trade
intensity. Furthermore, model output values for the selected variables were compared
with the US real GDP, which according to IMF projections, should decline by 8% in
2020 when compared to 2019 (US real GDP should be around 19,990.8 billion USD
in 2020) (IMF, 2020).
Trade creation effect results from the increase in the domestic demand for imports
from the trading partner after the imported good becomes less pricy than the domes-
tically produced substitute depending on the tariff rates decrease. The trade creation
effect is negative when the price of import or tariff increases.
Trade diversion effect refers to the substitution of goods coming from one foreign
supplier to another due to the changes in import prices and the difference between the
tariff rates offered to different foreign suppliers. The model captures the amounts of
trades and the countries where these trades will have to be diverted. A greater substitu-
tion elasticity set in the model results in higher trade diversion effects. In the US-
China
trade war case, the trade diversion from China showed the amount of imports that the
United States will have to source from other countries due to the increased tariffs. Con-
sequently, the trade diversion from China is neutral towards the total trade balance of
the US (it has effects only on trade balance with China).
The price effect. The model simulation will assume the infinite export supply elas-
ticity. Consequently, there will be no price effects.
The trade balance effect refers to a change in import and export values that result
from the trade tariff imposition. The effect on the US trade balance with China is the
difference between changes in the US imports and exports from and to China. The
change in the US trade balance with the world is comparably smaller because of the
trade diversion from the China effect.
The welfare effect refers to a change in deadweight loss. It sums up the changes in
the producer and consumer surplus and the changes in tariff revenues. The consumer
surplus losses and the producer surplus increase derive from the higher domestic prices
after the imposition of the tariffs due to the reduction of demand. In addition, the new
domestic price does not incline to the full extent of the tariff change.
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The trade intensity effect refers to a change in the trade volumes with a given trad-
ing partner. Tariffs are expected to reduce the trade intensity between the countries.
According to Frankel and Rose (1998), the US trade intensity with China can be calcu-
lated by normalising the bilateral trade flows by the total trade
(1)
(1)
or by nominal GDP in the two countries
(2)
.
(2)
The empirical research focused on two main scenarios: the trade war scenario and
the COVID-19 scenario.
The trade war scenario analysed the effects that the trade tariffs imposed between
the United States and China had on the selected variables (including Phase 1 deal). The
trade values from the UN-Comtrade database and the default sensitivity values were
used.
The COVID-19 scenario included the tariffs from the Trade war scenario but the
data was also impacted by the potential implications of the COVID-19 global pandem-
ic. It was achieved by taking the 2019 trade values from the UN-Comtrade database
at the disaggregated HS codes level and reducing them by 5% (optimistic case), 10%
(mild case), and 15% (downside case).
The SMART model reports are lengthy and present trade tariff effects per each HS6
product code and trading partner separately. Seeking to simplify the interpretation of
the results, the model output was consolidated into 15 broader categories according to
the HS2 product codes (Appendix A).
5. Results of the empirical research
5.1. The results of the Trade war scenario simulation and the sensitivity analysis
The utilization of the SMART model to simulate the impact of the trade war on the
United States economy (Table 3) revealed that the trade war entailed significant de-
clines in US exports and imports from China. The tariff increase simulation showed
that approximately 43,777 million USD of US imports would have to be obtained from
other trading partners than China. The US imports from China were projected to de-
crease by 108,458 million USD, and US exports to China were supposed to decrease by
23,661 million USD, which would make a positive effect on the total US trade balance
with China of 84,797 million USD. The significant difference between the changes in
imports and exports could be explained by the fact that China had imposed relatively
lower tariffs and far shorter lists of products. The US trade balance with the world was
calculated to improve by 41,020 million USD. As for the US welfare, it was calculated to
decrease by 2,193 million USD in 2020 solely due to the trade war. Amiti et al. (2019)
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Sandra Žemaitytė, Laimutė Urbšienė. Macroeconomic Effects
of Trade Tariffs: A Case Study of the U.S.-China Trade War Effects on the Economy of the United States
suggested deducting welfare losses from the trade balance improvement to reveal if the
domestic economy benefited from the import tariff imposition. According to the re-
sults shown in Table 3, the United States economy will benefit from the trade war.
TABLE 3. The model output for the Trade war scenario (in millions of USD and % of GDP)
Trade diversion
from China
US imports
from China
US exports
to China
Trade balance
with China
Trade balance
with world Welfa
re
43,777
(0.22%)
-108,458
(0.54%)
-23,661
(0.12%)
84,797
(0.42%)
41,020
(0.21%)
-2,193
(0.011%)
Source: compiled by authors, based on SMART model simulations and IMF GDP projections for 2020.
Also, based on the model results, the authors calculated that the US trade intensity
with China would decrease by 1.13% and 0.29% when normalising the bilateral trade
flows by the total trade and nominal GDPs, respectively.
Appendix B shows that the US targeted China’s machinery and electrical products,
while China targeted America’s agricultural and automobile sector products with its
imposed tariffs. The latter consequences were already visible in the market given the
many complaints from the US farmers and car producers, who claimed that the trade
war had cost them China’s market. On the other hand, it is evident from Appendix B
that the US will significantly improve trade balance with China not only for machinery
and electrical products but also for miscellaneous products. This includes medical in-
struments, arms, and ammunition, furniture, etc. Unfortunately, the US welfare will be
hurt the most by the product categories that received the heaviest import tariff burden.
Our results are close to the results obtained by Tu et al. (2020) who also employed
the SMART model on the three lists of the US-China tariffs (List 4 and Phase 1 deal
excluded) and found that the US imports from China and the US exports to China were
expected to be reduced by 91,459 and 36,706 million USD, respectively, and the trade
diversion would be 36,783 million USD. In addition, they estimated that the US welfare
was likely to shrink by 1,437 million USD. Taking into account that the authors of this
paper included lengthier import tariff lists, the projected declines in the US imports and
welfare were found to be more sizable, accordingly.
The sensitivity analysis of the Trade war scenario presented in Table 4 shows that
the lower substitution elasticity resulted in smaller trade war effects on trade diversion
and trade balance, and a higher decrease in welfare. The higher substitution elasticity
had the opposite effect.
The direction of changes in the model values was expected. For example, under the
higher substitution elasticity, the loss in welfare was lower because the consumers could
more easily switch to other goods. The disaggregated model results also suggested that
changing substitution elasticity did not affect the weights of each product category in
the total change of the macroeconomic indicators. The comparison of the results of the
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sensitivity analysis with the base case showed that the results of the cases under the
lower bound and the upper bound (substitution elasticity 0.75 and 2.25, respectively)
varied from the base scenario by approximately the same amount (Table 5).
TABLE 5. Changes from the base case in the sensitivity test of the Trade war scenario
(by percentage)
Substitution
elastic
ity
Trade
diver-
sion from
China
US imports
from China
US exports
to China
Trade bal-
ance with
China
Trade bal-
ance with
world
Welfare
0.75 -50.72% 20.47% 4.996% -24.79% 2.88% -9.40
%
2.25 52.48% -20.67% -4.999% 25.05% -4.22% 16.5
1%
3 106.37% -42.43% -10.001% 51.47% -7.11% 25.9
0%
Source: calculated by authors.
The trade diversion from China was found to be the most sensitive to the selected
substitution elasticity value. On the other hand, the changes in the trade balance with
the world and welfare adjusted by only 2.88% to 16.51% by halving and doubling the
substitution elasticity value. This suggests the robustness of the model results.
In line with the economic theory, the US-China trade war leads to significant de-
creases in the US bilateral trade flows and trade intensity with China. Both the US trade
balances with China and the whole world were calculated to improve significantly.
Moreover, the gains in terms of trade were found to outweigh the losses in the coun-
TABLE 4. The sensitivity analysis of the Trade war scenario (in millions of USD and % of GDP)
Substitu-
tion elastic-
ity
Trade
diversion
from China
US imports
from China
US exports
to China
Trade bal-
ance with
China
Trade bal-
ance with
world
Welfare
0.75 21,575
(0.11%)
-86,256
(0.43%)
-22,479
(0.11%)
63,777
(0.32%)
42,202
(0.21%)
-2,399
(0.012%)
1.5
(base sce-
nario)
43,777
(0.22%)
-108,458
(0.54%)
-23,661
(0.12%)
84,797
(0.42%)
41,020
(0.21%)
-2,193
(0.011%)
2.25 66,749
(0.33%)
-130,880
(0.65%)
-24,843
(0.12%)
106,037
(0.53%)
39,288
(0.20%)
-1,831
(0.009%)
3 90,342
(0.45%)
-154,473
(0.77%)
-26,027
(0.13%)
128,446
(0.64%)
38,104
(0.19%)
-1,625
(0.008%)
Source: compiled by authors, based on SMART model simulations and IMF GDP projections for 2020.
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of Trade Tariffs: A Case Study of the U.S.-China Trade War Effects on the Economy of the United States
try’s welfare. This might be explained by the relatively weak China’s retaliatory response
in avoiding to deepen the trade conflict. China is still a middle-income economy that
greatly relies on the US products, technologies, and the workplaces they create. China’s
unemployment is relatively high, and Beijing is hesitant to take any actions that would
cost jobs.
5.2. The results of the COVID-19 scenario simulation and the sensitivity analysis
The utilization of the SMART model to simulate the impact of the trade war on the
United States economy under the three different cases of the COVID-19 scenario (Ta-
ble 6) showed that the potential economic consequences of the COVID-19 pandem-
ic reduced the relative effects of the US-China trade war. Moreover, the harsher the
COVID-19 pandemic hits the global trade, the smaller impact the trade war is relatively
likely to have on the US trade diversion from China, trade balance, and welfare. In all
the three cases, the US economy was found to be benefiting from the trade war as gains
in terms of trade outweigh welfare losses.
TABLE 6. The model output for the COVID-19 scenario (in millions of USD and % of GDP)
Substitution
elasticity
Trade diversion
from China
US imports
from China
US exports
to China
Trade balance
with China
Trade balance
with world Welfare
Optimistic case (-5%)
0.75 20,589
(0.10%)
-82,036
(0.41%)
-21,355
(0.11%)
60,681
(0.30%)
40,092
(0.20%)
-2,212
(0.011%)
1.5
(base scenario)
41,777
(0.21%)
-103,224
(0.52%)
-22,478
(0.11%)
80,746
(0.40%)
38,969
(0.19%)
-2,017
(0.010%)
2.25 63,532
(0.32%)
-124,978
(0.63%)
-23,601
(0.12%)
101,377
(0.51%)
37,846
(0.19%)
-1,804
(0.009%)
3 85,800
(0.43%)
-147,246
(0.74%)
-24,726
(0.12%)
122,521
(0.61%)
36,722
(0.18%)
-1,610
(0.008%)
Mild case (-10%)
0.75 19,505
(0.10%)
-77,718
(0.39%)
-20,104
(0.10%)
57,614
(0.29%)
38,109
(0.19%)
-2,096
(0.010%)
1.5
(base scenario)
39,578
(0.20%)
-97,791
(0.49%)
-21,041
(0.11%)
76,749
(0.38%)
37,172
(0.19%)
-1,911
(0.010%)
2.25 60,188
(0.30%)
-118,401
(0.59%)
-21,979
(0.11%)
96,422
(0.48%)
36,233,760
(0.18%)
-1,709
(0.009%)
3 81,284
(0.41%)
-139,497
(0.70%)
-22,918
(0.11%)
116,579
(0.58%)
35,295,
322
(0.18%)
-1,525
(0.008%)
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Substitution
elasticity
Trade diversion
from China
US imports
from China
US exports
to China
Trade balance
with China
Trade balance
with world Welfare
Downside case (-15%)
0.75 18,303
(0.09%)
-72,814
(0.36%)
-19,107
(0.10%)
53,707
(0.27%)
35,405
(0.18%)
-1,948
(0.010%)
1.5
(base scenario)
37,139
(0.19%)
-91,650
(0.46%)
-20,111
(0.10%)
71,538
(0.36%)
34,400
(0.17%)
-1,773
(0.009%)
2.25 56,477
(0.28%)
-110,988
(0.56%)
-21,117
(0.11%)
89,872
(0.45%)
33,395
(0.17%)
-1,583
(0.008%)
3 76,427
(0.38%)
-130,938
(0.65%)
-22,123
(0.11%)
108,816
(0.54%)
32,389
(0.16%)
-1,408
(0.007%)
Source: compiled by authors, based on SMART model simulations and IMF GDP projections for 2020.
Appendix C provides the COVID-19 scenario trade effects disaggregated into fif-
teen product categories. The proportions by which each product line contributes to the
absolute trade effect value in the COVID-19 scenario did not differ much from those of
the Trade war scenario. This can be explained by the fact that the trade values were the
only thing that was different from the Trade war scenario. The results of the sensitivity
analysis (Table 6) showed that a lower substitution elasticity made a smaller impact on
trade diversion, imports, exports, trade balance, and welfare values. The opposite also
appeared to be true. These findings corresponded to those of the Trade war scenario
and once again proved the accurateness of the model: when elasticity and trade values
were exposed to change.
Furthermore, to identify the main trends, the results of the sensitivity analysis were
compared with the base case, and the percentage changes were calculated (Table 7).
In line with the analysis of the sensitivity results of the Trade war scenario, the
changing substitution elasticity did not significantly affect the US exports and the trade
balance with the world’s figures. Consequently, it can be concluded that the US exports
and trade balance with the world estimations tend to be robust. In relative terms, the
estimations of other trade effects were found to be not significant in absolute terms.
All things considered, the potential economic effects of the COVID-19 global pan-
demic reduced the US-China trade war effects. Despite that, the changes to the US
trade diversion from China, trade balance with the world, and welfare still comprised
0.009% to 0.21% of the forecasted 2020 real GDP. Moreover, even taking into account
the potential effects of the global COVID-19 pandemic, the US will still able to benefit
from the trade war, as under all the three cases, the gains from the improved trade bal-
ance were more significant than welfare losses. Nevertheless, the COVID-19 scenario
was constructed based on the economists’ projections that were made at the begin-
ning of the summer 2020. These predictions might have radically changed with another
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of Trade Tariffs: A Case Study of the U.S.-China Trade War Effects on the Economy of the United States
COVID-19 pandemic wave hitting in the autumn. Thus, the actual picture is prone to
change and may overtake the one predicted in the downside case.
TABLE 7. Changes from the base case in the sensitivity test of the COVID-19 scenario
(by percentage)
Substi-
tution
elasticity
Trade
diver-
sion from
China
US im-
ports from
China
US exports
to China
Trade bal-
ance with
China
Trade bal-
ance with
world
Welfare
Optimistic case (-5%)
0.75 50.72% 20.53% 4.996% -24.85% 2.881% -9.70%
2.25 -52.07% -21.08% -4.999% 25.55% -2.883% 10.55%
3 -105.38% -42.65% -10.001% 51.74% -5.769% 20.1
9%
Mild case (-10%)
0.75 50.72% 20.53% 4.454% -24.93% 2.521% -9.70%
2.25 -52.07% -21.08% -4.457% 25.63% -2.523% 10.55%
3 -105.38% -42.65% -8.917% 51.90% -5.048% 20.19%
Downside case (-15%)
0.75 50.72% 20.55% 4.995% -24.93% 2.920% -9.83%
2.25 -52.07% -21.10% -4.999% 25.63% -2.922% 10.71%
3 -105.79% -42.87% -10.001% 52.11% -5.847% 20.5
8%
Source: calculated by authors.
6. Conclusions
The partial equilibrium SMART model simulations suggest that the United States’ total
trade balance will improve by 41,020 million USD (0.21% of real GDP) in 2020, while
43,777 million USD (0.22% of real GDP) of US imports will have to be sourced from
other countries. In addition, the welfare will decrease by 2,193 million USD (0.011%
of real GDP). The US trade intensity with China will decline by 1.13% and 0.29% when
normalising bilateral trade flows by the total trade and nominal GDPs, respectively. The
potential COVID-19 economic consequences were found to reduce the trade war’s rel-
ative effects. The model estimations passed the robustness tests.
As for the wider implications, the US seems to benefit from the trade war. This
is mainly associated with the greater US market power and a relatively weak China’s
retaliatory response – a fear to escalate the trade conflict that would further hurt its
domestic companies and employment figures. However, the trade war effects between
the US industries are uneven. The US agricultural and automotive industries are hurt
more than others. If the trade conflict continues to escalate, not only the consumers will
suffer from the increasing prices, but also the fractured input-output linkages and the
320
ISSN 2029-4581 eISSN 2345-0037 Organizations and Markets in Emerging Economies
economic uncertainty will hurt some industries even further. This research is especially
important for policymakers considering the potential effects of the trade tariffs impo-
sition. The success of such protectionist policies is proven to considerably depend on
the trading partner’s retaliatory actions. In addition, a careful examination of the trade
policy’s possible effects on the country’s welfare, supply chains, and trade flows should
be performed before taking any actions. After all, trade wars are neither good nor easy
to win.
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APPENDICES
A p p en d i x A
Harmonized System (HS2) product categories
HS2 Product categories HS2 Product categories
01-05 Animal & Animal Products 50-63 Textiles
06-15 Vegetable Products 64-67 Footwear / Headgear
16-24 Prepared Foodstuffs 68-71 Stone / Glass
25-27 Mineral Products 72-83 Metals
28-38 Chemicals & Allied Industries 84-85 Machinery / Electrical
39-40 Plastics / Rubbers 86-89 Transport Equipment
41-43 Raw Hides, Skins, Leather, & Furs
90-97
Miscellaneous (medical instruments,
arms and ammunition, furniture, toys,
works of art, etc.)44-49 Wood & Wood Products
Source: compiled by authors, according to Harmonized Systems 2017 description presented in the UN
Trade Statistics website.
https://www.bea.gov/data/intl-trade-investment/international
https://www.nber.org/cycles.html
https://wits.worldbank.org/wits/wits/witshelp/Content/Data_Retrieval/P/Intro/C2.Background_on_Trade_Policy.htm
https://wits.worldbank.org/wits/wits/witshelp/Content/Data_Retrieval/P/Intro/C2.Background_on_Trade_Policy.htm
https://www.wto.org/english/thewto_e/whatis_e/tif_e/understanding_e
https://www.wto.org/english/thewto_e/whatis_e/tif_e/understanding_e
323
Sandra Žemaitytė, Laimutė Urbšienė. Macroeconomic Effects
of Trade Tariffs: A Case Study of the U.S.-China Trade War Effects on the Economy of the United States
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José Satsumi López-Morales, Felipe de Jesús Rosario-Flores, Antonio Huerta-Estevez.
Business in the Base of the Pyramid: A Literature Review and Directions for Future Research
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326
ISSN 2029-4581 eISSN 2345-0037 Organizations and Markets in Emerging Economies
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Copyright of Organizations & Markets in Emerging Economies is the property of Vilnius
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Journal of Accounting,
Auditing & Finance
2022, Vol. 37(1) 39–76
�The Author(s) 2018
Article reuse guidelines:
sagepub.com/journals-permissions
DOI: 10.1177/0148558X18781144
journals.sagepub.com/home/JAF
Controlled Currency Regime
and Pricing of Exchange Rate
Risk: Evidence From China
Xiuping Hua1, Wei Huang2, and Ying Jiang1
Abstract
Extant research finds that exchange rate pegs do little to reduce firms’ exposure to
exchange rate risk in emerging markets. We study whether exchange rate risk exposures
under a pegged/controlled floating currency regime can be priced in asset returns using
unique data on exchange rate regime changes in China. As the only currency in the basket
of International Monetary Fund’s (IMF) Special Drawing Rights that is not fully market-
driven for its exchange rate formation, the Chinese RMB (renminbi) offers an ideal case to
study the pricing of exchange rate risk in pegged and controlled floating regimes. We find a
negative stock market reaction to the announcement of changes from a pegged exchange
rate to a controlled floating regime, suggesting a negative stock valuation effect of expecta-
tion of increased exchange rate exposure to currency appreciation. Under the managed
floating exchange rate regime, the volatilities of accounting performance measures are sig-
nificantly greater for the companies with stronger exposure to foreign-exchange rate risk.
More importantly, foreign-exchange rate risk is priced in equity returns under the con-
trolled floating regime but not under the strictly pegged regime. Furthermore, the pricing is
stronger for firms with higher sensitivity to economic channels transmitting the exchange
rate risk exposure to risk premiums. Overall, our results suggest that currency hedging for
a multinational corporation with foreign-exchange exposure is warranted under a con-
trolled floating currency regime.
Keywords
exchange rate risk, asset returns, equity risk premium, foreign-exchange rate risk hedging,
Chinese currency risk
Introduction
Recent literature has examined the potential link between currency arrangements and the
extent of firm level exchange rate exposure. Among others, Parsley and Popper (2006)
examined the foreign-exchange exposure of firms with and without exchange rate pegs.
1Nottingham University Business School China, The University of Nottingham Ningbo China, China
2University of Hawaii at Manoa, Honolulu, USA
Corresponding Author:
Wei Huang, Shidler College of Business, University of Hawaii at Manoa, 2404 Maile Way, Honolulu, HI 96822, USA.
Email: weih@hawaii.edu
Article
https://doi.dox.org/10.1177/0148558X18781144
They found that firms in Asian-Pacific countries had more widespread exposure to foreign-
exchange rate risk than firms in western industrialized countries, particularly to USD fluc-
tuations. Interestingly, their results suggest that exchange rate pegs do not diminish the
widespread exposure to exchange rate risk. By contrast, other studies, such as Jorion
(1990), Bodnar and Gentry (1993), and Muller and Verschoor (2006), found exchange rate
exposures among firms in the United States and other industrialized countries are
moderate.
In view of the significant foreign-exchange risk exposure under a pegged system in
emerging markets, an important question that follows is whether the foreign-exchange rate
exposures under a pegged currency regime can be priced in asset returns. As stated in the
conclusion of Parsley and Popper (2006), this issue matters because it is related to whether
firms under a pegged currency regime should care about their exchange rate exposure and
hedge foreign-exchange rate risk. The issue has become more important following extraor-
dinary events such as the Asian financial crisis that show how exchange rate crises can gen-
erate widespread impact beyond currency markets. Indeed, fixed or inflexible exchange
regimes may contribute to stronger and more rapid domestic credit growth than a flexible
exchange regime, especially when there is large capital inflow and foreign-exchange inter-
vention (Reinhart & Montiel, 2001).
In this article, we explore China’s unique currency arrangement to examine whether and
to what extent the exchange rate risk exposure may be priced in asset returns under a con-
trolled currency regime. After more than a decade of pegging the renminbi (RMB) to the
USD, China adopted a controlled floating regime in 2005, setting its target for the RMB
based on a ‘‘reference basket’’ of currencies. Subsequently, during a 2-year period which
included the global financial crisis of 2008, China effectively pegged its currency against
the USD. In recent years, China has adopted a tightly controlled currency regime and has
kept the Yuan steady against the dollar by setting a midpoint for the value of the USD. In
daily trading, the Yuan is allowed to move 2% above or below that midpoint, which is
called the daily fixing. Despite its tight control mechanism, Chinese currency regime offers
an optimal setting to study the pricing of exchange rate risk under a pegged/controlled
floating regime for several reasons.
First, the regime change from pegging to the USD to a controlled floating that pegs to a
basket of currencies offers an experimental data to examine if the pricing of the exchange
rate exposure is different between strictly pegging and controlled floating regime. Under
the controlled floating regime, although the central bank has implemented a ‘‘daily fixing’’
value for the USD, it does not as regularly influence the exchange rate against other foreign
currencies, allowing the Yuan to follow the dollar’s rise against other currencies in the
basket such as the Euro. Therefore, it is likely that the exposure to exchange rate risk under
two different regimes may have different impact on asset return.
Second, China has robust economic channels that can transmit exchange rate risk expo-
sure to equity market risk premiums. This includes active international trades, cross-border
capital flows, and frequent domestic credit expansion. Third, there are off-shore markets
for the Chinese Yuan. The currency, both spot and derivatives, are traded and on the clear-
ing centers in Hong Kong, London, Frankfurt, and Singapore. The market-driven ‘‘off-
shore’’ exchange rate often deviates from the controlled ‘‘on-shore’’ exchange rate set by
the central bank, thus creating market-driven impact on the otherwise government con-
trolled exchange rate determination process.
Finally, the Chinese Yuan has risen as a major currency for international payments.1 On
November 30, 2015, International Monetary Fund (IMF) announced that it would formally
40 Journal of Accounting, Auditing & Finance
include the RMB as one of the five currencies in the basket of IMF’s special drawing
rights (SDR), effective on October 1, 2016. IMF set the weight for the RMB at 10.92%,
which is behind the weight for the USD (41.73%) and the Euro (30.93%), but is ahead of
that for Japanese Yen (8.33%) and the British pound (8.09%). The IMF’s move will help
the country’s market-oriented reform in its exchange rate formation and ultimately make
the RMB one of the major currencies for international reserve and settlement. Overall,
China’s currency regime offers a unique opportunity to study whether the exchange rate
risk can be priced under a pegged or a controlled floating currency regime. Other pegged
currencies, however, do not offer such an opportunity because of less robust transmission
channels or lack of managed floating mechanism.2
The pricing of exchange rate risk has attracted significant attention in academic
research. International capital asset-pricing theory suggests that the covariance of asset
returns with currency returns should be a priced factor in a world where purchasing power
parity does not hold (Adler & Dumas, 1983). Currency risk has typically been included
among the hypothesized factors in international asset pricing models (e.g., Solnik, 1974,
1997; Stulz, 1981a, 1981b). Studies using multicountry joint test have shown evidence of
pricing of exchange rate risk in asset returns. For example, using a three-factor model that
includes world, country, and exchange risk factors, Choi and Rajan (1997) found that
exchange risk is a significant factor affecting asset returns in addition to the domestic and
world market factors in the seven major industrialized markets, notwithstanding partial
market segmentation and local country factor. Similarly, Dumas and Solnik (1995) and De
Santis and Gérard (1998) found evidence supporting a specification of the international
Capital Asset Pricing Model (CAPM) that includes both market risk and foreign-exchange
risk for major developed markets. Carrieri and Majerbi (2006) conducted a multinational
asset pricing test for emerging markets and find significant unconditional risk premium for
exchange risk.
One limitation in interpreting the multinational asset pricing test is that it is a joint test
of market segmentation and exchange risk pricing. Differences in currency regimes across
countries further confound the interpretation of joint-test results. Considering that the factor
structure of asset returns is generally heterogeneous internationally (Choi & Rajan, 1997)
and currency risk premiums vary over time and across markets, researchers also stress the
importance of a single country test. Studies using a single country test show less consistent
results. For example, Jorion (1990, 1991) found that although U.S. equity values react to
fluctuations of the trade-weighted value of the dollar, the risk premium associated with for-
eign currency exposure is insignificant in the U.S. stock market and therefore active hed-
ging should not affect the cost of capital. Kolari, Moorman, and Sorescu (2008) showed
that foreign-exchange risk is priced in the cross-section of U.S. stock returns. However,
contrary to the multinational tests, they find that stocks most sensitive to foreign-exchange
risk (in absolute value) command lower returns, implying a nonlinear, negative premium
for foreign-exchange risk in the U.S. market.
In this article, we examine the pricing of exchange rate risk under a pegged/controlled
floating currency regime in an emerging market setting.3 Given the findings in prior studies
that firms in emerging markets with pegged currency regime experience significant
foreign-exchange risk exposure, we aim to understand whether and how exchange rate risks
under a pegged as well as controlled floating regime are priced in asset returns and as a
result whether such exchange rate risk exposure should be hedged.
Our findings can be summarized as follows. First, there was a negative stock market
reaction to the announcement of changes from a pegged exchange rate to a controlled
Hua et al. 41
floating regime that subsequently led to waves of currency appreciation. This suggests that
on average there is a negative stock valuation effect of expectation of increased exchange
rate risk exposure. By contrast, Chinese shares listed on the Hong Kong stock exchange do
not show the same effect due to Hong Kong’s pegged currency regime. Second, the volati-
lities of earnings, return on asset (ROA) and price-earnings ratios (P/E ratio) are signifi-
cantly greater for ‘‘export’’ companies than for nonexport companies after the exchange
rate reform in 2005. Third, results from asset pricing tests indicate that foreign-exchange
rate risk is not priced in stock returns under the strictly pegged exchange rate regime, but it
is priced under a controlled floating regime for the period after 2005. In particular, for the
sample period after 2005 reform, the asset pricing models that include exchange rate risk
factor show considerable reductions in asset pricing errors, in the form of decreases in the
absolute and squared alphas of various asset pricing models. Fourth, the pricing of
exchange rate risk is more pronounced for companies that are highly sensitive to economic
channels, such as international trade, hot money, and local credit market condition, which
transmit the exchange rate risk exposure to asset risk premiums. Similarly, Chinese stocks
listed on the Hong Kong stock exchange are not sensitive to the three transmission chan-
nels. Our findings suggest that in the presence of robust transmission channels, currency
hedging against foreign-exchange rate risk exposure is warranted under a controlled float-
ing exchange rate regime. This prediction has been borne out in the increasing use of cur-
rency derivatives in hedging by Chinese companies.
The remainder of the article is structured as follows. The section ‘‘Institutional Details’’
provides institutional background on China’s exchange rate regime and discussion of eco-
nomic channels transmitting the exchange rate risk to asset risk premium. The section
‘‘Data Descriptions’’ describes the data. The section ‘‘Empirical Results’’ presents empiri-
cal results for the link between exchange rate risk and stock returns. The section
‘‘Transmission Channels and Pricing of Exchange Rate Risk’’ analyzes whether the pricing
of exchange rate risk is associated with firms’ sensitivity to possible economic channels
that transmit exchange rate risk exposure to stock returns. The section ‘‘Conclusion’’ offers
concluding remarks.
Institutional Details
Overview of China’s Exchange Rate Arrangements
During the period from 1979 to 1994, China had a dual RMB exchange rate regime, in
which the official rate and the market rate coexisted. The market value of RMB per USD
was always greater than the official rate and both exchange rates depreciated against the
USD continuously.4 In 1994, the Chinese monetary authority adopted exchange rate regime
that was pegged to the USD. During 1994 and 1997, the RMB appreciated steadily against
the USD. The currency value was then pegged to the USD at the level of US$1 to 8.28
RMB Yuan between October 1997 and July 2005.
As part of the reform toward a market-driven exchange rate determination, on July 21,
2005, the People’s Bank of China (PBOC), the nation’s central bank, adopted a new
regime that pegs the RMB to a ‘‘reference basket’’ of foreign currencies, including some
Asian currencies. The PBOC stated that the selection of assigned index weights is in line
with China’s real external sector development and the basket should be composed of cur-
rencies of the countries to which China has a prominent exposure in terms of foreign trade,
external debt, and foreign direct investment (FDI). The PBOC provided only guidelines
42 Journal of Accounting, Auditing & Finance
about the composition and trade weights of the reference basket. Because the United States
is China’s most important trading partner and the majority of exports from China are
denominated in USD, the USD presumably accounts for the largest trade weight in the
basket. Indeed, Spiegel (2005) found that movements in China’s trade-weighted exchange
rate indexes over the long term are relatively insensitive to currency composition. The
RMB appreciated by 2.1% immediately and a cumulative 21% against the USD by July
2008. The appreciation trend ended during the global financial crisis. To reduce uncertainty
and stimulate exports during the global financial crisis, China effectively pegged the Yuan
to the USD at 6.8274 Yuan per USD until June 2010. On June 19, 2010, China resumed
the reform to increase flexibility in exchange rate fluctuations. By June 2015, the RMB
rose to a new record of 6.1161 Yuan per USD, marking a 10.42% appreciation since the
resumption of exchange rate reform in June 2010. In recent years, China has implemented
a tightly controlled floating regime by setting the midpoint for the value of USD and allow-
ing a 2% daily fluctuation. Although still under a heavily managed regime, the value of the
RMB has been more volatile recently. Figure 1 describes the major changes in China’s for-
eign-exchange regime since 1979, including the exchange rate regime change on July 21,
2005.
Economic Channels Transmitting Exchange Rate Exposure to Asset Risk Premium
The exchange rate may affect a firm’s profitability directly, if the firm has either foreign
operations or assets and debt denominated in foreign currency. Moreover, the exchange rate
can have an indirect impact on firms’ profitability even if a firm has no foreign currency rev-
enues because the exchange rate regime can affect foreign competition and domestic macroe-
conomic conditions. Thus, a potentially wide range of firms could be exposed to movements
in foreign-exchange rates, regardless of their direct financial exposure (Parsley & Popper,
2006). To study the pricing of foreign-exchange risk, it is important to understand the
mechanisms that transmit foreign-exchange rate risk exposure to stock returns. The extent of
pricing of exchange rate risk exposure may depend on firms’ sensitivity to the economic
channels that transmit the exchange rate risk to asset returns. In view of related literature and
China’s market-oriented reform as well as prevailing macroeconomic policy, we propose
three economic channels through which exchange rate risk exposure may be transmitted to
stock returns: International trade, hot money channel, and domestic credit supply.
International trade. The most recognized transmission channel for exchange rate risk is
international trade. It is well known that currency appreciation generally raises the relative
price of domestic goods, creating less favorable terms of international trade that is detri-
mental to exports. Dornbusch and Fischer (1980) and Pavlova and Rigobon (2007) sug-
gested that domestic stock prices should fall in response to increasing domestic currency
value and vice versa due to trade linkage. However, currency depreciation would stimulate
exports and curtail imports. Therefore, for industries generating significant earnings from
export, currency appreciation (depreciation) may cause stock valuation to decline
(increase). The opposite may hold for industries with heavy imports.
The number of listed companies with exports in China has continued to rise over the
last decade. Appendix A reports the industry distribution of listed companies (Panel A) and
the percentage of companies with exports in sales (Panel B). It appears that the percentage
of companies that have revenues from exports increases over time for almost all industries
except for real estate and media. The percentage of total listed companies with exports
Hua et al. 43
increased from 6% in 2001 to 54% in 2014. Of those listed companies with high percen-
tages of revenue from exports, changes in sales and profits in international trade provide an
explicit transmission channel for the exchange rate risk to impact stock prices.5 Hence, the
pricing of exchange rate risk should be more pronounced for companies with higher sensi-
tivity to terms of trade.
Hot money channel. Another potential transmission channel through which the exchange
rate may affect asset pricing is speculative capital flow or so-called hot money.
Figure 1. China’s currency regime changes since 1979.
Note. This figure documents major changes/events in the reform of China’s currency exchange rate regime since 1979.
44 Journal of Accounting, Auditing & Finance
International capital flow induced by speculation, either because of new information or irra-
tional exuberance or fear, may result in changes in risk premia in asset markets (Duarte &
Stockman, 2005). Furthermore, emerging countries generally have more volatile capital
inflows than developed countries (Broner & Rigobón, 2006). We therefore consider capital
flows as one transmission channel for exchange rate risk, even though empirical evidence
on whether stock market behavior in emerging economies is affected by international capi-
tal flows remains inconclusive (Agosin & Huaita, 2012).
As China slowly liberalizes its capital account over the last decade, foreign investors
have been channeling money into Chinese real estate, stock markets, bank accounts, and
other investments with the expectation of local currency appreciation. The speculative capi-
tal inflow may have fueled inflation, driven up stock prices, and helped accelerate a harm-
ful bubble in the real estate market to some extent. Given the importance of international
capital flow following the development of financial globalization in China, we conjecture
that firms with stock prices that are more sensitive to the hot money channel are more
likely to respond to exchange rate risk.6 However, the responses of stock returns to interna-
tional capital flows may be ambiguous. On one hand, the sudden inflow of large amounts
of hot money would increase the money supply in China and would help create a credit
boom, and consequently, the credit expansion induced by hot money may generate positive
valuation effects on asset prices. On the other hand, hot money generally increases with the
expectation of RMB appreciation, which may have negative effect on the terms of trade for
export-oriented industries. As such, the inflow of hot money may be taken by investors as
a signal to possible RMB appreciation as well as deterioration of terms of trade. Investors
would expect a decline in the profit margin and competitiveness of export-oriented compa-
nies, which may cause negative equity valuation effects on those firms.
Domestic credit supply. Related to the hot money channel, existing studies have also noted
that the exchange rate regime can affect risk premia in asset markets through international
reserve accumulation and local credit expansion. In particular, fixed or inflexible exchange
regimes may contribute to stronger and more rapid domestic credit growth than flexible
regimes, especially in the context of large capital inflows and heavy foreign-exchange
intervention (Reinhart & Montiel, 2001). Empirically, the exchange rate regime flexibility
has a statistically significant impact on domestic credit levels despite controlling for capital
inflows (Magud, Reinhart, & Vesperoni, 2014). Thus exchange rate risk exposures may be
transmitted into asset risk premia through local credit markets.
The persistent trade surplus of China in recent years generated rapid accumulation of
foreign reserves, which led to appreciation pressure on the RMB exchange rate.7 China’s
central bank has long purchased foreign-exchange assets and maintained capital account
control which resulted in a large injection of base money and the excess domestic credit
expansion in the form of domestic broad money supply (M2). The reform in the exchange
regime in July 2005 had not changed this trend. To some extent, an appreciation trend
of RMB since July 2005 may in fact have increasing instead of decreasing pressure on
foreign-exchange market intervention because of the rising inflow of speculative capital.
China’s holdings of foreign reserves have risen from US$140 billion in 1997 to more
than US$3.33 trillion at the end of 2015. Over the same period, the central bank injected
more than 20 trillion RMB Yuan to buy the country’s US$3.33 trillion foreign-exchange
reserves, which forced the base currency in circulation to surge. Based on official statistics,
in the process of this reserve buildup, China’s broad money supply (M2) has expanded at
an average rate of 16.5% a year from 1999 to 2015. Both China’s international reserve and
Hua et al. 45
M2-to-GDP ratio are now the highest in the world. Because the credit expansion may be
highly correlated with expansion of asset bubbles in China’s property, commodity, and
stock markets, for listed companies in some sectors, such as real estate and mining indus-
tries, exchange rate risk exposures may be transmitted to equity risk premium through this
channel.8 In view of the significant effect of exchange rate policy on credit expansion, we
hypothesize that companies with equity prices more sensitive to domestic credit expansion
channels are also more likely to have significant risk premium for exchange rate risk
exposure.
Data Descriptions
Our sample contains all listed ‘‘A’’ shares on the Shanghai Stock Exchange and Shenzhen
Stock Exchange. Data are obtained from Wind Information Co. Ltd. We use monthly divi-
dend adjusted prices for the period from July 1995 to June 2015. We exclude the sample
from 1993 to 1994 because only a limited number of stocks were traded during this period
in China. We also exclude those stocks if more than 8 months of prices are missing for a
given year. Panel A of Appendix A shows that the final number of companies in the
sample ranges from 280 to 2,593 during the sample period. Following the industry classifi-
cation standard of China’s Securities Regulatory Commission, we report the number of
companies in 18 industries: agriculture, forestry, stockbreeding, and fishing; mining; manu-
facturing; utilities including electricity, heating, gas, and water; construction; whole sale
and retail; transportation, storage, and post; hotel and restaurant; information technology;
financial institutions; real estate; renting and business services; science research and tech-
nology services; water conservancy, environment, and public facilities; education; health
and social work; culture, sport, and entertainment; and other industries.9 The Chinese stock
market grew fast in the last two decades, especially in the manufacturing industry. For
example, the number of stocks in manufacturing increased more than 10 times since 1995
and accounts for more than half of the total number of listed companies.
To examine the impact of foreign trade on the pricing of exchange rate risk, we distin-
guish companies with export business from those without each year in each industry. Panel
B of Appendix A reports the percentage of companies with export revenues in each indus-
try.10 In most of industries, the percentage of companies with export revenues steadily
increased over time. For example, for manufacturing industry, this percentage increased
from 8% in 2001 to 70% in 2014, while for construction industry, the percentage increased
from 5% in 2001 to 45% in 2014.
Following the conventions in the exchange rate quote, we adopt two nominal measure-
ments for RMB exchange rates. The first series is the nominal RMB versus USD spot
exchange rate (denoted hereafter as RMB/USD). It is the RMB value of one USD. RMB/
USD goes up (down) when RMB depreciates (appreciates) against the USD. The second
series is the nominal effective exchange rate (NEER), a weighted average of a basket of
foreign currencies per RMB Yuan (hereafter BASKET/RMB). The BASKET/RMB goes up
(down) when RMB appreciate (depreciate). The RMB/USD exchange rate data are released
by the PBOC and are available through WIND database, while the BASKET/RMB series
were obtained from Bank for International Settlements (BIS). Figure 2 plots the time series
trend of RMB/USD and BASKET/RMB, which is the RMB NEER, from July 31, 1995, to
June 30, 2015. The figure shows that RMB has appreciated over the USD and other major
currencies in the last 10 years. Notably, the RMB exchange rate series measured in both
USD and basket of currencies have become very volatile since 2010.
46 Journal of Accounting, Auditing & Finance
For the exchange rate risk transmission channels, we construct relevant macro time
series variables that proxy for the factors discussed in the previous section.11 TRADE is
the monthly percentage changes of total values of exports and imports and is a macroeco-
nomic variable that captures China’s international trade. Hot money (HM) is the difference
between the change in foreign-exchange reserves and the balance of trade and service
minus the FDI. It captures the capital flows unrelated to FDI. Finally, we use M2 broad
money supply to proxy for domestic credit supply.12 The data sources involved in the con-
struction of the above variables are from the National Bureau of Statistics, National
Development and Reform Commission, State Administration of Foreign Exchange,
Bloomberg, and Wind Database.
Table 1 reports descriptive statistics on the percentage change of the two exchange rate
series and proxies for the three transmission channels: percentage changes of international
trade (TRADE), hot money (HM), and broad money supply (M2). The mean percentage
change of the RMB/USD exchange rate is negative, suggesting an appreciation of RMB
against the USD over the sample period. Likewise, the positive average percentage change
in BASKET/RMB also suggests an RMB appreciation against a basket of currencies.
Empirical Results
Industry Exposure to Exchange Rate Movements
Exchange rate exposure is often examined at the industry level because an industry in one
country often competes with the same industry in another country. Industries involved in
extensive international trade are more often exposed to exchange rate risk than industries
with low levels of international trade (Priestley & Ødegaard, 2007). To examine the indus-
try exposure to foreign-exchange rate risk in China, we conduct a regression of 18 industry
returns on the monthly changes of exchange rates. The equation is as follows:
Figure 2. The trend of RMB/USD and BASKET/RMB.
Note. The figure shows the trend of the exchange rates measured by RMB/USD (USD in the figure, right scale),
which is the nominal RMB exchange rate against USD, and by BASKET/RMB (NEER in the figure, left scale), which is
the RMB NEER, from July 31, 1995 to June 30, 2015. NEER = nominal effective exchange rate.
Hua et al. 47
rit =ji + giXt + et, ð1Þ
where rit represents 18 value-weighted average industry returns (i = 1, 2, . . . , 18), and Xt is
the percentage change of exchange rates. We use RMB/USD and BASKET/RMB, respec-
tively, as Xt to measure the exchange rate shocks. The estimated exposure coefficient gi
represents the degree of the impact of exchange rate movement on industry stock returns.
For BASKET/RMB, we split the sample into two periods before and after July 2005 to cap-
ture the impact of exchange rate regime change in China. We expect that RMB apprecia-
tion would decrease the stock returns due to its negative impact on exports. Because the
values of the BASKET/RMB (RMB/USD) exchange rates will increase (decrease) when
RMB appreciates, the exposure coefficients gis of BASKET/RMB (RMB/USD) should be
negative (positive).
Table 2 reports the estimation results on Equation 1. The degree of sensitivity to
exchange rate change is measured by coefficient gi. There are a couple of noteworthy
results. First, stock returns at industry level are not sensitive to exchange rate movement
(measured by BASKET/RMB) prior to July 2005 when China adopted a regime of strictly
pegging the RMB to the USD at an exchange rate of 8.28. However, after the exchange
regime was changed to a controlled floating rate pegging to a trade-weighted ‘‘reference
basket’’ of currency, the time series exchange rate movement is significantly correlated
with most of industry return series. The sign is consistent with our prediction, negative for
BASKET/RMB and positive for RMB/USD, suggesting that Chinese currency appreciation
(or depreciation) exerts a negative (or positive) effect on stock returns. Second, for the
USD-based exchange rate series (RMB/USD), the significant sensitivity to exchange rate
movement exists for some industries such as mining, manufacturing, construction, finance,
and other industries. These industries typically have high ratio of export. The results sug-
gest that there are industry variations in exposure to foreign-exchange rate movement, per-
haps due to different industry’s sensitivity to the exchange rate risk transmission channels.
Price Reactions of ‘‘A’’ Shares to Announcement of Exchange Rate Reform
Prior studies suggest that stock prices should fall in response to increasing domestic cur-
rency value and vice versa because currency appreciation may deteriorate the terms of
Table 1. Descriptive Statistics of Percentage Changes in Exchange Rates and Economic Transmission
Variables.
BASKET/RMB, RMB/USD, TRADE, HM, and M2 represent monthly percentage change of RMB nominal
effective exchange rate, nominal RMB exchange rate against the USD, international trade (TRADE), hot
money (HM), and broad money supply (M2), respectively. For the BASKET/RMB, the sample starts
from July 1995, whereas for the USD, the sample starts from July 2005. The sample for
macroeconomic variables starts in July 1995. All variables end at June 2015.
M Median SD Sample variance Kurtosis Skewness
BASKET/RMB 0.0024 0.0029 0.0124 0.0002 1.2416 0.1470
RMB/USD –0.0025 –0.0018 0.0037 0.0000 2.0636 –1.3232
TRADE 0.0219 0.0161 0.1487 0.0221 2.4801 0.3476
HM –0.2657 –0.2094 2.8734 8.2564 24.3162 1.9056
M2 0.0134 0.0123 0.0131 0.0002 8.4673 0.9010
48 Journal of Accounting, Auditing & Finance
trade while currency depreciation may enhance the terms of trade, particularly for indus-
tries generating revenue from export (e.g., Dornbusch & Fischer, 1980; Pavlova &
Rigobon, 2007). China has been under external pressure to appreciate its currency value,
and the ongoing reform to increase the exchange rate flexibility has generally raised the
expectation on currency appreciation. Therefore, the announcement of exchange rate
reform provides an opportunity to study the valuation effect of the exchange rate risk expo-
sure. To that end, we examine stock market reactions to news announcements about
Table 2. Sensitivity of Industry Returns to Exchange Rate Movements.
The numbers shown in the panel are estimated gis from the following regression:
rit =ji + giXt + et,
where rit represents industry portfolio returns (industries are classified by China Securities Regulatory
Commission. The Civil Services, Maintenance, and Other Industry are omitted due to lack of data).
The value-weighted industry portfolios are formed at the end of June of each year, t. Companies with
less than 4 months prices are omitted from that year. Xt is the percentage change of nominal effective
exchange rate (BASKET/RMB), which is a weighted average of a basket of foreign currencies per RMB
Yuan, and nominal RMB versus USD spot exchange rate (RMB/USD), which is the RMB value of every
USD.
BASKET/RMB
(July 1995-June 2005)
BASKET/RMB
(July 2005-June 2015)
RMB/USD
(July 2005-June 2015)
Agri. 0.60 (0.73) –1.86 (–1.99**) 2.91 (1.09)
Min. 0.45 (0.48) –2.57 (–3.43***) 4.43 (2.02**)
Manuf. 0.47 (0.79) –2.37 (–3.08***) 4.24 (1.90*)
Util. 0.53 (0.89) –1.18 (–1.63) 3.13 (1.52)
Con. 0.87 (1.29) –1.29 (–1.53) 5.67 (2.40**)
Who.&Ret. 0.70 (1.18) –2.45 (–3.08***) 3.37 (1.45)
Trans. 0.46 (0.77) –1.58 (–1.98**) 3.52 (1.54)
Hot.&Rest. 0.51 (0.60) –3.17 (–3.56***) 3.43 (1.30)
I.T. 0.71 (0.92) –2.53 (–3.05***) 3.50 (1.45)
Fin. 0.14 (0.20) –1.93 (–2.73***) 3.78 (1.85*)
Real Est. 0.38 (0.56) –2.31 (–2.90***) 3.37 (1.45)
Rent.&Bus. 0.04 (0.06) –2.82 (–3.50***) 3.61 (1.52)
Scie.&Tech. 1.08 (1.23) –1.70 (–1.62) 1.81 (0.60)
Envi. 0.96 (0.89) –2.29 (–2.91**) 3.49 (1.53)
Edu. –0.31 (–0.39) –2.16 (–1.87*) 3.58 (1.08)
Health 0.55 (0.77) –1.99 (–1.82*) 3.41 (1.09)
Enter. 0.56 (0.61) –2.58 (–2.93***) 3.26 (1.27)
Gen. –0.14 (–0.18) –2.59 (–3.09***) 4.62 (1.89*)
Note. Numbers in parentheses are t statistics. For the BASKET/RMB case, the sample breaks into two parts at July
2005. The industries include Agriculture, Forestry, Stockbreeding, and Fishing (Agri.); Mining (Min.); Manufacturing
(Manuf.); Utility (Util.) including Electricity, Heating, Gas, and Water; Construction (Con.); Whole sale and Retail
(Who.&Ret.); Transportation, Storage, and Post (Trans.); Hotel and Restaurant (Hot.&Rest.); Information Technology
(I.T.); Financial Institutions (Fin.); Real Estate (Real Est.); Renting and Business Services (Rent.&Bus.); Science
Research and Technology Services (Scie.&Tech.); Water Conservancy, Environment, and Public Facilities (Envi.);
Education (Edu.); Health and Social Work (Health); Culture, Sport, and Entertainment (Enter.); and Other Industries
(Gen.). For Agriculture, Forestry, Stockbreeding, and Fishing (Agri.), the sample starts from July 1997 and for Health
and Social Work (Health) the sample starts from July 1996, due to limited number of companies. Other industries
starts from July 1995 and all series end at June 2015.
*denotes statistical significance at 10% significance level. **denotes statistical significance at 5% significance level.
***denotes statistical significance at 1% significance level.
Hua et al. 49
exchange rate reform on July 21, 2005, and June 19, 2010.13 We use a standard event
study methodology (Brown & Warner, 1985; Mazouz, Joseph, & Joulmer, 2009). We
hypothesize that because of expectation of currency appreciation following exchange rate
regime change, the cumulative abnormal returns (CAR) of companies with export revenue
in sales are more adversely affected by RMB regime reform news than companies that do
not have export revenue in business activities. We estimate the abnormal returns (ARjt) by
applying the following equation:
ARjt =Rjt � âj � b̂jRmt, ð2Þ
where Rjt is the return of company j at day t, and the estimated coefficients âj and b̂j are
from the standard market model for an estimation window (–260, –61) interval prior to the
event (t = 0):
Rjt = aj + bjRmt + ejt, ð3Þ
where Rmt is the returns on Shanghai or Shenzhen composite indexes depending on where
security j was listed. The CAR for each security j, CARj, is the sum of average abnormal
returns over the event period. The formula is as follows:
CARj, T1, T2
=
XT2
t = T1
ARjt, ð4Þ
where CARj, T1, T2
is the CAR during the period from t = day T1 to t = day T2. We report
six event windows, namely [–1,0], [–3,+3], [–5,+5], [–6,+6], [–11,+11], and [–21,+21]. We
examine the different reactions to news announcements for either export or nonexport
companies.14
Table 3 shows that the average CARs for export and nonexport companies over six win-
dows for two event days. Average CARs of both export and nonexport companies are nega-
tive for both event days, suggesting that the news announcement of exchange rate reform
caused expectation of RMB appreciation and triggered negative stock market reaction on
average. This shows that the expectation of currency appreciation has a negative valuation
effect on the equity market, reflecting the fact that China has an export-oriented economy.
In fact, the results show that the export companies have a stronger negative market reaction
than nonexport companies. For the announcement day of July 21, 2005, the CARs for
export portfolios are significantly negative for all six windows at either the 1% or 5% sig-
nificance level, while those of nonexport portfolios are not significant for some event win-
dows such as [–3,+3] and [–6,+6]. Furthermore, the magnitude of the negative CARs of
export portfolios are larger than those of the nonexport portfolio and the differences in
CARs between export portfolios and nonexport portfolios are significantly negative at the
1% level except for the event window of [–1,0]. For the event date of June 19, 2010, we
observed similar patterns and in most of the event windows the negative CARs of both
export and nonexport portfolios are significantly different from zero. Again, the differences
in CARs between export and nonexport portfolios are statistically significant across all six
event windows. The results thus show that companies with export revenue in sales are
more adversely affected by the news of Chinese exchange rate regime change, which
50 Journal of Accounting, Auditing & Finance
effectively caused the RMB appreciation process. This implies that international trade is an
important channel for transmitting exchange rate risk to asset returns.
An Examination of Price Reaction of ‘‘H’’ Shares
Some Chinese stocks are listed in Hong Kong as ‘‘H’’ shares with share prices denomi-
nated in Hong Kong dollars. Because the Hong Kong dollar remained pegged to the USD
during the sample period of our study, it is possible that Chinese shares listed on the Hong
Kong stock exchange may respond to exchange rate regime change in China in a different
way from what we observed for the mainland China shares. Thus, a study of ‘‘H’’ shares
can offer further evidence on whether the difference in sensitivities to RMB regime change
may cause a different price reaction.15 To that end, we examine the stock price reaction to
announcement of RMB exchange rate reform for ‘‘H’’ shares. In particular, we compare
the differences in CARs between ‘‘H’’ shares that have corresponding ‘‘A’’ shares traded
simultaneously in China and ‘‘H’’ shares that have no corresponding ‘‘A’’ shares. In addi-
tion, we also compare the CARs between ‘‘H’’ shares and their corresponding ‘‘A’’ shares.
We use Hang Seng China Enterprises Index as the market index for ‘‘H’’ shares.
Table 4 reports the average CARs over six windows around the announcement day on
July 21, 2005. ‘‘H with A’’ (‘‘H without A’’) refers to ‘‘H’’ shares that have (that have no)
corresponding ‘‘A’’ share companies listed on stock exchange in mainland China, respec-
tively. ‘‘A-H’’ shares refer to ‘‘A’’ shares that have ‘‘H’’ shares simultaneously cross-listed
on Hong Kong Stock Exchange. ‘‘Mean Comparison’’ reports the mean differences
between CARs. The left panel shows that for the ‘‘H with A’’ shares, the stock price has a
Table 3. Price Reaction of ‘‘A’’ Shares to Announcements of Exchange Rate Reform.
Event
window Export Nonexport
Mean
comparison
Event
window Export Nonexport
Mean
comparison
Announcement day: July 21, 2005 Announcement day: June 19, 2010
[–1,0] –0.0030 –0.0023 –0.0007 [–1,0] –0.0044 –0.0017 –0.0028
(–2.19**) (–2.15**) (–0.53) (–3.40***) (–1.45) (–2.93***)
[–3,+3] –0.0100 –0.0013 –0.0087 [–3,+3] –0.0239 –0.0156 –0.0083
(–3.96***) (–0.66) (–4.31***) (–9.82***) (–7.25***) (–4.66***)
[–5,+5] –0.0180 –0.0044 –0.0136 [–5,+5] –0.0208 –0.0121 –0.0088
(–5.68***) (–1.76*) (–4.92***) (–6.82***) (–4.47***) (–4.14***)
[–6,+6] –0.0121 –0.0012 –0.0110 [–6,+6] –0.0326 –0.0243 –0.0083
(–3.51***) (–0.43) (–3.85***) (–9.81***) (–8.27***) (–3.30***)
[–11,+11] –0.0311 –0.0201 –0.0109 [–11,+11] –0.0559 –0.0427 –0.0132
(–6.67***) (–5.57***) (–3.17***) (–12.65***) (–10.92***) (–3.58***)
[–21,+21] –0.0448 –0.0309 –0.0139 [–21,+21] –0.0593 –0.0361 –0.0232
(–7.13***) (–6.26***) (–3.17***) (–9.82***) (–6.76***) (–3.73***)
Note. This table shows the average cumulative abnormal returns (CAR) for export and nonexport ‘‘A’’ share
companies over six windows for two event days in which the Chinese government announced exchange rate
regime reform news. Export (nonexport) companies refer to companies with (without) export revenues at the
event year. Mean Comparison reports the mean differences between CARs from export and nonexport companies.
The t statistics are shown in the parentheses.
*denotes statistical significance at 10% significance level. **denotes statistical significance at 5% significance level.
***denotes statistical significance at 1% significance level.
Hua et al. 51
T
a
b
le
4
.
P
ri
ce
R
ea
ct
io
n
o
f
‘‘H
’’
Sh
ar
es
to
A
n
n
o
u
n
ce
m
en
ts
o
f
E
x
ch
an
ge
R
at
e
R
ef
o
rm
.
E
ve
n
t
w
in
d
o
w
H
w
it
h
A
(1
)
H
w
it
h
o
u
t
A
(2
)
M
ea
n
co
m
p
ar
is
o
n
(1
)
–
(2
)
A
-H (3
)
M
ea
n
co
m
p
ar
is
o
n
(1
)
–
(3
)
E
ve
n
t
w
in
d
o
w
H
w
it
h
A
(4
)
H
w
it
h
o
ut
A
(5
)
M
ea
n
co
m
p
ar
is
o
n
(4
)
–
(5
)
A
-H (6
)
M
ea
n
co
m
p
ar
is
o
n
(4
)
–
(6
)
A
n
n
o
u
n
ce
m
en
t
d
ay
:
Ju
ly
2
1,
2
0
0
5
A
n
no
u
n
ce
m
en
t
d
ay
:J
u
n
e
1
9,
2
01
0
[–
1,
0
]
0
.1
7
86
–
0.
0
6
4
3
0
.2
42
9
–
0
.0
00
0
8
0
.1
7
87
[–
1
,0
]
–
0
.0
00
7
–
0
.0
07
0
0
.0
0
64
0
.0
0
00
3
0
.0
00
7
(2
.1
1*
*
)
(–
0
.7
7
)
(1
.9
2*
)
(–
0
.0
3
)
(2
.2
7
*
*
)
(–
0
.0
3
)
(–
0
.1
8
)
(0
.1
5)
(0
.0
2
)
(0
.0
3)
[–
3,
+
3
]
0
.4
0
11
0
.1
4
36
0
.2
57
5
0
.0
06
1
0
.3
9
50
[–
3
,+
3
]
–
0
.0
83
7
–
0
.0
13
0
–
0
.0
70
7
–
0.
0
0
4
1
0
.0
79
6
(4
.1
8*
*
*
)
(1
.3
7
)
(1
.8
0*
)
(1
.1
2)
(3
.9
3
*
*
*
)
(–
1
.0
8
)
(–
0
.1
7
)
(–
0
.6
6
)
(–
1
.1
2
)
(1
.0
5)
[–
5,
+
5
]
0
.4
4
49
0
.0
5
74
0
.3
87
5
0
.0
15
7
0
.4
2
92
[–
5
,+
5
]
–
0
.1
24
4
0
.0
8
41
–
0
.2
08
5
–
0.
0
0
4
2
0
.1
20
2
(3
.1
3*
*
*
)
(0
.5
2
)
(2
.0
9*
*
)
(2
.1
6*
*
)
(2
.8
8
*
*
*
)
(–
1
.1
9
)
(0
.8
8)
(–
1
.4
9
)
(–
1
.0
3
)
(1
.1
8)
[–
6,
+
6
]
0
.4
8
49
0
.0
3
74
0
.4
47
5
0
.0
14
9
0
.4
7
00
[–
6
,+
6
]
–
0
.2
91
0
0
.0
5
74
–
0
.3
48
4
–
0.
0
0
7
8
0
.2
83
2
(3
.0
4*
*
*
)
(0
.3
5
)
(2
.2
8*
*
)
(2
.0
7*
*
)
(2
.8
2
*
*
*
)
(–
2
.7
2
*
*
)
(0
.5
4)
(–
2
.3
5
*
*
)
(–
1
.4
8
)
(2
.7
3*
*
)
[–
11
,+
1
1
]
0
.5
8
97
0
.0
9
24
0
.4
97
3
0
.0
18
1
0
.5
7
10
[–
1
1
,+
11
]
–
0
.0
99
5
0
.0
7
18
–
0
.1
71
3
–
0.
0
1
4
1
0
.0
85
4
(1
.9
6*
)
(0
.7
7
)
(1
.5
0)
(2
.1
6*
*
)
(1
.8
0
*
)
(–
0
.7
1
)
(0
.3
1)
(–
0
.8
6
)
(–
1
.7
4
*
)
(0
.6
3)
[–
21
,+
2
1
]
1
.2
4
33
0
.0
5
74
1
.1
86
0
0
.0
07
2
1
.2
3
61
[–
2
1
,+
21
]
–
0
.1
86
5
–
0
.0
74
1
–
0
.1
12
4
–
0.
0
1
9
8
0
.1
66
7
(2
.3
2*
*
)
(0
.4
0
)
(2
.0
3*
*
)
(0
.7
5)
(2
.1
9
*
*
)
(–
0
.9
4
)
(–
0
.4
2
)
(0
.4
3)
(–
1
.6
0
)
(0
.8
6)
N
ot
e.
T
h
is
ta
b
le
sh
o
w
s
th
e
av
er
ag
e
cu
m
u
la
ti
ve
ab
n
o
rm
al
re
tu
rn
s
(C
A
R
)
fo
r
‘‘H
’’
sh
ar
es
an
d
th
ei
r
co
rr
es
p
o
n
d
in
g
‘‘A
’’
sh
ar
es
co
m
p
an
ie
s,
o
ve
r
si
x
w
in
d
o
w
s
fo
r
tw
o
ev
en
t
d
ay
s
in
w
h
ic
h
th
e
C
h
in
es
e
go
ve
rn
m
en
t
an
n
o
u
n
ce
d
ex
ch
an
ge
ra
te
re
gi
m
e
re
fo
rm
n
ew
s.
‘‘H
w
it
h
A
’’
(‘
‘H
w
it
h
o
u
t
A
’’)
re
fe
r
to
‘‘H
’’
sh
ar
es
th
at
h
av
e
(t
h
at
h
av
e
n
o
)
co
rr
es
p
o
n
d
in
g
‘‘A
’’
sh
ar
e
co
m
p
an
ie
s
lis
te
d
o
n
st
o
ck
ex
ch
an
ge
in
m
ai
n
la
n
d
C
h
in
a,
re
sp
ec
ti
ve
ly
.
T
h
e
sa
m
p
le
si
ze
fo
r
‘‘H
w
it
h
A
’’
is
4
8
(f
o
r
‘‘H
w
it
h
o
u
t
A
’’
is
4
6
)
in
2
0
0
5
an
d
it
is
7
1
fo
r
‘‘H
w
it
h
A
’’
(7
3
‘‘H
w
it
h
o
u
t
A
’’)
in
2
0
1
0.
‘‘A
-H
’’
sh
ar
es
re
fe
r
to
‘‘A
’’
sh
ar
es
th
at
h
av
e
‘‘H
’’
sh
ar
es
si
m
u
lt
an
eo
u
sl
y
cr
o
ss
-l
is
te
d
o
n
H
o
n
g
K
o
n
g
St
o
ck
E
x
ch
an
ge
.
‘‘M
ea
n
C
o
m
p
ar
is
o
n
’’
re
p
o
rt
s
th
e
m
ea
n
d
iff
er
en
ce
s
b
et
w
ee
n
C
A
R
s.
T
h
e
t
st
at
is
ti
cs
ar
e
sh
o
w
n
in
th
e
p
ar
en
th
es
es
.
*
d
en
o
te
s
st
at
is
ti
ca
l
si
gn
ifi
ca
n
ce
at
1
0
%
si
gn
ifi
ca
n
ce
le
ve
l.
*
*
d
en
o
te
s
st
at
is
ti
ca
l
si
gn
ifi
ca
n
ce
at
5
%
si
gn
ifi
ca
n
ce
le
ve
l.
*
*
*
d
en
o
te
s
st
at
is
ti
ca
l
si
gn
ifi
ca
n
ce
at
1
%
si
gn
ifi
ca
n
ce
le
ve
l.
52
positive CAR up to 6 days around the announcement day on July 21, 2005. By contrast, the
market reaction for ‘‘H’’ shares without domestic ‘‘A’’ shares is not significant for any of
the windows examined. The results imply that for investors in ‘‘H’’ shares with the fungi-
ble ‘‘A’’ shares traded on domestic Chinese markets, an expectation of RMB appreciation
against the USD could lead to an increase in expected value of ‘‘H’’ shares in Hong Kong
dollars, because Hong Kong currency remained pegged to USD. On the contrary, the prices
of ‘‘H’’ shares without corresponding ‘‘A’’ shares are not affected by the RMB apprecia-
tion and do not react significantly to the announcement exchange rate regime change.
The same panel also reports the comparison of CARs between ‘‘H’’ shares and their cor-
responding ‘‘A’’ shares. Results suggest that although ‘‘H’’ shares that have corresponding
‘‘A’’ shares experienced significantly positive price reaction, their ‘‘A’’ share counterparts
do not have any significant price reactions over most of windows except for [–5,+5],
[–6,+6], and [–11,11]. On average, the difference in CARs between ‘‘H’’ shares and their
corresponding ‘‘A’’ shares is significantly positive at the 5% level or higher. The difference
in CARs between ‘‘H’’ shares and their corresponding ‘‘A’’ shares suggests that the strictly
pegged currency regime in Hong Kong leads to a different stock price reaction to the
Chinese exchange rate reform for shares cross-listed in Hong Kong and China. The results
in the right panel show that on the event day of June 19, 2010 (announcement of resump-
tion of suspended exchange rate reform), the CARs are generally not significant for ‘‘H’’
shares as well as for the corresponding ‘‘A’’ shares.
Exchange Rate Exposure and Earnings Volatility
The analysis of market reaction in above section shows that investors do expect an increase
in exchange rate exposure following the change from pegged regime to a managed floating
rate. We thus expect that under the managed floating regime, firms with greater foreign-
exchange exposure may experience greater volatility of earnings measure. To examine the
link between exchange rate exposure and volatility of accounting measures, we first group
companies into ‘‘export’’ and ‘‘nonexport’’ stocks. ‘‘Export’’ (nonexport) companies refer
to companies with (without) export revenues each year. As we showed in the article, com-
panies with revenues from export are more sensitive to the change from pegged exchange
rate to a floating rate regime. We thus compute the standard deviation of accounting perfor-
mance and of valuation measures for the two portfolios based on a 3-year rolling window.
The accounting performance measures include earnings scaled by total assets and ROA,
and the valuation measure includes P/E ratio. Three accounting measures are all ratios;
therefore, the standard deviations have the same units.
Table 5 reports the volatilities of accounting measures for each year from 2001 until
2014. We compute the mean differences in three volatilities between ‘‘export’’ and ‘‘non-
export’’ companies for the period before or after exchange rate regime change in 2005. The
results of mean differences test show that for the period after 2005, the volatilities of all
three accounting measures are significantly greater for the ‘‘export’’ companies than for the
‘‘nonexport’’ companies. However, for the period before 2005, the differences between
‘‘nonexport’’ and ‘‘export’’ companies are not significant. This result suggests that the
exchange rate regime change in 2005 led to greater foreign-exchange rate exposure, which
in turn led to greater volatility in earnings, profitability, and P/E ratio for export companies
whose exposure to exchange rate risk is greater.
Hua et al. 53
Pricing of Exchange Rate Risk in Cross-Section of Stock Returns
To test whether the exchange rate risks are priced in stock returns, we use a factor portfolio
approach similar to Kolari et al. (2008) to evaluate whether the constructed exchange rate
risk factor is cross-sectionally related to expected returns and whether the inclusion of the
risk factor reduces the pricing error of existing asset-pricing models. As shown in Kolari
et al. (2008), by constructing a foreign-exchange risk factor in the form of a zero-
investment portfolio that buys stocks sensitive to foreign-exchange rate changes and sells
stocks that are not sensitive to exchange rate changes, the factor portfolio approach over-
comes the weakness of using raw changes in the exchange rate, which may contain infor-
mation irrelevant to asset pricing.
We first measure the extent to which each stock is exposed to foreign-exchange risk.
The foreign-exchange exposure for each firm is measured by regressing its stock returns on
the foreign-exchange return series (Xt) with four other risk factors (Carhart, 1997):
Table 5. Volatility of Firm Performance of Nonexport and Export Companies.
Nonexport Export
Number of
companies
Earnings
volume
ROA
volume
P/E
volume
Number of
companies
Earnings
volume
ROA
volume
P/E
volume
2001 963 0.017 3.138 115.11 115 0.019 3.294 214.82
2002 869 0.016 2.901 124.97 280 0.016 3.485 254.32
2003 807 0.328 1.805 344.09 406 0.327 2.073 210.65
2004 817 0.309 2.083 263.28 492 0.319 2.604 366.87
2005 811 0.330 2.548 677.63 513 0.350 3.241 434.26
Mean comparison –0.006
(–0.06)
–0.44
(–1.23)
8.83
(0.08)
2006 826 0.323 2.553 627.86 560 0.357 3.288 562.93
2007 847 0.321 2.561 510.71 632 0.350 3.166 307.07
2008 875 0.332 3.458 152.89 682 0.369 3.750 369.75
2009 954 0.347 4.160 135.783 765 0.370 8.059 894.99
2010 978 0.314 4.201 245.34 1,051 0.361 10.252 1,294.75
2011 1,087 0.326 3.853 192.84 1,192 0.356 7.342 1,131.68
2012 1,149 0.364 4.088 197.85 1,283 0.375 8.214 975.11
2013 1,155 0.397 5.106 260.29 1,333 0.402 6.568 286.60
2014 1,201 0.405 4.409 314.59 1,390 0.419 4.809 364.68
Mean comparison –0.26
(–1.88*)
–2.34
(–3.35***)
–394.38
(–2.45**)
Note. The table shows average performance volatility of nonexport and export companies. ‘‘nonexport’’ (‘‘export’’)
companies refer to companies without (with) export revenues each year. The data on export revenue for each
company, obtained from annual financial statement, are available from year 2001. Firm performance measures
include earnings, ROA and P/E ratio. Earnings volatility is measured by the standard deviation of earnings to total
assets over the past 3 years. ROA and P/E volatility are measured by the standard deviation of each measure over
the past 3 years. The panels show the number of companies included in each year (note that companies with less
than 4 months prices are eliminated from that year) and cross-sectional mean of volatility of each performance
measure from nonexport and export companies. Mean differences of average volatility of nonexport and export
companies are reported in the row below the subsample period. The t statistics of mean comparison tests are
reported in the parentheses.
*denotes statistical significance at 10% significance level. **denotes statistical significance at 5% significance level.
***denotes statistical significance at 1% significance level.
54 Journal of Accounting, Auditing & Finance
rnt � Rft
� �
= an + an1 Rmt � Rft
� �
+ an2SMBt + an3HMLt + an4MOMt + an5Xt + ent, ð5Þ
where rnt is the log return on stock n in month t, Rmt is the return on all companies’ value-
weighted returns, Rft is the 1-year deposit interest rate at month t, SMB is the return on a
portfolio of small stocks minus the return on a portfolio of large stocks, HML is the return
on a portfolio of high book-to-market stocks minus the return on a portfolio of low book-
to-market stocks, and MOM is the return on a portfolio of past winner stocks minus a port-
folio of past loser stocks. Portfolios are rebalanced each year at the end of June. Xt is the
percentage change of exchange rates. We use RMB/USD and BASKET/RMB, respectively,
as Xt to measure exchange rate risks. The coefficient an5 is a proxy for estimated exposure
to foreign-exchange risk.
We obtain annual foreign-exchange risk exposure coefficients for each stock by estimat-
ing Equation 5 using monthly data over 2-year rolling windows beginning in July each
year.16 Each year, we then rank firms into 25 portfolios based on the value of the firm-
specific exposure coefficient an5.17 Following Kolari et al. (2008), the portfolios ranked 1st
and 25th (i.e., portfolios with the highest foreign-exchange sensitivity in absolute value)
are defined as the sensitive portfolio and those ranked from the 2nd to 24th are grouped as
insensitive portfolios. We construct the exchange rate risk factor, XMI, by creating a portfo-
lio that buys stocks with extreme negative or positive sensitivity to foreign-exchange risk
(portfolios ranked 1st and 25th) and sells all other stocks (portfolios ranked 2nd to 24th).
We evaluate whether the exchange rate is a priced factor by including XMI as an additional
factor into both the Fama-French three-factor model (Fama & French, 1993) and the Fama-
French–Carhart four-factor model and examining the change in pricing errors.18 In particu-
lar, we regress the excess returns on 25 portfolios sorted by the firm-specific exposure coef-
ficient an5 against the Fama-French three- or four-factor (Carhart) models plus the
exchange rate risk factor XMI:
Rit � Rft
� �
= ai + bi1 Rmt � Rft
� �
+ bi2SMBt + bi3HMLt + bi4MOMt + bi5XMIt + eit, ð6Þ
Rit � Rft
� �
= ai + bi1 Rmt � Rft
� �
+ bi2SMBt + bi3HMLt + bi4XMIt + eit, ð7Þ
where Rit represents value-weighted portfolio returns (portfolios from 1 to 25) at month t.
As in Kolari et al. (2008), we argue that the exchange rate risk factor XMI is a priced
factor in stock returns if the average of absolute ai or squaredai (the pricing error) signifi-
cantly decreases in Equation 6 or 7 compared with the base models without XMI. We for-
mally conduct a Gibbons–Ross–Shanken test (Gibbons, Ross, & Shanken, 1989, GRS test
hereafter) with the null hypothesis that model’s intercepts are jointly equal to zero for all
25 portfolios.
Table 6 shows the average of coefficient an5, size (logarithmic), and average annual raw
returns on the 25 portfolios formed on foreign-exchange sensitivity, where the exchange
rates are measured by the RMB/USD and BASKET/RMB, respectively. Portfolios ranked 1st
and 25th are those with the highest foreign-exchange sensitivity in absolute value. We first
calculate a cross-sectional average raw return of each of the 25 portfolios each year. We
then compute the intertemporal average of annual portfolio returns. All returns are annual-
ized. In the last row of each panel, we report the differences between average annual raw
returns of foreign-exchange insensitive portfolios (2U . . . 24) and sensitive portfolios
(1U25).
Hua et al. 55
T
a
b
le
6
.
Si
ze
s
an
d
R
aw
R
et
u
rn
s
o
f
2
5
Po
rt
fo
lio
s
Fo
rm
ed
o
n
Fo
re
ig
n
-E
x
ch
an
ge
Se
n
si
ti
vi
ty
.
Fo
r
th
e
p
er
io
d
Ju
ly
1
9
9
7
to
Ju
n
e
2
0
1
5
(f
ro
m
2
0
0
7
fo
r
th
e
R
M
B
/U
SD
ca
se
),
2
5
p
o
rt
fo
lio
s
ar
e
fo
rm
ed
at
en
d
o
f
Ju
n
e
o
f
ea
ch
ye
ar
,
t,
b
as
ed
o
n
th
e
se
n
si
ti
vi
ty
o
f
in
d
iv
id
u
al
fir
m
s
to
ch
an
ge
s
in
ex
ch
an
ge
ra
te
s
(R
M
B
/U
SD
o
r
B
A
SK
E
T
/R
M
B
).
Se
n
si
ti
vi
ty
is
m
ea
su
re
d
b
y
th
e
co
ef
fic
ie
n
t
o
n
X
t
in
th
e
fo
llo
w
in
g
re
gr
es
si
o
n
:
r n
t
�
R
ft
�
� =
a
n
+
a
n1
R
m
t
�
R
ft
�
� +
a
n2
SM
B
t
+
a
n3
H
M
L t
+
a
n4
M
O
M
t
+
a
n5
X
t
+
e n
t,
ab
o
ve
eq
u
at
io
n
is
es
ti
m
at
ed
w
it
h
d
at
a
fr
o
m
1
9
9
5
(2
0
0
5
fo
r
th
e
R
M
B
/U
SD
ca
se
)
th
ro
u
gh
2
0
1
5
o
n
a
2
-y
ea
r
ro
lli
n
g
w
in
d
o
w
.
T
h
e
r n
t
is
th
e
lo
g
re
tu
rn
o
n
st
o
ck
n
in
m
o
n
th
t.
R
m
t
is
th
e
va
lu
e-
w
ei
gh
te
d
av
er
ag
e
re
tu
rn
o
f
al
l
co
m
p
an
ie
s
in
th
e
sa
m
p
le
.
R
ft
is
th
e
1
-y
ea
r
d
ep
o
si
t
in
te
re
st
ra
te
in
m
o
n
th
t.
SM
B
is
th
e
re
tu
rn
o
n
a
p
o
rt
fo
lio
o
f
sm
al
l
st
o
ck
s
m
in
u
s
th
e
re
tu
rn
o
n
a
p
o
rt
fo
lio
o
f
la
rg
e
st
o
ck
s.
H
M
L
is
th
e
re
tu
rn
o
n
a
p
o
rt
fo
lio
o
f
h
ig
h
b
o
o
k-
to
-m
ar
ke
t
st
o
ck
s
m
in
u
s
th
e
re
tu
rn
o
n
a
p
o
rt
fo
lio
o
f
lo
w
b
o
o
k-
to
-m
ar
ke
t
st
o
ck
s.
M
O
M
is
th
e
re
tu
rn
o
n
a
p
o
rt
fo
lio
o
f
p
as
t
w
in
n
er
st
o
ck
s
m
in
u
s
a
p
o
rt
fo
lio
o
f
p
as
t
lo
se
r
st
o
ck
s.
X
t
is
th
e
p
er
ce
n
ta
ge
ch
an
ge
o
f
ex
ch
an
ge
ra
te
s
(R
M
B
/U
SD
o
r
B
A
SK
E
T
/R
M
B
).
T
h
e
co
ef
fic
ie
n
t
a
n5
o
n
X
t
is
th
u
s
es
ti
m
at
ed
o
ve
r
a
2
-y
ea
r
ro
lli
n
g
p
er
io
d
.
Si
ze
is
th
e
lo
ga
ri
th
m
o
f
m
ar
ke
t
va
lu
e
at
th
e
en
d
o
f
Ju
n
e
o
f
ea
ch
ye
ar
,
t.
Po
rt
fo
lio
re
tu
rn
s
ar
e
fr
o
m
Ju
ly
o
f
ye
ar
t
to
Ju
n
e
o
f
ye
ar
t
+
1
.
A
n
n
u
al
re
tu
rn
is
o
b
ta
in
ed
b
y
m
u
lt
ip
ly
in
g
th
e
va
lu
e-
w
ei
gh
te
d
av
er
ag
e
m
o
n
th
ly
re
tu
rn
s
o
f
th
e
p
o
rt
fo
lio
s
b
y
1
2
.
Fi
rm
m
o
nt
h
C
o
ef
fic
ie
n
t
o
n
X
(a
n5
)
Si
ze
A
ve
ra
ge
an
n
u
al
re
tu
rn
R
M
B
/U
SD
B
A
SK
E
T
/R
M
B
R
M
B
/U
SD
B
A
SK
E
T
/R
M
B
R
M
B
/U
SD
B
A
SK
E
T
/R
M
B
R
M
B
/U
SD
B
A
SK
E
T
/R
M
B
Ju
ly
1
99
7
-J
u
n
e
2
00
6
1
—
4
,6
4
4
—
–
4
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4
—
2
0
.7
0
—
–
0.
0
8
8
2
—
4
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0
8
—
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2
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7
—
2
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4
—
–
0.
0
1
6
3
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8
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0
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6
4
—
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0
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0
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1
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0
8
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0
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6
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2
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2
—
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0.
0
2
7
1
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4
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2
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0
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2
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0.
0
2
2
1
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2
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6
—
–
0.
0
1
9
(c
on
tin
ue
d)
56
T
a
b
le
6
.
(c
o
n
ti
nu
ed
)
Fi
rm
m
o
nt
h
C
o
ef
fic
ie
n
t
o
n
X
(a
n5
)
Si
ze
A
ve
ra
ge
an
n
u
al
re
tu
rn
R
M
B
/U
SD
B
A
SK
E
T
/R
M
B
R
M
B
/U
SD
B
A
SK
E
T
/R
M
B
R
M
B
/U
SD
B
A
SK
E
T
/R
M
B
R
M
B
/U
SD
B
A
SK
E
T
/R
M
B
1
8
—
4
,6
0
8
—
0
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2
—
2
0
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4
—
0
.0
0
9
1
9
—
4
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2
0
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0
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0
—
2
0
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0
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0
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0
7
2
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0
8
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1
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0
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0
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7
—
0
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2
2
2
1
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5
Z
er
o
in
ve
st
m
en
t
p
o
rt
fo
lio
(2
U
..
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4
)
–
(1
U
2
5)
0
.0
3
5
(1
.0
8
)
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ly
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00
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n
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01
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3
2
2
.8
2
0
.0
5
0
0
.0
5
9
1
5
6
,7
2
0
7
,1
1
6
5
.9
3
0
.5
4
2
1.
6
8
2
1
.9
7
0
.0
7
7
0
.0
0
4
1
6
6
,7
2
0
7
,1
0
4
6
.8
1
0
.7
9
2
2.
7
4
2
2
.1
1
0
.0
1
2
0
.0
0
8
1
7
6
,7
2
0
7
,1
1
6
7
.5
8
1
.0
3
2
1.
5
9
2
2
.1
0
0
.0
3
5
0
.0
1
4
1
8
6
,7
2
0
7
,1
0
4
8
.3
6
1
.2
9
2
1.
7
5
2
1
.6
7
0
.0
6
2
0
.0
7
9
1
9
6
,7
4
4
7
,1
1
6
9
.2
1
1
.5
8
2
1.
9
4
2
2
.0
9
–
0.
0
0
1
–
0.
0
1
3
(c
on
tin
ue
d)
57
T
a
b
le
6
.
(c
o
n
ti
nu
ed
)
Fi
rm
m
o
nt
h
C
o
ef
fic
ie
n
t
o
n
X
(a
n5
)
Si
ze
A
ve
ra
ge
an
n
u
al
re
tu
rn
R
M
B
/U
SD
B
A
SK
E
T
/R
M
B
R
M
B
/U
SD
B
A
SK
E
T
/R
M
B
R
M
B
/U
SD
B
A
SK
E
T
/R
M
B
R
M
B
/U
SD
B
A
SK
E
T
/R
M
B
2
0
6
,7
2
0
7
,1
2
8
1
0
.2
4
1
.9
0
2
1.
6
1
2
1
.8
8
0
.0
1
0
0
.0
5
5
2
1
6
,7
2
0
7
,1
0
4
1
1
.4
0
2
.2
7
2
1.
8
2
2
1
.7
6
0
.0
1
7
–
0.
0
0
7
2
2
6
,7
3
2
7
,1
1
6
1
2
.7
4
2
.7
4
2
1.
9
8
2
1
.8
1
–
0.
0
2
7
0
.0
5
4
2
3
6
,7
2
0
7
,1
2
8
1
4
.4
7
3
.3
4
2
1.
6
4
2
1
.8
1
0
.0
5
3
0
.0
8
6
2
4
6
,7
3
2
7
,1
1
6
1
6
.9
5
4
.1
5
2
2.
0
1
2
1
.7
4
0
.0
2
8
0
.0
2
0
2
5
6
,7
5
6
7
,1
5
2
2
5
.0
3
6
.5
6
2
1.
4
9
2
1
.7
2
0
.0
5
8
0
.0
0
9
Z
er
o
in
ve
st
m
en
t
p
o
rt
fo
lio
(2
U
..
.2
4
)
–
(1
U
2
5)
0
.0
3
1
(–
2
.8
8
*
*
)
0
.0
5
6
(–
3
.3
1
*
*
*
)
N
ot
e.
T
he
ta
b
le
sh
o
w
s
av
er
ag
es
o
f
fir
m
si
ze
,
se
n
si
ti
vi
ty
to
fo
re
ig
n
-e
x
ch
an
ge
ra
te
,
an
d
an
n
u
al
ra
w
re
tu
rn
s
o
f
th
e
2
5
p
o
rt
fo
lio
s
so
rt
ed
o
n
se
n
si
ti
vi
ty
to
fo
re
ig
n
-e
x
ch
an
ge
ra
te
.
Fo
r
B
A
SK
E
T
/R
M
B
ex
ch
an
ge
ra
te
,
th
e
sa
m
p
le
b
re
ak
s
in
to
tw
o
p
er
io
d
s,
th
at
is
,
Ju
ly
1
9
9
7
to
Ju
n
e
2
0
0
6,
an
d
Ju
ly
2
0
0
7
to
Ju
n
e
2
0
1
5.
Fo
r
R
M
B
/U
SD
ex
ch
an
ge
ra
te
,
th
e
sa
m
p
le
is
fr
o
m
Ju
ly
2
0
0
7
to
Ju
n
e
2
0
1
5.
Fi
rm
m
o
nt
h
s
fo
r
ea
ch
p
o
rt
fo
lio
ar
e
al
so
re
p
o
rt
ed
.
T
h
e
ze
ro
-i
nv
es
tm
en
t
p
o
rt
fo
lio
in
th
e
b
o
tt
o
m
ro
w
o
f
ea
ch
p
an
el
is
th
e
va
lu
e-
w
ei
gh
te
d
m
o
n
th
ly
re
tu
rn
o
f
st
o
ck
s
in
Po
rt
fo
lio
s
2
th
ro
u
gh
2
4
m
in
u
s
th
e
va
lu
e-
w
ei
gh
te
d
re
tu
rn
o
f
th
e
st
o
ck
s
in
p
o
rt
fo
lio
s
1
an
d
2
5
.
T
h
e
t
st
at
is
ti
cs
o
f
m
ea
n
co
m
p
ar
is
o
n
te
st
s
o
n
th
e
av
er
ag
e
an
n
u
al
ra
w
re
tu
rn
s
o
f
th
e
ze
ro
-i
nv
es
tm
en
t
p
o
rt
fo
lio
ar
e
re
p
o
rt
ed
in
th
e
la
st
ro
w
in
ea
ch
p
an
el
(s
ee
th
e
la
st
tw
o
co
lu
m
n
s)
.
*
*
d
en
o
te
s
st
at
is
ti
ca
l
si
gn
ifi
ca
n
ce
at
5
%
si
gn
ifi
ca
n
ce
le
ve
l.
*
*
*
d
en
o
te
s
st
at
is
ti
ca
l
si
gn
ifi
ca
n
ce
at
1
%
si
gn
ifi
ca
n
ce
le
ve
l.
58
We divide the whole sample into two subperiods to capture the impact of exchange rate
regime switching. For the risk factors constructed by BASKET/RMB, we report the results
for two subsample periods. The first sample period is from July 1997 to June 2006 and the
second sample period is from July 2007 to June 2015.19 Because the exchange rate was
pegged to the USD prior to 2005, for the exchange rate risk factors measured by RMB/
USD, we report the second time period, namely, after the regime change in 2005. The
results show that for the second time period (July 2007 to June 2015) the average annual
return of the combined portfolio containing the two extreme ranks (1st and 25th) is signifi-
cantly higher than that of the remaining portfolios (2nd to 24th) when the exchange rate is
measured either by RMB/USD (at the 5% level) or by BASKET/RMB (at the 1% level).
However, the difference is not significant for the first sample period (July 1997 to June
2006).
Kolari et al. (2008) found that firms most sensitive to exchange rate fluctuations are on
average smaller. In other words, their exchange rate risk factor has a strong correlation
with the size factor. Lewellen, Nagel, and Shanken (2010) stated that the testing asset is
subject to factor structure if any factors added in the asset pricing model are correlated
with the size (or value) factor. To further examine the relations among risk factors, we
report the correlation of exchange rate risk factors with Fama-French’s three factors and
the momentum factor in Table 7. The strongest negative correlation exists between the
Table 7. Correlation Matrix of Fama-French Factors and Foreign-Exchange Risk Factors.
Panel A rm – rf SMB HML MOM
rm – rf 1.00
SMB 0.10 1.00
HML 0.18 0.06 1.00
MOM –0.12 –0.28 –0.44 1.00
BASKET XMI 0.12 0.12 –0.12 0.33
USD XMI 0.04 0.27 –0.45 0.06
Panel B Mean Median SD Sample variance Kurtosis Skewness
BASKET/RMB XMI (July
1997-June 2015)
–0.0029 (–1.25) –0.0041 0.0338 0.0011 5.2182 –0.6880
BASKET/RMB XMI (July
2007-June 2015)
–0.0029 (–0.66) –0.0051 0.0432 0.0019 3.7901 –0.8259
RMB/USD XMI (July
2007-June 2015)
–0.0004 (–0.09) 0.0006 0.0416 0.0017 7.9155 –1.3923
Note. This table reports the correlation coefficients between foreign-exchange risk factors and Fama-French three
factors and momentum factor. The rm – rf is the market risk premium calculated by the value-weighted average
returns of all companies minus the 1-year deposit interest rate. SMB is the return on a portfolio of small stocks
minus the return on a portfolio of large stocks. HML is the return on a portfolio of high book-to-market stocks
minus the return on a portfolio of low book-to-market stocks. MOM is the return on a portfolio of past winner
stocks minus a portfolio of past loser stocks. BASKET/RMB XMI and RMB/USD XMI are the foreign-exchange risk
factor created by a zero-investment portfolio that takes long positions in portfolios ranked 1st and 25th (highly
sensitive) and short positions in portfolios ranked 2nd to 24th (less or nonsensitive), which are formed at end of
June of each year based on the sensitivity of individual firms to changes in the nominal effective exchange rate
(BASKET/RMB) and nominal RMB exchange rate against USD (RMB/USD). For the BASKET XMI, the sample starts in
July 1997, whereas for the USD XMI, the sample starts in July 2007. Numbers in parentheses below the ‘‘Mean’’
are t statistics. All variables end at June 2015.
Hua et al. 59
exchange rate risk factor measured by RMB/USD with the HML factor, which is –0.45.
Furthermore, the correlation coefficients of exchange rate risk factor with the size factor
(SMB) is 0.12 for BASKET/RMB-based XMI and 0.27 for RMB/USD-based XMI, respec-
tively. These correlation levels should not be subject to the factor structure problem raised
by Lewellen et al. (2010).
In this article, we measure the Chinese exchange rate risk factor (XMI) in two ways:
RMB/USD XMI is the USD risk, which is measured by the RMB exchange rate against the
USD. BASKET/RMB is the ‘‘multicurrency’’ risk, which is measured by the ‘‘reference
basket’’ of currencies. As was discussed in previous section, even though China had
pegged RMB’s value to the dollar until July 2005, there were still fluctuations in exchange
rates between RMB and other currencies. It is sensible to separate the two different risks
and test the ‘‘multicurrency’’ exchange risk before 2005. However, most of the Chinese
export transactions are denominated in USDs and account for at least 20% in the trade-
weighted currency basket index, so it is likely that companies are more exposed to USD
exchange rate movements than other currencies even after China switched to a controlled
floating exchange rate regime that is pegged to a reference basket of currencies since July
2005.
Panel B of Table 7 shows the descriptive statistics of exchange rate risk factors (XMIs)
measured by two different exchange rate series. The mean of RMB/USD-based XMI during
the period of July 2007 to June 2015 is negative, but not significant, with a monthly aver-
age return of –0.04%. The mean of BASKET/RMB XMI is also negative for the overall
sample period as well as the second period of July 2007 to June 2015. However, neither of
them is statistically significant. Because the exchange rate risk factors (XMI) are created by
a zero-investment portfolio that takes long positions in portfolios ranked 1st and 25th and
short positions in portfolios ranked 2nd to 24th, our results are broadly consistent with the
findings in Kolari et al. (2008) for the U.S. market that the zero-investment portfolio
returns are negative.
We next estimate the Fama-French–Carhart four-factor and Fama-French three-factor
models along with the XMI factor as in Equations 6 and 7. If XMI is a priced factor, it
should reduce the mean pricing error of asset pricing models, which is represented by the
absolute or squared value of the intercept. Similar to Kolari et al. (2008), we perform the
GRS test. The null hypothesis is that the models’ intercepts are jointly equal to zero. If the
exchange rate risk is priced, the inclusion of XMI factor should reduce the F statistic or
increase the p value, which indicates that by adding the XMI factor the intercepts of the
model are closer to zero in a statistical sense. In addition, the pricing errors measured by
absolute alphas or squared alphas should reduce with the inclusion of the XMI factor if the
exchange rate risk is priced.
Table 8 reports the results of asset pricing tests using two different XMIs. For the
exchange rate risk factors measured by BASKET/RMB, we report the results for two subper-
iods. The first sample period runs from July 1997 to June 2006 and the second sample
period is from July 2007 to June 2015. We use this setting because the exchange rate
regime change occurred in July 2005 and we require 2 years of data to calculate coeffi-
cients’ loadings on the pricing factors. The model’s intercepts in Equations 6 and 7 are
denoted as alpha. The absolute alpha and squared alpha reported in the table are average
values of 25 estimations.
The results in Table 8 show that the F statistic and alphas reduced markedly in both the
three- and four-factor models for the period from July 2007 to June 2015 when XMI mea-
sured by RMB/USD is included. For example, when XMI measured by RMB/USD is
60 Journal of Accounting, Auditing & Finance
T
a
b
le
8
.
A
ss
et
P
ri
ci
n
g
Te
st
o
f
th
e
Fo
re
ig
n
-E
x
ch
an
ge
R
is
k
Fa
ct
o
r.
X
M
I
m
ea
su
re
d
b
y
B
A
SK
E
T
/R
M
B
(J
u
ly
1
9
9
7
-J
u
ne
2
0
0
6
)
X
M
I
m
ea
su
re
d
b
y
B
A
SK
E
T
/R
M
B
(J
u
ly
2
0
0
7-
Ju
n
e
2
0
1
5)
M
o
d
el
G
R
S
F
st
at
is
ti
c
G
R
S
p
va
lu
e
A
b
so
lu
te
al
p
h
a
Sq
u
ar
ed
al
p
ha
(3
1
0–
6
)
V
ar
ia
n
ce
o
f
al
p
h
a
(3
1
0
–
6
)
G
R
S
F
st
at
is
ti
c
G
R
S
p
va
lu
e
A
b
so
lu
te
al
p
h
a
Sq
u
ar
ed
al
p
h
a
(3
1
0
–
6
)
V
ar
ia
n
ce
o
f
al
p
h
a
(3
1
0
–
6
)
3
fa
ct
o
r
0
.7
1
6
0
.8
6
0
.0
0
17
4
.4
6
4
.5
2
0
.2
42
0
.9
9
0
.0
0
23
6
.8
8
7
.0
9
3
fa
ct
o
r
+
X
M
I
0
.7
1
6
0
.8
6
0
.0
0
17
(0
.5
1)
4
.9
7
(0
.6
7)
5
.0
8
0
.2
38
0
.9
9
0
.0
0
19
(–
1
.8
2
*
)
5
.4
8
(–
1
.6
3
)
5
.6
0
4
fa
ct
o
r
0
.6
9
9
0
.8
8
0
.0
0
17
4
.5
3
4
.5
8
0
.3
02
0
.9
9
0
.0
0
49
1
1.
9
1
2
.4
4
fa
ct
o
r
+
X
M
I
0
.6
8
8
0
.8
9
0
.0
0
18
(0
.5
0)
5
.1
8
(0
.7
4)
5
.2
9
0
.2
90
0
.9
9
0
.0
0
14
(–
1
.7
5
*
)
7
.6
1
(–
1
.2
3
)
7
.6
4
X
M
I
m
ea
su
re
d
b
y
R
M
B
/U
SD
(J
u
ly
2
0
0
7
-J
u
ne
2
0
1
5
)
3
fa
ct
o
r
0
.6
21
0
.9
4
0
.0
0
36
1
6.
6
1
6
.0
3
fa
ct
o
r
+
X
M
I
N
A
0
.6
08
0
.9
4
0
.0
0
28
(–
2
.8
5
*
*
*
)
1
2.
3
(–
2
.4
4
*
*
)
1
1
.4
4
fa
ct
o
r
0
.5
55
0
.9
7
0
.0
0
49
6
2.
7
6
5
.2
4
fa
ct
o
r
+
X
M
I
N
A
0
.5
43
0
.9
7
0
.0
0
22
(–
2
.0
7
*
*
)
1
6.
2
(–
1
.0
6
)
1
5
.2
N
ot
e.
T
h
is
ta
b
le
re
p
o
rt
s
th
e
es
ti
m
at
io
n
re
su
lt
s
fo
r
ad
d
in
g
th
e
fo
re
ig
n
-e
x
ch
an
ge
ri
sk
fa
ct
o
r
in
th
e
Fa
m
a-
Fr
en
ch
th
re
e-
fa
ct
o
r
m
o
d
el
s
an
d
th
e
Fa
m
a-
Fr
en
ch
–
C
ar
h
ar
t
fo
ur
-f
ac
to
r
m
o
d
el
s.
R
it
�
R
ft
�
� =
a
i
+
b
i1
R
m
t
�
R
ft
�
� +
b
i2
SM
B
t
+
b
i3
H
M
L t
+
b
i4
X
M
I t
+
e it
,
R
it
�
R
ft
�
� =
a
i
+
b
i1
R
m
t
�
R
ft
�
� +
b
i2
SM
B
t
+
b
i3
H
M
L t
+
b
i4
M
O
M
t
+
b
i5
X
M
I t
+
e it
,
R
it
is
th
e
va
lu
e-
w
ei
gh
te
d
av
er
ag
e
re
tu
rn
fo
r
2
5
p
o
rt
fo
lio
s
b
as
ed
o
n
th
e
se
n
si
ti
vi
ty
o
f
in
d
iv
id
u
al
fir
m
s
to
th
e
ch
an
ge
s
o
f
ex
ch
an
ge
ra
te
s
(e
.g
.,
R
M
B
/U
SD
an
d
B
A
SK
E
T
/R
M
B
).
R
m
t
is
th
e
va
lu
e-
w
ei
gh
te
d
av
er
ag
e
re
tu
rn
o
f
al
l
co
m
p
an
ie
s
in
th
e
sa
m
p
le
.
R
ft
is
th
e
1
-y
ea
r
d
ep
o
si
t
in
te
re
st
ra
te
in
m
o
n
th
t.
SM
B
is
th
e
re
tu
rn
o
n
a
p
o
rt
fo
lio
o
f
sm
al
l
st
o
ck
s
m
in
u
s
th
e
re
tu
rn
o
n
a
p
o
rt
fo
lio
o
f
la
rg
e
st
o
ck
s.
H
M
L
is
th
e
re
tu
rn
o
n
a
p
o
rt
fo
lio
o
f
h
ig
h
b
o
o
k-
to
-m
ar
ke
t
st
o
ck
s
m
in
u
s
th
e
re
tu
rn
o
n
a
p
o
rt
fo
lio
o
f
lo
w
b
o
o
k-
to
-m
ar
ke
t
st
o
ck
s.
M
O
M
is
th
e
re
tu
rn
o
n
a
p
o
rt
fo
lio
o
f
p
as
t
w
in
n
er
st
o
ck
s
m
in
u
s
a
p
o
rt
fo
lio
o
f
p
as
t
lo
se
r
st
o
ck
s.
X
M
I t
is
th
e
fo
re
ig
n
-e
x
ch
an
ge
ri
sk
fa
ct
o
r
cr
ea
te
d
b
y
a
ze
ro
-i
nv
es
tm
en
t
p
o
rt
fo
lio
th
at
ta
ke
s
lo
n
g
p
o
si
ti
o
n
s
in
p
o
rt
fo
lio
s
ra
n
ke
d
1
st
an
d
2
5
th
(h
ig
h
ly
se
n
si
ti
ve
)
an
d
sh
o
rt
p
o
si
ti
o
n
s
in
p
o
rt
fo
lio
s
ra
n
ke
d
2
n
d
to
2
4
th
(l
es
s
o
r
n
o
ns
en
si
ti
ve
).
T
h
e
ta
b
le
sh
o
w
s
th
e
G
ib
b
o
ns
,
R
o
ss
,
an
d
Sh
an
ke
n
’s
(1
9
8
9
)
F
st
at
is
ti
c
(G
R
S)
fo
r
te
st
in
g
th
e
n
u
ll
hy
p
o
th
es
is
th
at
th
e
m
o
d
el
’s
in
te
rc
ep
ts
ar
e
jo
in
tl
y
eq
u
al
to
ze
ro
fo
r
al
l
2
5
ex
ch
an
ge
ra
te
-s
en
si
ti
ve
p
o
rt
fo
lio
s.
T
h
e
p
va
lu
es
o
f
th
e
G
R
S
F
te
st
ar
e
re
p
o
rt
ed
in
th
e
co
lu
m
n
n
ex
t
to
th
e
G
R
S
F
st
at
is
ti
c.
A
b
so
lu
te
al
p
h
a
is
th
e
m
ea
n
o
f
th
e
ab
so
lu
te
va
lu
e
o
f
th
e
m
o
d
el
’s
in
te
rc
ep
ts
o
f
th
e
2
5
ex
ch
an
ge
ra
te
-s
en
si
ti
ve
p
o
rt
fo
lio
s.
Sq
ua
re
d
al
p
h
a
is
th
e
m
ea
n
o
f
sq
u
ar
ed
in
te
rc
ep
ts
o
f
th
e
2
5
ex
ch
an
ge
ra
te
-s
en
si
ti
ve
p
o
rt
fo
lio
s.
T
he
ab
so
lu
te
o
r
sq
u
ar
ed
va
lu
es
o
f
th
e
in
te
rc
ep
ts
re
p
re
se
nt
th
e
m
ea
n
p
ri
ci
n
g
er
ro
rs
.
V
ar
ia
n
ce
o
f
al
p
h
a
is
th
e
m
ea
n
o
f
sq
u
ar
ed
d
iff
er
en
ce
s
b
et
w
ee
n
th
e
al
p
h
a
an
d
th
e
av
er
ag
e
o
f
re
gr
es
si
o
n
in
te
rc
ep
ts
.
T
h
e
n
u
m
be
rs
in
th
e
p
ar
en
th
es
es
ar
e
th
e
t
st
at
is
ti
c
fo
r
te
st
in
g
th
e
d
iff
er
en
ce
s
in
ab
so
lu
te
al
p
h
as
(o
r
sq
u
ar
ed
al
ph
as
)
b
et
w
ee
n
th
e
m
o
d
el
s
w
it
h
an
d
w
it
h
o
u
t
th
e
X
M
I
fa
ct
o
r.
T
h
e
n
u
ll
hy
p
o
th
es
is
is
th
at
th
e
d
iff
er
en
ce
o
f
ab
so
lu
te
(s
q
u
ar
ed
)
al
p
h
as
b
et
w
ee
n
m
o
d
el
s
w
it
h
an
d
w
it
h
o
u
t
X
M
Is
is
eq
u
al
to
ze
ro
.
In
th
e
ca
se
o
f
B
A
SK
E
T
X
M
I,
th
e
sa
m
p
le
b
re
ak
s
in
to
tw
o
p
er
io
d
s
(J
u
ly
1
9
9
7
to
Ju
ly
2
0
0
6
an
d
Ju
ly
2
0
0
7
to
Ju
n
e
2
0
1
5)
.
In
th
e
ca
se
o
f
U
SD
X
M
I,
th
e
sa
m
p
le
is
fr
o
m
Ju
ly
2
0
0
7
to
Ju
n
e
2
0
1
5
.
T
h
is
is
b
ec
au
se
th
e
ex
ch
an
ge
ra
te
w
as
st
ri
ct
ly
p
eg
ge
d
to
th
e
U
SD
,
th
e
p
ri
ci
n
g
te
st
fo
r
U
SD
X
M
I
is
th
er
ef
o
re
n
o
t
av
ai
la
b
le
p
ri
o
r
to
2
0
0
7.
61
included in the three-factor models, the decreases of average absolute and squared alphas
are statistically significant at the 1% and 5% levels, respectively. When XMI measured by
RMB/USD is included in the four-factor models, the decreases of average absolute alphas
are statistically significant at the 5% levels. The results suggest that during the period of
the controlled floating exchange rate regime that is pegged to a ‘‘reference basket’’ of cur-
rencies, the exchange rate risk exposure to USD fluctuation is priced in Chinese stock
returns. This is not surprising given that the USD carries the largest weight in the currency
basket. In addition, the majority of international trade in China is denominated in the USD
and most of the foreign reserves are in the USD.
For XMI measured by BASKET/RMB, the average absolute alpha significantly decreased
at the 10% level when XMI is included as an additional factor in asset pricing tests for the
second subperiod (July 2007 to June 2015), but not for the first subperiod (July 1997 to
June 2006). These results suggest that reference basket-based foreign-exchange risk was
priced in asset returns after the July 2005 exchange rate regime change. Overall, asset pric-
ing test results show that the exchange rate risk is not priced in equity returns under the
exchange regime that is strictly pegged to the USD. However, the exchange rate risk, either
measured with the USD or measured with the basket of currencies, is priced under the con-
trolled floating regime that is pegged to an index of the currency basket.
Hedging of Exchange Rate Risk Exposure
Our findings that exchange risk exposures are priced in stock returns suggest that compa-
nies should hedge their currency exposure. We therefore examine if companies have ever
increased their hedging activities through derivatives trading.20 The data on the holdings of
derivative assets began available in 2007, which makes it impossible to compare the
change in derivatives holding before and after the exchange rate regime change in 2005.
To examine the hedging of currency risk, we hand collected the use of currency derivatives
from annual reports of the companies that hold derivatives assets.
Appendix B reports the information on currency-related derivative assets. Although only
a small percentage of companies in China hold derivatives assets, the percentage is increas-
ing. For example, in 2007, only 14 companies out of 1485 companies, or 0.94% of the
stock exchange listed companies, held derivative assets. This number increased to 24 com-
panies out of 2,472, or 0.97% of listed companies in 2013. In 2014, the number of compa-
nies using derivative assets jumped up to 52 out of 2,593, or 2.0% of listed companies.
Prior to 2014, among the companies who use derivative assets, mass majority were finan-
cial institutions. However, the pattern seems to have changed in recent years. For example,
in 2014, out of 52 companies using derivative assets, 24 companies are nonfinancial com-
panies. The summary statistic shows that among the companies that use derivatives assets,
the percentage of currency derivatives account for over 70% of the fair value of the total
derivatives traded.
Figure 3 shows the trend in derivatives holdings by Chinese companies. The figure
shows the quarterly weighted derivative holdings and the weighted number of companies
that hold derivatives assets. The weight for each quarter is equal to the total assets of the
companies using derivatives in this quarter divided by the total assets of all companies
using derivatives over the entire sample period. Clearly, both the derivatives holdings (left
scale) and the number of companies (right scale) using derivatives have been steadily
increasing since 2007. This suggests that companies with exchange rate risk exposure are
cognizant of the risk and have increasingly engaged in the hedging activities.
62 Journal of Accounting, Auditing & Finance
Transmission Channels and Pricing of Exchange Rate Risk
The asset pricing test results in the previous section show that exchange rate risk exposure
is priced under the controlled floating regime that is pegged to a trade-weighted index of
currency basket. To examine what may have affected the strength of the pricing, we
attempt to identify the economic channels that can transmit the exchange rate risk exposure
to asset risk premium under the controlled floating exchange rate regime in China. In par-
ticular, we examine whether the pricing of exchange rate risk is related to the firm’s sensi-
tivity to the economic channels that transmit exchange rate risk exposure to asset risk
premiums. To that end, we first measure the sensitivities of stock returns to the three trans-
mission channels discussed in the section ‘‘Economic Channels Transmitting Exchange
Rate Exposure to Asset Risk Premium’’ and then perform asset pricing tests on the portfo-
lios sorted on the sensitivity to transmission channels. Given the results in the section
‘‘Pricing of Exchange Rate Risk in Cross-Section of Stock Returns,’’ we conduct the test
for the sample from July 2005 to June 2015, the period with the controlled floating rate
regime.
Sensitivities of Stock Returns to Transmission Channels
We first examine the sensitivity of stock returns to the transmission channels. If the stock
returns strongly react to the three transmission channels, then those channels should have
an impact on stock price variation when there is exchange rate movement. We use panel
regression with cross-sectional portfolio fixed effects to test the contemporaneous impact
of exchange rate movements and three transmission channels on stock returns.21 We make
the following specification for the panel regressions:
Figure 3. The derivative holdings of listed companies.
Note. The figure shows quarterly weighted derivative holdings and number of companies that use derivatives. The
weight for each given quarter is the ratio of the total assets of the companies using derivatives in this quarter to
the total assets of all companies using derivatives over the entire sample period. The sample period spans from the
first quarter of 2007 (when the derivative holding data becomes available) to the second quarter of 2015. The left
scale is for the weighted derivatives holdings, with unit of 1 billion RMB. The right scale is for the weighted number
of companies.
Hua et al. 63
Rit = vi + d1Xi, t + d2TCi, t + ei, t, ð8Þ
where Rit represents returns on 25 exchange rate-sensitive portfolios and Xi,t is the percent-
age change of exchange rates measured by BASKET/RMB and RMB/USD. TCi,t represents
three transmission channel variables that include percentage changes in international trade,
hot money, and local credit supply. The estimated exposure coefficients d1 and d2 represent
the return sensitivity to the changes in exchange rate and the transmission channels, respec-
tively. Cross-sectional fixed effects are also included and are denoted by vi. The ei, t is the
stochastic error term which is generally allowed to be serially correlated. Table 9 reports
the results of the panel ordinary least squares (OLS) regressions for four combinations of
different variables.
The results in Panel A of Table 9 suggest that returns on the 25 portfolios sorted by
foreign-exchange rate sensitivity are sensitive to international trade, hot money, and local
credit supply (broad money supply). As expected, the coefficient of exchange rate risk is
Table 9. Return Sensitivities to Transmission Channels.
Independent variables Model 1 Model 2 Model 3 Model 4
Panel A: Dependent variable: Returns on foreign-exchange rate-sensitive portfolios
Constant 0.021 (0.002***) 0.010 (0.003***) 0.019 (0.002***) 0.008 (0.003)
BASKET/RMB –2.533 (0.154***) –2.523 (0.153***) — —
RMB/USD — — 2.203 (0.518***) 2.046 (0.518***)
TRADE –0.073 (0.015 ***) –0.093 (0.015***) –0.046 (0.015***) –0.067 (0.016***)
HM –0.003 (0.001***) –0.003 (0.001***) –0.003 (0.001***) –0.003 (0.001***)
M2 — 0.838 (0.172***) — 0.827 (0.180***)
Panel B : Dependent variable: Portfolio returns on ‘‘H’’ share stocks with corresponding ‘‘A’’ shares
Constant 0.074 (2.45**) 0.103 (2.14**) 0.042 (1.09) 0.070 (1.18)
BASKET/RMB –10.645 (–6.47***) –10.670 (–6.50***) — —
RMB/USD — — 1.250 (0.14) 1.650 (0.18)
TRADE 0.100 (0.40) 0.154 (0.62) 0.210 (0.84) 0.261 (1.10)
HM 0.005 (0.82) 0.005 (0.79) 0.007 (0.97) 0.007 (0.95)
M2 — –2.22 (–0.81) — –2.104 (–0.72)
Panel C: Dependent variable: Portfolio returns on ‘‘H’’ share stocks without corresponding ‘‘A’’ shares
Constant –0.016 (–0.54) 0.014 (0.31) –0.018 (–0.51) 0.013 (0.29)
BASKET/RMB –2.440 (–1.03) –2.465 (–1.02) — —
RMB/USD — — 2.535 (0.33) 2.976 (0.39)
TRADE 0.086 (0.34) 0.142 (0.56) 0.112 (0.45) 0.168 (0.67)
HM –0.007 (–1.56) –0.008 (–1.21) –0.007 (–1.02) –0.007 (–1.06)
M2 — –2.283 (–0.89) — –2.32 (–0.92)
Note. In this table, Panel A reports the estimation results from the following panel OLS regression:
Rit = vi + d1Xit + d2TCit + eit,
where Rit represents 25 foreign-exchange sensitive portfolio return series and Xit is percentage change of exchange
rates, expressed in BASKET/RMB and RMB/USD, respectively. TCit represents three transmission channel variables,
which include percentage changes of international trade (TRADE), hot money (HM), and credit expansion (M2).
Panel B and C report the estimation results from the following OLS regression:
rHt = vt + dH1Xt + dH2TCt + et,
where rHt represents portfolio return series of ‘‘H’’ shares with corresponding ‘‘A’’ shares and ‘‘H’’ shares without
corresponding ‘‘A’’ shares. Definitions of Xt and TCt are the same as Xit and TCit.
Numbers in parentheses are t statistics. The sample is from July 2005 to June 2015.
**denotes statistical significance at 5% significance level. ***denotes statistical significance at 1% significance level.
64 Journal of Accounting, Auditing & Finance
positive for RMB/USD and negative for BASKET/RMB series. This suggests that RMB
appreciation on average is associated with decreases in the stock returns. The coefficients
are all statistically significant at the 1% significance level. The coefficient of international
trade variable is generally negative and significant at the 1% significance level, suggesting
that a currency appreciation would harm an export-oriented company’s earnings through
declining international trade. The coefficient of hot money is significantly negative. These
results together suggest that international trade expansion and the increase in hot money
generally lead to a negative impact on stock returns. Empirical evidence here indicates that
Chinese investors have interpreted the changes of speculative cash inflows as a negative
signal to the stock market. The coefficient of broad money supply is positive at the 1% sig-
nificance level, implying that credit expansion positively affect market valuation. Overall,
the results show that the three transmission channels significantly affect stock returns.
If Chinese ‘‘A’’ share stock returns have become sensitive to transmission channels after
the reform of controlled floating exchange rate in 2005, we should expect that returns on
Chinese shares listed in Hong Kong stock exchange as ‘‘H’’ shares are not sensitive to the
transmission channels, because ‘‘H’’ shares are denominated in Hong Kong dollars that
pegs to the USD. We thus examine the return sensitivities of ‘‘H’’ share stocks to the trans-
mission channels. Panel B and C in Table 9 report the results for ‘‘H’’ shares with the cor-
responding ‘‘A’’ shares and ‘‘H’’ shares without the corresponding ‘‘A’’ shares,
respectively. Both Panel B and C show that ‘‘H’’ share stock returns are not sensitive to
the three transmission channels. This difference suggests that the change from pegged to
managed floating exchange rate regime affect the return sensitivity to transmission channels
only for the domestic ‘‘A’’ shares. Panel B shows that, similar to the domestic ‘‘A’’ share
return, ‘‘H’’ shares with the corresponding ‘‘A’’ shares traded on Chinese domestic stock
markets is negatively correlated with exchange rate series measured in BASKET/RMB at
the 1% significance level. By contrast, results in Panel C suggests that for ‘‘H’’ shares
without corresponding ‘‘A’’ shares, there is no significant correlation between the ‘‘H’’
share returns and BASKET/RMB exchange rate series, possibly due to the pegged exchange
rate for Hong Kong dollars.
Pricing of Exchange Rate Risk for Portfolios Sorted by Transmission Channels
We further examine if a company whose stock returns are more sensitive to above three
channels are more likely to respond to exchange rate risk. For each stock we first estimate
the following regression using 2 years of rolling data and obtain annual sensitivity un1s
(same as obtaining annual foreign-exchange risk exposure coefficients described in the sec-
tion ‘‘Pricing of Exchange Rate Risk in Cross-Section of Stock Returns’’) to international
trade (TRADE), hot money (HM), and domestic credit supply (M2) based on Equation 9 as
follows.
rnt � Rft
� �
= cn + un1TRADE or HM or M2ð Þ+ ent, ð9Þ
where rnt is the log return on stock n in month t and Rft is the 1-year deposit interest rate
at month t. According to the values of sensitivity to TRADE, we sort stocks at end of each
June into three groups: high, medium, and low groups at breakpoints of the top 30%,
middle 40%, and bottom 30%. The procedure is repeated for the hot money and credit
supply channels. We then estimate Fama-French three-factor or momentum four-factor
regressions with foreign-exchange risk being added as shown in Equations 6 and 7. The
Hua et al. 65
dependent variable Rit is the value-weighted portfolio returns on high, medium, and low
groups ranked by sensitivity coefficients to changes in international trade, hot money, and
broad money supply, respectively. Table 10 reports the results. The high and low sensitivity
coefficient groups consist of those stocks with most positive or negative coefficients and
hence have higher sensitivities (in absolute value) to the transmission channels than those
in the medium sensitivity coefficient group.
There are three main findings based on the empirical results reported in Table 10. First,
Panel A reports portfolios sorted by sensitivity coefficients of international trade. Because
the proxy for international trade, variable TRADE, is the monthly percentage change of
total values of export and imports, our focus is therefore on the absolute value of the sensi-
tivity coefficient. Results show that the ‘‘medium’’ group, which has the smallest sensitiv-
ity to international trade in absolute term, does not have significant beta coefficient of the
exchange rate risk factor. On the contrary, the ‘‘low’’ group, which has the most negative
sensitivity to international trade, show significantly positive coefficient of exchange rate
risk factor measured by the RMB/USD series. By contrast, the ‘‘high’’ group, that is, the
stock with most positive sensitivity to international trade, has a marginally significant nega-
tive beta coefficient of foreign-exchange risk factor measured by RMB/USD series. The
evidence thus suggests that stocks that are affected by international trade carry stronger
risk premium on their exposure to foreign-exchange risk.
Second, Panel B in Table 10 report the results on portfolios sorted by hot money sensi-
tivity. The ‘‘low’’ group, which has the most negative sensitivity to hot money, shows posi-
tive beta coefficient of exchange rate risk factor measured by either BASKET/RMB or
RMB/USD. The ‘‘high’’ group, which has the most positive sensitivity to hot money, shows
positive beta coefficient of exchange rate risk factor measured by BASKET/RMB. In con-
trast, the ‘‘medium’’ group, which has the least (in absolute value term) sensitivity to hot
money does not have significant beta coefficient of exchange rate risk factor.
Third, the results based on portfolios sorted by broad money supply (M2) in Panel C
show that the ‘‘low’’ group respond significantly to the exchange rate risk measured by
BASKET/RMB, while the ‘‘high’’ group has a significantly positive beta coefficient of
exchange risk measured by RMB/USD. In contrast, although the ‘‘medium’’ group has a
negative beta coefficient, the magnitude is small. The positive coefficient of foreign-
exchange rate risk measured by BASKET/RMB (RMB/USD) for ‘‘low’’ (‘‘high’’) group sup-
ports the notion that the increase in risk premium for RMB appreciation is stronger for
companies highly sensitive (in absolute value term) to local credit market expansion. For
instance, highly leveraged (thus sensitive to local credit market condition) and export-
dependent firms may particularly experience stronger increase in equity risk premium on
expectation of RMB appreciation. In sum, evidence suggests that the pricing of foreign-
exchange rate risk is more pronounced for companies that are highly sensitive to the three
transmission channels, namely, international trade, hot money and local credit supply.
Conclusion
We examine whether and how exchange rate risks under a pegged as well as controlled
floating regime can be priced in asset returns. We do so by studying the Chinese currency
and stock return data that cover the regime changes in exchange rate determination. Prior
research reported that Asian-Pacific firms have more widespread and significant exposure
to foreign-exchange rate risk than their counterparts in industrialized economies, and the
exchange rate pegs do not alleviate this exposure. It remains unknown, however, whether
66 Journal of Accounting, Auditing & Finance
T
a
b
le
1
0
.
P
ri
ci
n
g
o
f
Fo
re
ig
n
-E
x
ch
an
ge
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is
k
in
th
e
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re
se
n
ce
o
f
a
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an
sm
is
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o
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h
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(u
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)
to
ch
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o
f
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o
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,
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it
ex
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an
si
o
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ve
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a
ro
lli
n
g
2
-y
ea
r
p
er
io
d
:
r n
t
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ft
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c n
+
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n1
T
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o
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2
ð
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e n
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t
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id
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is
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e
1
-y
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r
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o
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te
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in
m
o
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t.
T
R
A
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(o
r
H
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o
r
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2
)
is
th
e
p
er
ce
n
ta
ge
ch
an
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o
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in
te
rn
at
io
n
al
tr
ad
e
(o
r
h
o
t
m
o
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ey
o
r
cr
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it
ex
p
an
si
o
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)
in
m
o
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t.
St
o
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s
ar
e
so
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in
to
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ig
h
,
m
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iu
m
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hi
s
ta
b
le
re
p
o
rt
s
al
p
h
a,
w
h
ic
h
is
th
e
m
o
d
el
in
te
rc
ep
t,
an
d
b
i4
(b
i5
),
th
e
ri
sk
p
re
m
iu
m
o
f
th
e
fo
re
ig
n
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x
ch
an
ge
ri
sk
fa
ct
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r.
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h
e
sa
m
p
le
p
er
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d
is
fr
o
m
Ju
ly
2
0
0
7
to
Ju
n
e
2
0
1
5.
N
u
m
b
er
s
in
p
ar
en
th
es
es
ar
e
t
st
at
is
ti
cs
.
*
*
d
en
o
te
s
st
at
is
ti
ca
l
si
gn
ifi
ca
n
ce
at
5
%
si
gn
ifi
ca
n
ce
le
ve
l.
*
*
*
d
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o
te
s
st
at
is
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ca
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si
gn
ifi
ca
n
ce
at
1
%
si
gn
ifi
ca
n
ce
le
ve
l.
68
such exchange rate risk exposures under a pegged or controlled floating exchange rate
regime can be priced in asset returns.
Chinese RMB exchange rate determination has gone through a few major changes from
a strictly USD-pegged regime to a controlled floating mechanism. As the only currency in
the basket of IMF’s SDR that is not market-driven for its exchange rate formation, Chinese
RMB offers an ideal case to study the pricing of exchange rate risk in a pegged or con-
trolled floating regime given the country’s robust economic activities and off-shore trading
of the currency.
We find that under a strictly pegged exchange rate regime, exchange rate risk does not
seem to be priced in asset returns. However, the exchange rate risk is priced during the
period of a controlled floating exchange rate regime. There is a negative stock market reac-
tion to the announcement of the change from a strictly pegged exchange rate to a controlled
floating regime, which subsequently led to a series of appreciation of RMB. In general, due
to China’s export-oriented economy, a currency appreciation adversely affects the econ-
omy; the negative stock market reaction suggests a negative stock valuation effect of
expectation of increased exchange rate risk exposure. We also find that volatilities of earn-
ings, ROA and P/E ratios are significantly greater for ‘‘export’’ companies with larger
exposure to exchange rate risk than for ‘‘nonexport’’ companies after the exchange rate
reform in 2005. In addition, the time series exchange rate movement is significantly corre-
lated with stock returns after the change to the controlled floating regime.
We further find that the pricing of exchange rate risk is more pronounced for stocks that
are more sensitive to international trade, hot money and local credit supply, the three main
economic channels that transmit the exchange rate risk exposure to asset risk premiums.
Our analyses with the ‘‘H’’ shares show that Chinese shares listed on the Hong Kong stock
exchange do not exhibit the same effect to the announcement of exchange rate reform, and
more importantly, they are not sensitive to the three transmission channels due to Hong
Kong’s pegged currency regime. We also find that the use of financial derivatives
by Chinese companies have increased over recent years. Overall, our results suggest that
foreign-exchange rate risks under a controlled floating exchange rate regime can be priced
in asset returns and currency risk hedging under such regime is warranted.
Hua et al. 69
A
p
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(c
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d)
70
A
p
p
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n
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ix
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.
(c
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ti
nu
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gr
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in
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t.
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.
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ch
.
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u.
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lt
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n
te
r.
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en
.
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ta
l
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el
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4
N
ot
e.
P
an
el
A
sh
o
w
s
th
e
n
u
m
b
er
o
f
co
m
p
an
ie
s
in
ea
ch
in
d
u
st
ry
in
th
e
fin
al
sa
m
p
le
.
T
h
e
in
d
us
tr
ie
s
in
cl
u
d
e
A
gr
ic
u
lt
u
re
,
Fo
re
st
ry
,
St
o
ck
br
ee
d
in
g
an
d
Fi
sh
in
g
(A
gr
i.)
;
M
in
in
g
(M
in
.)
;
M
an
u
fa
ct
ur
in
g
(M
an
uf
.)
;
U
ti
lit
ie
s
in
cl
u
d
in
g
E
le
ct
ri
ci
ty
,
H
ea
ti
n
g,
G
as
,
an
d
W
at
er
(U
til
.)
;
C
o
n
st
ru
ct
io
n
(C
on
.)
;
W
h
o
le
sa
le
an
d
R
et
ai
l
(W
ho
.&
R
et
.)
;
Tr
an
sp
o
rt
at
io
n
,
St
o
ra
ge
an
d
Po
st
(T
ra
ns
.)
;
H
o
te
l
an
d
R
es
ta
u
ra
n
t
(H
ot
.&
R
es
t.
);
In
fo
rm
at
io
n
Te
ch
n
o
lo
gy
(I
.T
.)
;
Fi
n
an
ci
al
In
st
it
u
ti
o
n
s
(F
in
.)
;
R
ea
l
E
st
at
e
(R
ea
l
E
st
.)
;
R
en
ti
n
g
an
d
B
u
si
n
es
s
Se
rv
ic
es
(R
en
t.&
B
us
.)
;
Sc
ie
n
ce
R
es
ea
rc
h
an
d
Te
ch
n
o
lo
gy
Se
rv
ic
es
(S
ci
e.
&
Te
ch
.)
;
W
at
er
C
o
n
se
rv
an
cy
,
E
nv
ir
o
n
m
en
t
an
d
P
ub
lic
Fa
ci
lit
ie
s
(E
nv
i.)
;
E
d
u
ca
ti
o
n
(E
du
.)
;
H
ea
lt
h
an
d
So
ci
al
W
o
rk
(H
ea
lth
);
C
u
lt
u
re
,
Sp
o
rt
an
d
E
n
te
rt
ai
n
m
en
t
(E
nt
er
.)
;
an
d
O
th
er
In
d
u
st
ri
es
(G
en
.)
.
T
h
e
C
iv
il
Se
rv
ic
e,
M
ai
n
te
n
an
ce
,
an
d
O
th
er
se
rv
ic
es
in
d
u
st
ry
ar
e
n
o
t
sh
o
w
n
d
ue
to
la
ck
o
f
d
at
a.
T
h
e
in
d
u
st
ry
p
o
rt
fo
lio
s
ar
e
fo
rm
ed
at
th
e
en
d
o
f
Ju
n
e
o
f
ea
ch
ye
ar
,
t.
Fo
r
ex
am
p
le
,
ye
ar
1
9
9
5
re
p
re
se
nt
s
th
e
p
er
io
d
fr
o
m
Ju
ly
1
9
9
5
to
Ju
n
e
1
9
9
6.
C
o
m
p
an
ie
s
w
it
h
le
ss
th
an
4
m
o
n
th
s
p
ri
ce
s
ar
e
o
m
it
te
d
fr
o
m
th
at
ye
ar
.
P
an
el
B
sh
o
w
s
th
e
p
er
ce
n
ta
ge
o
f
co
m
p
an
ie
s
w
it
h
ex
p
o
rt
re
ve
n
u
es
w
it
h
in
ea
ch
in
d
us
tr
y
fr
o
m
2
0
0
1
to
2
0
1
4.
T
he
d
at
a
o
n
ex
p
o
rt
re
ve
n
u
e
fo
r
ea
ch
co
m
p
an
y,
o
b
ta
in
ed
fr
o
m
ea
ch
co
m
p
an
y’
s
an
n
u
al
fin
an
ci
al
st
at
em
en
t,
ar
e
av
ai
la
b
le
fr
o
m
ye
ar
2
0
0
1.
71
A
p
p
e
n
d
ix
B
.
D
er
iv
at
iv
e
H
o
ld
in
gs
o
f
A
ll
Li
st
ed
C
o
m
p
an
ie
s.
To
ta
l
n
u
m
b
er
o
f
co
m
p
an
ie
s
N
u
m
b
er
o
f
co
m
p
an
ie
s
h
o
ld
in
g
d
er
iv
at
iv
es
N
u
m
b
er
o
f
fin
an
ci
al
In
st
it
u
ti
o
n
s
h
o
ld
in
g
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:
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‘‘n
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(N
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)
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s,
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.
72
Acknowledgments
The authors gratefully acknowledge the thoughtful and constructive comments from Chris Adcock,
Warren Bailey, Vihang Errunza, Ghon Rhee, Trevor Rogers, Bharat Sarath (the editor), Chu Zhang,
an anonymous referee, and seminar participants at University of Hawaii at Manoa, Deakin University,
Hunan University, Jinan University, Nottingham University Business School China. They are grateful
to Menglong Yang and Mingyuan You for their research assistance.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/
or publication of this article.
Funding
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or
publication of this article: Wei Huang gratefully acknowledges financial support from John and Sue
Dean professorship at the Shidler College of Business, University of Hawaii Manoa, National Natural
Science Foundation of China (Project #71773027), the Center for Economics, Finance and
Management Studies (CEFMS) at Hunan University. Xiuping Hua gratefully acknowledges financial
support from Center for Inclusive Finance at the People’s Bank of China Ningbo Branch and Ningbo
Science and Technology Bureau for Innovation Team (No. 2011B81006).
Notes
1. According to the press release of the Society for Worldwide Interbank Financial
Telecommunication (SWIFT, October 6, 2015), Chinese Yuan has entered the top four of world
payment currencies by value in August 2015 along with USD, Euro, and British pound, overtak-
ing the Japanese yen and reaching a record high share of 2.79% in global payments. In the last 3
years, the RMB has overtaken seven currencies rising from position number 12 with a share of
0.84% in August 2012.
2. Besides China, other major economies, such as Bahrain, Cuba, Djibouti, Eritrea, Hong Kong,
Jordan, Lebanon, Oman, Panama, Qatar, Saudi Arabia, United Arab Emirates, and Venezuela,
have pegged their currencies to the USD (see www.investopedia.com/).
3. No prior study has examined the pricing of exchange rate risk under a pegged regime in an emer-
ging market environment. Bartov, Bodnar, and Kaul (1996) studied the regime change in the
United States. They studied the relation between exchange rate variability and stock return vola-
tility for U.S. multinational firms over two 5-year periods around the 1973 switch from fixed to
floating exchange rates and find a significant increase in the volatility of monthly stock returns.
4. The official rate for the USD price was adjusted from 2.80 RMB in 1985 to 3.72 in 1986 and to
5.20 in 1990. After fluctuation between 1992 and 1994, it depreciated to 5.80. Correspondingly,
the market exchange rate changed from 3.08 Yuan per dollar to 4.20 in 1985, 5.70 in 1988, and
8.70 in January 1994.
5. For example, Henan Rebecca Hair Products (stock code: 600439.SH) is one of the listed compa-
nies with high ratios of exports to sales. By June 2005, most of its products were sold to overseas
markets, with 69.30% of total sales in the North American market, 10.45% in the European
market, and 18.20% in the African market. With the RMB currency appreciation trend since July
2005, the company has faced higher external uncertainties and has worked very hard to increase
domestic sales and distribution channels. Despite its effort, most of the company’s sales are still
in the overseas market. In mid-2009, 59.65% of its total sales were in the North American
market, while 6.01% were in the European market and 29.55% in the African market. Our model
estimation at firm level confirms that international trade was a driving force of their stock returns
during 2005 and 2015. The company is highly sensitive to international trade. The company,
Hua et al. 73
along with other companies with high export revenues, such as Qingdao Haier Co. (600690.SH)
and Ningbo Electric Appliance (600724.SH), are among the group with high sensitivity to Trade
in Table 10 during some sample years.
6. For example, one type of industry prone to the hot money effect is mining companies, such as
Yunnan Copper (code: 000878.SZ). The company has principally engaged in the production and
sale of copper concentrates, precious metals, and related products, as well as chemical products.
The company, which operates its businesses mainly in the domestic markets, is popularly per-
ceived in the market and has attracted a large amount of short-term speculative funds, especially
during exchange rate reform periods. The other sector that may have attracted a lot of hot money
is real estate. However, the government had adopted strict regulation measures to prevent the
influx of hot money into this sector. For instance, foreign institutions with a physical presence in
China are only allowed to buy commercial property for their own use and it must be in the city
in which they are registered. Thus, not all real estate companies are affected by hot money. The
stock mentioned above, Yunnan Copper, along with other companies such as Tibet Mining
(000762.SZ), China Merchants Property Development Co. (000024.SZ), and Overseas Chinese
Town (000069.SZ), are part of ‘‘Hot Money (HM)’’ sensitive group in Table 10 during some
sample years.
7. China has recently surpassed Japan to become the world’s largest reserve holder (Ouyang, Rajan,
& Willett, 2010).
8. China Vanke Co. (000002.SZ), the country’s largest real estate developer by sales, provides an
example. The company has focused primarily on residential property development and its home
sales have always been affected by the government’s credit expansion or tightening measures.
Other similar stocks, including China Guanyu Development Co. (000537.SZ), Zhongjiang
Property Co. (600053.SH), and Huayuan Property Co. (600743.SH) are part of M2 high-
sensitivity group in Table 10 during some sample years.
9. According to classification by China’s Securities Regulatory Commission in 2012, there are total
19 industries. We omit Civil service, maintenance, and other services industry in our sample due
to lack of data.
10. We obtained the export revenue information from companies’ financial reports and the data are
only available from 2001.
11. All variables are expressed in percentage changes.
12. M2 broad money supply includes savings deposits, money market mutual funds, and other time
deposits. They are less liquid but can be quickly converted into cash or checking deposits.
13. As aforementioned, on July 21, 2005, the People’s Bank of China (PBOC), China’s central bank,
made the announcement to switch Yuan from strictly pegged to the USD to a regime in which
the RMB was pegged to a basket of foreign currencies, including some Asian currencies. The
RMB appreciated by 2.1% immediately and a cumulative 21% against the USD by July 2008.
The appreciation process took a toll on Chinese exports, especially during the global financial
crisis. To help the country’s exporters during the financial crisis, China had effectively pegged
the Yuan at about 6.83 per dollar until June 2010. However, during this period, speculation
about an imminent move in the value of the RMB intensified amid rising trade tensions between
the United States and China. On June 19, 2010, the PBOC announced a new reform to improve
the flexibility in exchange rate determination by setting a midpoint and daily variation for the
value of USD. By June 2015, the RMB rose to a new record of 6.1161 Yuan per USD, marking
a 10.42% appreciation since the resumption of exchange rate reform in June 2010. Although the
RMB exchange rate regime is still heavily managed, it tends to be more volatile.
14. We obtained sales distribution data from companies’ financial statements. Export companies are
those with export revenues in their sales.
15. We thank the referee for suggesting this test.
16. For example, we estimate Equation 5 for each firm using data from July 1995 to June 1997 to
obtain firm-specific values of an5 coefficient for 1997. We repeat this procedure until 2015.
74 Journal of Accounting, Auditing & Finance
17. We also tried ranking the firms into 10 portfolios based on the value of an5; the test results are
similar to using the 25 grouping method.
18. As in Kolari, Moorman, and Sorescu (2008), because the emphasis is on the change in pricing
error, the risk factor can therefore be constructed based on the absolute value of foreign-
exchange sensitivity.
19. We use this setting because the exchange rate regime switching occurred in July 2005 and we
require 2 years of data to calculate coefficients’ loadings on the pricing factors.
20. We thank the referee for suggesting this analysis.
21. The random effects are also estimated and the results are very similar.
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US-China trade war and China’s stock market:
an event-driven analysis
He Chengyinga, Chen Ruib and Liu Yingb
aEconomic College, GuangXi University, Nanning, China; bDevelopment Research Department,
Guosen Securities Co., Ltd, Shenzhen, China
ABSTRACT
The US-China trade war, initiated in March 2018, substantially
transformed the trading partnership between the two largest eco-
nomic powers. It directly influenced the profitability of domestic
enterprises related to the export chain and harmed the domestic
economy in China and its stock market. This study empirically
examines the effects of the trade war on China’s stock market
based on chronological events and tests whether it is the conta-
gion effect or the present value effect. The empirical study sup-
ports the contagion effect because the impact of the US-China
trade war differed significantly in different sectors only when the
US announced its imposition of more tariffs on US$50 billion
worth of Chinese products. However, there is no apparent differ-
ence between the industries for other events, nor is there any sig-
nificant difference between the industries in terms of long-
term impact.
ARTICLE HISTORY
Received 12 May 2021
Accepted 4 October 2021
KEYWORDS
Trade war; Chinese stock
market; tariffs
JEL CODES
G11; G12; G14
Introduction
Since China officially entered the World Trade Organization (WTO) in December
2001, it has become a major player in the global economy. The rapid proliferation of
Chinese Outbound Direct Investment (ODI) has been attracting worldwide attention
due to its overwhelming presence and distinct characteristics, in contrast to previous
strategies (Yan et al., 2020). Globalisation reshaped the Chinese economy, leading to
increased export revenue and improved technology adoption (Skare & Soriano, 2021),
sustainability, and energy efficiency (Dabbous & Tarhini, 2021). However, trade fric-
tion is inevitable, as industrialisation in emerging countries has considerably changed
pertaining to division of labour and distribution of benefits. On 22 March 2018, US
President Donald Trump declared elevating tariffs on approximately US$50 billion in
imports from China. The disputes escalated on 6 July 2018, when the US announced
a 25% tariff on US$34 billion Chinese goods. Between November and December of
CONTACT He Chengying 2098533681@qq.com
� 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/
licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is
properly cited.
ECONOMIC RESEARCH-EKONOMSKA ISTRAŽIVANJA
2022, VOL. 35, NO. 1, 3277–3290
https://doi.org/10.1080/1331677X.2021.1990781
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https://doi.org/10.1080/1331677X.2021.1990781
http://www.tandfonline.com
the same year, the two countries agreed not to impose further tariffs at the
G20 summit.
The trade war between the US and China has increased the economic uncertainty
of the world. The tariffs imposed by the US on Chinese commodities have directly
affected China’s export chain and increased the downward pressure on its foreign
trade. These tariffs have also increased stock market risks, constrained listed compa-
nies’ performance, and made trading volume changes more susceptible (Cai et al.,
2020). This is likely to result in a lose-lose situation (Cui et al., 2019). Specifically,
every move in the US-China trade conflict could sway the capital market and affect
the stock market. When the escalation of the US-China trade conflict worsened,1 the
A-shares market reacted strongly. On 19 June 2018, the Shanghai Composite Index and
the GEM Index plunged by 3.78% and 5.76%, respectively, and 1019 stocks fell. Since
January 2019, China and the US have launched multiple rounds of high-level negotia-
tions and made substantial progress. On 25 February 2019, the US postponed tariffs on
Chinese products, initially scheduled for 1 March 2019. Affected by the news, the three
major A-share stock indexes rose more than 5%, and the market exceeded the 200
daily limits with turnover exceeding CN¥920 billion, a new high since December 2015.
These descriptive results briefly reflect the impact of the US-China trade friction as an
external shock on the Chinese stock market, but it does not exclude the effect of other
factors on the stock market during the same period. More importantly, this analysis
does not describe the impact of external shocks on the stock market industries.
Accordingly, this study empirically analyses the impact of the US-China trade friction
on listed Chinese companies. We aim to test whether the impact is a contagion effect
or the present value effect. As this trade war is a competition between the two largest
economic powers, its impact on the stock market is more like a system risk caused by
the contagion effect than an idiosyncratic risk caused by the present value effect.
Compared with the existing literature, the contributions of this study are reflected
in the following two aspects.
1. Based on the literature survey, we are among the first to use event-driven meth-
ods to test the impact of the US-China trade friction by comparing the changes
in abnormal stock returns before and after the event occurred. The real chal-
lenges are the abnormal returns found in major events of the trade friction.
When the front-to-back changes are not significant, it is often difficult to distin-
guish whether this is due to the inefficiency of the trade friction events or the
early responses caused by trade friction events.
2. At the industry level, we found that the impact of the trade war on the stock
market yield of different sectors is insensitive to the existence of industry
branches. When the tariff of US$50 billion was imposed, the impact of the trade
war on various industries was revealed in three stages. The later period revealed
that the overall impact of the trade war on the stock market was a systemic risk,
and industry differences were not significant. As we find more evidence that the
impact of the trade war on different sectors was not significantly different from
the market average, the hypothesis that the impact of the trade war on the stock
market is mainly the contagion effect is supported.
3278 H. CHENGYING ET AL.
The structure of this article is as follows. The second section is a literature review
on the US-China trade friction. The third section discusses the research methods and
models. The fourth section provides a descriptive statistical analysis of the impact of
the trade friction on the capital markets of listed companies. The fifth section
presents the empirical results of the effects of trade friction on the capital markets of
listed companies and determines whether the trade friction affected the Chinese stock
market. Finally, the sixth section provides the conclusions and recommendations.
Literature review
Existing research on the US-China trade friction can be summarised into the follow-
ing four levels:
The rise of US trade protectionism and the appeal of certain political and eco-
nomic interests in the US superstructure were key reasons for the increase in the
trade friction between the two countries. Navarro and Autry (2011) believed that
China’s state capitalism created many national champions to effectively carry out
mercantilism and protectionism through illegal export subsidies, infringement of US
intellectual property rights, lax environmental protection, and general overuse of
labour to destroy American industries and employment. Bhid�e and Phelps (2005)
believed that US policy pressure resulted from domestic industrial workers, but the
‘mercantilism’ of China was an inevitable result of trade between less developed and
developed countries.
Regarding research on how China should address the series of problems caused by
trade frictions, Bergsten et al. (2014) show that in the US-China trade, the potential
income of the US is from the increase in service trade. Therefore, persuading China
to open up the service market would greatly benefit the US and significantly improve
the bilateral trade imbalance. Meltzer (2016) showed that from the US’ perspective,
through China’s connection with the Asian value chain and the economic growth of
some American states from the import of Chinese products, the only solution to the
US-China friction was working side-by-side, which proved beneficial rather than pro-
voking any dispute.
In trade wars, ‘market size’ matters in the sense that countries are more likely to
initiate anti-dumping cases against countries with smaller home markets relative to
their own, than against countries with larger markets (Miyagiwa et al., 2016). The
macroeconomic costs of a trade war can be substantial, with permanently lower-
income and trade volumes (Lind�e & Pescatori, 2019). Two methods of calibration
and empirical research are generally used to evaluate the economic impact of trade
friction. In terms of calibration research, Guo et al. (2018) used a multi-sector, multi-
country general equilibrium model with intersectional linkages to forecast the changes
in exports, imports, output, and real wages by implementing the 45% tariffs indicated
by Donald Trump. Zhou and Shi (2019) and Lou (2019) adopted the global trade
analysis project (GTAP) model to simulate how the US-China trade friction would be
affected in different scenarios. The US-China trade friction affects both countries.
Particularly, the economy is negatively affected (Lou, 2019), whether short or long
term. China’s economic growth, exports, and imports have declined more than the
ECONOMIC RESEARCH-EKONOMSKA ISTRAŽIVANJA 3279
US’ (Zhou & Shi, 2019). Itakura (2020) evaluated the impact of the US-China trade
war using a dynamic computable general equilibrium (CGE) model of global trade.
He conducted an ex-ante simulation analysis exploring three scenarios to understand
how the trade war affects import tariffs, investment, and productivity.
In terms of empirical research, Du et al. (2017) used monthly data from 1990
through 2013 for China and estimated a model of political relations. They also con-
cluded that political shocks were short-lived. Freund and Gagnon (2017) examined
the effects of the border-adjusted consumption taxes (mainly value-added taxes or
VATs) in a sample of 34 advanced economies from 1970 to 2015. They found that
the real exchange rate tended to rise as the full amount of any consumption tax
increased, with little effect on the current account balance and modest offsetting
effects on trade and income balances. Muro et al. (2018) sorted out the list of 128 tax
items issued by China to counter the ‘232 investigations’ and the list of 106 tax items
to counter the ‘301 investigations’, matched to the US states and employment,
respectively, and combined them with the results of the US President Trump’s 2016
election vote. They found that the list issued by China was based on the geography of
US manufacturing, affecting the representative industries in the states where the
major Democratic parties were located. Cui et al. (2019) explored the boundary
effects of the US-China trade war by considering a multi-region CGE model to set up
six trade disruption scenarios based on the severity of the trade friction and empiric-
ally examined the gains and losses of the two countries and their potential impacts
on other countries. Amiti et al.,(2020) developed a new method for quantifying the
impact of policy announcements on investment rates using stock market data. By
estimating the effect of the US-China tariff announcements on aggregate returns and
the differential returns of firms exposed to China, they identified the effect on treated
and untreated firms. They showed theoretically and empirically that the estimates of
policy-induced stock market declines implied lower returns to capital, thereby lower-
ing investment rates.
In terms of research on the impact of the US-China trade friction on financial
markets, studies usually focus on the impact of trade conflicts on the trade itself, and
some studies considered the impact of conflicts on financial markets, especially on
the stock market. Heilmann (2016) found that boycotts could have strong negative
effects on bilateral trade in goods and services, and an event study on Japanese stock
market returns suggested that the Chinese boycott only temporarily depressed stock
values of the explicitly boycotted Japanese firms. Concerning the impact of the US-
China trade frictions on financial markets, Jia et al. (2019) quantified the horizontal
and trend effects of the US-China trade friction on the systematic risk of China’s
banking, securities, and insurance industries from the perspectives of mean value level
and tendency over time, using the improved event analysis method. Fang et al. (2019)
also used the event analysis method to quantitatively analyse the spillover effects of
the US-China trade friction on China’s stock, bond, and foreign exchange markets
and the spillover across these markets based on the event analysis. Goulard (2020)
studied the consequences of the US-China trade war on the exchanges between
Europe and China and analysed the possible diversion created by this trade war for
the European market.
3280 H. CHENGYING ET AL.
Though these literature explorations of the US-China trade friction provide a solid
foundation for the study of this article, they have some deficiencies: (1) There are few
deep-seated reasons for provoking trade disputes with the US. (2) Most of China’s
coping strategies were mainly qualitative analyses, lacking data support. (3) The study
of the economic effects of the US-China trade friction is insufficiently integrated with
the current global background, and most of the quantitative simulations are based on
hypothetical scenarios and not on the product list officially announced by the two
sides, leading to a specific deviation in the simulation results. (4) The multi-risk per-
spective evaluation and analysis of the financial impact of the US-China trade friction
is more affected than the financial market itself. Generally speaking, much literature
analyses the causes of the US-China trade friction, related countermeasures, and the
economic and financial impacts from a macro perspective. However, there is a lack of
evaluation of trade friction from the micro perspective of listed companies. Therefore,
this study analyses the impact of the US-China trade friction on the financial market
from the micro perspective of listed companies. Consequently, it quantitatively analy-
ses the reflection of the stock prices of listed companies in different industries in
response to the US-China trade friction and supplements the research in this field.
Research methods
Research scholars in the financial field often use the event study analysis framework
to study the impact of a specific event on a company’s stock price or yield and test
the financial market’s response to the new information (Brown & Warner, 1980).
While many authors have identified the hazards of ignoring event-induced variance
in event studies, Boehmer et al. (1991) have demonstrated that a simple adjustment
to the cross-sectional techniques produce appropriate rejection rates when the null is
true and equally powerful tests when it is false. Kolari and Pynn€onen (2010) proposed
a new test statistic that modified the t-statistic of Boehmer et al. (1991) to consider
cross-correlation and showed that it performed well in competition with others and
was readily useable to test multiple-day cumulative abnormal returns (CARs).
The empirical research in this study employs Kolari and Pynn€onen (2010) method
to evaluate the impact of the trade war on excess returns of different sectors in
China’s stock market. The excess return is calculated based on ten sectors categorised
by the Wind2 data and compared with the Chinese market indexes at critical times of
the trade war. The hypothesis tests whether the excess returns are positive after one
trading day and a cumulative 2–30 trading days. The specific regression formula is as
follows:
Rit ¼ aþ biRmt þ eit (1)
Here Rit is the return of industry i in time t, and Rmt is the market return in time
t. The time window used here for estimation is from the 210th day before a particular
event to the 11th day before the same particular event—200 trading days to calculate
the parameters in the regression. The market returns used in this study are the per-
formances of the CSI All Share Index and the return of the industries (the perform-
ances of all the 10 CSI All Share Sector Indices), including Energy, Materials,
ECONOMIC RESEARCH-EKONOMSKA ISTRAŽIVANJA 3281
Industrials, Consumer Discretionary, Consumer Staples, Health care, Financials,
Information Technology, Telecommunication Services, and Utilities. The specific
regression formula used to calculate the excess returns of industry i is as follows:
ARit ¼ Rit � aþ biRmt (2)
The hypothesis of this study is whether the excess return of industry i, ARit, is sig-
nificant. The null hypothesis is that the trade war has an equally negative impact on
all industries. If the excess return of industry i was significantly positive, then the
industry i index outperformed the market after an event of the trade war and the
impact of the trade war on industry i was less than the market average. Conversely, if
the excess return ARit was significantly negative, then the industry i was lower than
the market average after an event of the trade war, that is, the negative impact of the
trade war on industry i was more significant.
US-China trade conflict: incidents and description
The trade between China and the US has been firmly connected over the last decade,
and a large-scale trade conflict is likely to have adverse effects on both sides. The
trade war disturbs China’s macroeconomic trends, particularly when the market con-
siders that the US regards the trade war as a means to prevent China’s rise. If the
trade friction progresses into a full-scale confrontation, the overall economy of both
countries will inevitably suffer. The tariff will diminish the export competitiveness of
Chinese goods and directly affect enterprises that deliver sizeable exports to the US.
The earnings of export enterprises in China are anticipated to decline, directly weak-
ening the investors’ expectations and the stock price. This direct effect can be called
the ‘present value effect’.
The stock price reflects the market’s expectations of the corporation’s future profits
and dividends. Specifically, the trade war is likely to have a prominent ‘event infor-
mation effect’ on stock markets. Even listed firms that are not mainly exporting to
the US are pessimistic due to the macroeconomic situation. This indirect effect is
known as the contagious effect. This term was coined during the Great Depression of
the 1930s due to the mutual increase of tariffs among countries. History enhanced
market pessimism. However, China’s countermeasures against the US also influence
the US stock market, strengthening the contagion effect.
As trade friction consists of a series of chronological events, it can be divided into
six stages, as shown in Table 1, based on the intensity of the trade war and the
sequence of events.
From the CSI All Share Index perspective, the US-China trade conflict has fallen
by 27.27% since March 2018. Among the six critical periods, the CSI All Share Index
showed an increase only in the fourth phase (from 12 July 2018 to 1 August 2018),
with a range of 1.55%. The third and fifth phases saw the largest declines. In the third
phase, tariffs were levied on the US$50 billion worth of Chinese products and more
tariffs were announced on the US$200 billion worth of products. The fifth phase cor-
responds to the raised tax rate on the US$200 billion worth of products from 10%
to 25%.
3282 H. CHENGYING ET AL.
Empirical analysis
The trade war affects the stock market in the short term. In 2018, the CSI All Share
Index fell significantly, and the impact on various industries was different. This section
verifies the impact of the trade war on all the 10 CSI All Share Sector Indices, includ-
ing the trade war’s announcement period and execution period, on the stock market.
1st period: increase tariffs on steel and aluminium
The first period was the Start period. On 8 March 2018, the US imposed 25% and
10% tariffs on Chinese steel and aluminium imports, respectively, and the trade war
officially commenced. However, in the early days, market expectations of the trade
war were unclear, and the CSI All Share Index had a positive return on that day.
Tariffs were only imposed on steel and aluminium, with no significant negative
impact on the entire market.
However, on 23 March 2018, when the US announced taxes on US$50 billion
worth of products, the CSI All Share Index declined sharply, by nearly 4%. The dif-
ference between the sectors was not significant as only the Consumer Staples Index
had a significant positive excess return, and the Telecommunication Services Index
had a significant negative excess return (Table 2).
The 30 days cumulative abnormal return of various sectors from 8 March 2018
was not significant, except for the Health care Index, which had a positive significant
rate. Some industries had significant cumulative abnormal returns when the excess
return only cumulated to some specified days, but it was insignificant when excess
returns were accumulated for the 30 days. The tariffs only cast a very short-term
impact on some sectors, but in the long run, there was no significant impact on these
sectors compared to the market average.
2nd period: increase tariffs on US$50 billion worth of Chinese products
In this period, when the US imposed tariffs on US$50 billion worth of Chinese prod-
ucts, the stock market responded significantly to the trade war, and the whole stock
Table 1. The impact of the critical period of trade conflict on the broader market.
Stage Extent
The Return of CSI
All Share Index
The 1st Stage
(23 March 2018 to 8 April 2018)
Negative events dominate the outbreak phase �2.48
The 2nd Stage
(9 April 2018 to 29 May 2018)
After the outbreak of the trade conflict,
the two sides gradually eased and
began consultations until they reached
a consensus to suspend the trade war.
�1.99
The 3rd Stage
(30 May 2018 to 11 July 2018)
The US once again provokes
a trade war with negative events
�11.98
The 4th Stage
(12 July 2018 to 1 August 2018)
Trade war eased again 1.55
The 5th Stage
(2 August 2018 to 28 October 2018)
This period is full of negative events �12.62
The 6th Stage
(29 October 2018 to 31 December 2018)
Trade war once again eases �2.57
Source: Wind and authors’ calculations.
ECONOMIC RESEARCH-EKONOMSKA ISTRAŽIVANJA 3283
market declined significantly. On 4 April 2018, the US issued a tariff list for 1300
products, involving 25% tariffs on products worth US$50 billion. The market grad-
ually responded to the trade war. On 15 June 2018, the US government released a list
of goods subject to tariffs, imposing a 25% tariff on approximately US$50 billion
worth of goods imported from China: (1) the tariffs would be imposed on 6
June2018 for approximately US$34 billion worth of goods. (2) The tariffs would be
imposed on 23 August 2018 on US$16 billion worth of goods. During this period, 4
April 2018 and 15 June 2018 were the announcement dates, 6 July 2018 was the
US$34 billion tariff implementation date, and 23 August 2018 was the original US$16
billion tariff implementation date.
When the news was reported, the CSI All Share Index dropped only by 0.4% on 4
April 2018. The Consumer Staples Index had a significant positive excess return,
which did not respond to the US$50 billion tariffs negatively. The negative abnormal
return of the Information Technology Index was significant, and other sectors did
not have significant excess returns relative to the market average (Table 3).
On the 30 days effect, only the Healthcare Index had a significant positive cumula-
tive abnormal return. Other sectors did not have a significant return, even when the
excess return accumulated to other specified days.
On 16 June 2018, the US government announced once again that it would impose
a 25% tariff on approximately US$50 billion worth of products imported from China.
Consequently, the CSI All Share Index dropped by nearly 5% the following Monday.
However, only the Utilities Index had a significant negative return. Meanwhile, as the
impact of the trade war on the entire market gradually became apparent, its impact
Table 2. Cumulative abnormal return from 23 March 2018 in the 1st period.
Sectors 1 3 5 10 20 30
Energy 0.45 �1.84 �1.6 �3.71 �1.62 �2.17
Materials �0.23 0.11 0.14 �0.52 �0.08 �1.62
Industrials 0.22 0.86 1.1 1.68 2.08 2.57
Consumer Discretionary 0.21 �0.73 �1.25 �2.59�� �2.7 �2.39
Consumer Staples 2.42��� 0.27 �0.94 1.09 0.35 1.52
Health care 0.56 2.89��� 1.69 4.17��� 3.42 8.96���
Financial �0.73 �3.46�� �2.18 �4.2 �5.21 �6.52
Information Technology �0.31 3.64��� 3.19�� 4.41�� 5.49� 4.3
Telecommunication Services �1.59� 0.8 0.89 1.79 1.61 0.53
Utilities �0.43 �0.3 �0.1 �1.25 �1.63 �0.99
Source: Wind and authors’ calculations.
Table 3. Cumulative abnormal return from 4 April 2018 in the 2nd period.
Sectors 1 3 5 10 20 30
Energy �0.29 0.12 0.38 1.64 1.8 5.72
Materials �0.23 0.34 0.3 0.81 �0.47 �0.32
Industrials �0.15 �0.73 �0.65 0.25 0.44 0.11
Consumer Discretionary 0.47 �0.3 �0.26 �0.85 �0.69 0.17
Consumer Staples 2.66��� 0.75 0.62 0.85 2.48 3.88
Health care 0.16 0.13 0.89 �0.61 5.65�� 5.74��
Financial �0.16 1.72 1.11 �0.99 �2.3 �2.92
Information Technology �1.51�� �2.65�� �2.33 0.67 �1.2 �3.38
Telecommunication Services �1.08 �1.21 �1.12 �1.44 �1.48 �2.35
Utilities �0.06 �0.18 0.03 �0.76 �0.17 1.38
Source: Wind and authors’ calculations.
3284 H. CHENGYING ET AL.
on various sectors was nearly the same. The 30 days effect also showed the same
result; only the Consumer Discretionary Index had a significant negative cumulative
abnormal return, and the Utilities Index had a significant positive return. The cumu-
lative abnormal returns of other sectors were not significant relative to the market
average (Table 4).
On 6 July 2018, when the US executed the 25% tariff on US$34 billion in products,
the CSI All Share Index increased by 0.55%. There was no obvious excess return
between any sector and the market average. However, as the impact accumulated,
some sectors started showing significant excess returns, such as the Consumer
Discretionary Index and the Health care Index, which showed significant positive
returns (Table 5).
3rd period: increase tariffs on US$200 billion worth of products
The tariffs levied in this period generated a far-reaching impact on the economy and
stock markets. On 2 August 2018, the US planned to increase the tariff rate from
10% to 25% on US$200 billion worth of goods. On 18 September 2018, the US
announced that it would impose a 10% tariff on US$200 billion worth of Chinese
products from 24 September 2018, and the tax rate would increase to 25% from 1
January 2019.
On 11 July 2018, the US issued measures to impose tariffs on US$200 billion
worth of Chinese goods. The CSI All Share Index dropped by 1.92% on that day, but
the impact on various sectors was not significantly different from the market average.
Table 4. Cumulative abnormal return from 16 June 2018 in the 2nd period.
Sectors 1 3 5 10 20 30
Energy 0.37 1.32 1.09 0.71 �0.9 2.06
Materials �0.41 0.15 0.73 1.13 1.19 3.57
Industrials �0.45 �0.61 �0.04 1.11 �0.2 2.38
Consumer Discretionary 0.15 0.23 0.39 �0.65 �2.17 �3.72�
Consumer Staples 0.29 1.6 2.14 �1.24 �0.69 �4.85
Health care 0.61 2.98�� 3.57�� 4.07� 4.57 �6.03
Financial 0.59 0.19 �1.54 �5.56�� �4.51 0.03
Information Technology 0.25 �1.76 �1.1 5.67�� 5.59 2.25
Telecommunication Services �1.08 �3.57�� �3.69� 0.45 3.3 1.79
Utilities �1.98��� �2.16��� �2.34��� �2.05� �1.27 5.06���
Source: Wind and authors’ calculations.
Table 5. Cumulative abnormal return from 6 July 2018 in the 2nd period.
Sectors 1 3 5 10 20 30
Energy �0.61 �0.22 �1.62 �1.93 2.22 6.9
Materials �0.01 0.71 0.64 1.36 4.37 4.5
Industrials �0.5 �0.85 �0.99 �0.83 1.84 2.54
Consumer Discretionary 0.12 �0.35 �0.8 �0.87 �3.59�� �5.7���
Consumer Staples 0.65 0.65 1.09 0.51 �2.96 �8.24
Health care �0.32 0.84 1.94 0.94 �5.78� �8.71��
Financial 0.39 0.94 0.83 �0.62 2.22 3.4
Information Technology �0.25 �1.22 �1.11 �0.38 �3.11 �0.76
Telecommunication Services �0.25 �1.21 �0.08 3.03 1.8 8.61�
Utilities �0.59 �0.58 �0.95 2.06� 5.29��� 4.74��
Source: Wind and authors’ calculations.
ECONOMIC RESEARCH-EKONOMSKA ISTRAŽIVANJA 3285
After 30 trading days, the Consumer Discretionary Index and the Health Care Index
had relatively large negative cumulative abnormal returns, and the
Telecommunication Services Index and Utilities Index had significant positive returns
relative to the market average (Table 6).
On 2 August 2018, the US planned to increase the tariff rate on US$200 billion
worth of Chinese goods from 10% to 25%. That day, the Consumer Discretionary
Index showed a significant positive excess return, and the Health care Index had a
significant negative excess return relative to the market average. There was no signifi-
cant difference between the other industries and the market (Table 7).
After 30 trading days, the Consumer Discretionary and the Consumer Staples
Indexes showed significant negative cumulative abnormal returns, and the Energy
Index showed significant positive excess returns. The cumulative abnormal return of
the other sectors had no significant difference.
On 18 September 2018, the US announced that it would impose a 10% tariff on
US$200 billion worth of Chinese products, and it would take effect on 24 September
2018. Additionally, this tax rate would increase to 25% from 1 January 2019. Only
the Industrials Index had a significant positive excess return that day. After 30 trad-
ing days, there was no significant difference between any sectors and market average
(Table 8).
On 1 December 2018, China and the US reached a consensus to suspend the
imposition of new tariffs on each other’s products. Compared with the market aver-
age, only the Consumer Discretionary Index had a significant positive excess return.
After 30 trading days, the Health care Index showed significant negative cumulative
Table 6. Cumulative abnormal return from 11 July 2018 in the 2nd period.
Sectors 1 3 5 10 20 30
Energy �0.5 �2.33 �2.37 �0.69 6.88 7.44
Materials 0.63 0.35 0.37 1.6 3.45 3.2
Industrials �0.14 �0.59 0.08 1.82� 3.34�� 2.9
Consumer Discretionary 0 �0.47 �0.77 �1.98 �4.3�� �5.99���
Consumer Staples 0.47 1.34 0.66 �1.94 �5.84 �8.66�
Health care 1.07 2.31� 1.49 �6.2�� �10.33��� �11.13���
Financial �0.44 �0.62 �1.9 1.73 3.38 3.87
Information Technology �0.51 �0.14 1.31 �0.27 �2.28 0.47
Telecommunication Services �0.56 0.71 4.07�� 3.15 3.07 10.67��
Utilities �0.53 �0.68 �0.17 3.66��� 6.43��� 5.75���
Source: Wind and authors’ calculations.
Table 7. Cumulative abnormal return from 2 August 2018 in the 2nd period.
Sectors 1 3 5 10 20 30
Energy 0.64 3.89�� 6.94��� 5.88� 5.98 10.05�
Materials 0.7 0.54 1.56 1.04 �0.43 �1.8
Industrials 0.07 0.22 1.01 1.11 �0.42 1.51
Consumer Discretionary �0.94�� �1.73�� �2.38��� �2.75�� �4.74��� �5.57��
Consumer Staples �0.44 �2.28 �3.13 �3.96 �6.51 �9.38�
Health care 1.62�� �1.64 �3.15� �1.56 �2.69 �5.55
Financial �0.7 0.9 1.29 �0.33 3.73 4.72
Information Technology 0.23 0.37 �0.79 2.28 1.91 1.17
Telecommunication Services 0.71 1.18 0.8 5.29� 9.21�� 8.07
Utilities 0.24 1 1.51 �0.15 �0.51 2.05
Source: Wind and authors’ calculations.
3286 H. CHENGYING ET AL.
abnormal returns, the Industrials and Telecommunication Services Index showed sig-
nificant positive cumulative abnormal returns, and the cumulative abnormal returns
of other sectors were not significantly different from the market average (Table 9).
Conclusion
The trade war is a protracted war. This article attempts to reveal how the trade war
affected Chinese listed companies and test whether the trade war effects on the
Chinese stock market was the contagion effect or the present value effect.
In the very short term, the trade war affected the stock market. However, the
impact of the trade war on the Chinese stock market differs in different periods.
Initially, in the trade war, we found that the market’s expectations were unclear, and
some people were even optimistic. However, by imposing more tariffs on the US$50
and US$200 billion worth of products, the market realised that the trade war was real
with long-term trade friction, and the stock market had a larger decline during
this period.
Through an event study, we also found that the announcement day of the trade
war greatly affected the stock market the following day, though the influence on each
industry was different. After the announcement of more tariffs on steel and alumin-
ium, many industries did not respond to the trade war. By the time the tariffs were
imposed on US$50 billion worth of Chinese products, the impact on different indus-
tries became apparent. Some industries had a significantly negative excess returns,
while some industries were sensitive to the start of the trade war.
Table 8. Cumulative abnormal return from 18 September 2018 in the 2nd period.
Sectors 1 3 5 10 20 30
Energy �0.53 0.56 0.72 5.01 1.84 0.18
Materials 0.42 0.41 0.06 1.29 �2.22 �1.94
Industrials 0.65� 0.43 �0.09 �0.29 �1.42 0.65
Consumer Discretionary �0.59 �0.19 0.16 0.31 �0.86 �1.69
Consumer Staples 0.03 0.57 1.53 3.19 2.34 �6.04
Health care �0.01 �0.27 0.06 0.84 �2.04 �1.76
Financial 0.22 0.56 1.24 0.78 5.04 6.75
Information Technology �0.88 �1.82 �3.04 �5.48� �4.04 �4.54
Telecommunication Services �1.56 �2.9� �3.71� �6.24�� �5.34 �6.28
Utilities 0.21 0.06 �0.3 �0.76 �3.27 �0.9
Source: Wind and authors’ calculations.
Table 9. Cumulative abnormal return from 1 December 2018 in the 2nd period.
Sectors 1 3 5 10 20 30
Energy �0.23 �0.62 0.4 0.14 �3.98 �3.75
Materials �0.09 0.43 1.36 1.14 1.04 0.71
Industrials �0.16 �0.03 1.09 1.81 2.28 3.68�
Consumer Discretionary 0.7� 0.42 0.56 1.66 2.49 3.2
Consumer Staples 0.67 1.72 1.47 2.4 4.54 5.2
Health care 0.02 1.55 �3.43 �5.74� �7.58� �13.58��
Financial �0.62 �1.33 �1.22 �1.49 �3.47 �3.49
Information Technology 0.01 �0.79 �0.33 �0.95 �0.04 0.26
Telecommunication Services 1.3 0.54 �0.29 0.64 6.4 9.07�
Utilities 0.22 0.63 1.55 1.72 4.6� 2.79
Source: Wind and authors’ calculations.
ECONOMIC RESEARCH-EKONOMSKA ISTRAŽIVANJA 3287
However, there is no obvious difference between the various industries and the
market trend during the execution period. During the announcement period and the
execution period, when tariffs were to be imposed on US$200 billion worth of prod-
ucts, the impact of the trade war on various industries at all points of time did not
deviate significantly from the market. Therefore, the impact of the trade war on vari-
ous industries has now become a systemic risk.
Additionally, the impact of the announcement of increased tariffs on the US$300
billion worth of products and the suspension of these tariffs on various industries is
not significantly different from the market. The impact of the trade war on the indus-
try is reflected in the systemic risk of the entire market. Therefore, the short-term
impact of the trade war on various industries showed insensitivity to industry differ-
entiation. The overall impact of the trade war on the stock market was the same.
There was no significant difference between sectors. These results suggest that the
effect of the trade war on the Chinese stock market was mainly contagion effect.
Since 2019, China and the US have launched multiple rounds of high-level nego-
tiations and made substantial progress; the two sides have stopped increasing each
other’s tariffs, and the trade frictions were eased but not stopped. The US continues
to blacklist Chinese entities. Under these circumstances, the effect of the trade friction
on the Chinese stock market may be mainly present value effect rather than conta-
gion effect. However, this judgment requires us to analyse listed companies or related
industries on the entity list, which will be our future research direction.
Notes
1. On 18 June 2018, Trump claimed that, on the basis of the previous US$50 billion in
goods, he would impose a 10% punitive tariff on US$200 billion in Chinese goods. The
Chinese Ministry of Commerce responded with counter-sanctions on the US.
2. Wind Information Inc. (WIND) are the largest and most prominent financial data
provider in China. WIND serves 90% of China’s financial institutions and 70% of the
Qualified Foreign Institutional Investors (QFII) operating in China (Liu et al., 2019).
Acknowledgements
Thanks to the anonymous reviewer and editors of Economic Research-Ekonomska Istra�zivanja.
Disclosure statement
No potential conflict of interest was reported by the authors.
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- Abstract
Introduction
Literature review
Research methods
US-China trade conflict: incidents and description
Empirical analysis
1st period: increase tariffs on steel and aluminium
2nd period: increase tariffs on US$50 billion worth of Chinese products
3rd period: increase tariffs on US$200 billion worth of products
Conclusion
Acknowledgements
Disclosure statement
References
2
2
Unit VI Assignment
Cecilia Williamson
Columbia Southern University
Impacts of expansionary and contractionary fiscal policy on the Smartwatch Industry
The current fiscal policy of the Federal Government is focused on reducing the budget deficit and increasing national savings. This is being done through the implementation of austerity measures such as reduced government spending, and reduced government borrowing. This policy has had an overall contractionary effect on the economy and may dampen demand for consumer products (Romer,2021). The current status of the Federal Government budget and fiscal policy will have a significant impact on the smartwatch industry over the next two years. Expansionary fiscal policy, which is when the government increases spending to stimulate the economy, will likely have a positive impact on the industry. Increased government spending can lead to increased consumer demand, which can lead to increased sales of smartwatches and other related products. On the other hand, contractionary fiscal policy, which is when the government decreases spending to slow economic growth, can harm the industry. Lower government spending can lead to decreased consumer demand, which can result in fewer sales of smartwatches and related products.
In the short-term, contractionary fiscal policy will suppress demand for smartwatches and harm the industry. This is because fewer people will have the disposable income to purchase these products, thus reducing sales and profits. Additionally, higher taxes may lead to smartwatch companies having to pay more taxes and reduce their profits. In the long term, however, expansionary fiscal policies such as increased government spending and tax cuts could have an impact on the industry. Expansionary fiscal policy is intended to stimulate economic activity and increase consumer demand, which can lead to increased sales and profits for smartwatch companies. For example, increased public spending on infrastructure or research and development could lead to increased demand for smartwatch products. Additionally, tax cuts could lead to greater disposable income for consumers, which could lead to increased demand for smartwatches.
Fiscal policy, recessionary, and expansionary gaps’ impacts on the smartwatch industry
Fiscal policy is how the government uses its spending and taxation powers to influence the economy. It can be used to close a recessionary gap, which is the gap between what the economy is producing and what it could potentially produce. This occurs when the economy is at less than full employment and the government can close this gap by increasing its spending or cutting taxes. On the other hand, fiscal policy can also be used to close an expansionary gap, which is the gap between what the economy is producing and what it could produce at full employment. This occurs when the economy is operating above full employment and the government can close this gap by decreasing its spending or raising taxes.
The current status of the Federal Government’s budget and fiscal policy will have a significant impact on the smartwatch industry over the next two years. In the event of a recessionary gap, the Federal Government may choose to use an expansionary fiscal policy to stimulate the economy. This could involve cutting taxes, increasing government spending, or both. This would increase consumer demand, which would be beneficial to the smartwatch industry as consumers would have more disposable income to purchase their products. Additionally, the government may also choose to use targeted tax cuts or subsidies to encourage investment in the smartwatch industry, resulting in increased innovation and growth. On the other hand, if the Federal Government is attempting to close an expansionary gap, it may choose to use a contractionary fiscal policy to reduce economic growth (Mankiw,2020). This could involve raising taxes, decreasing government spending, or both. This could harm the smartwatch industry, as consumers would have less disposable income to purchase their products. Furthermore, the government may also choose to use targeted tax increases or regulations to discourage investment in the smartwatch industry, resulting in decreased innovation and growth.
The rationale for budget deficit
The Federal Government budget reflects the overall economic health of the country. It is determined by several factors, including tax revenue received, spending on programs and services, and the amount of money borrowed through federal debt. The budget deficit is the difference between what the government spends and what it collects in revenue. When the deficit is high, the government must borrow money to cover its expenses, which increases the national debt (Stupak, 2019). This can lead to higher interest rates, making it more expensive for companies to borrow money. This could make it more difficult for companies in the smartwatch industry to finance new investments and research, causing a slowdown in innovation and growth. The rationale for budget deficits can be linked to several economic theories. Keynesian economics argues that government spending can help stimulate economic growth and reduce unemployment during periods of economic difficulty (Tevdovski, et al.,2022). Additionally, Keynesian economists supported the budget deficit by arguing that increased government spending can create jobs, increase consumption, and help reduce unemployment. They also believe that budget deficits can help to reduce inequality by providing additional funds for programs to support lower-income households. More specifically, Keynesian economists argue that budget deficits can help to stabilize the economy by providing a buffer against economic downturns. On the other hand, supply-side economics suggests that reducing taxes can lead to increased economic activity and higher levels of investment in the private sector (Lavoie, 2022). In the case of the Federal Government budget deficit, these theories can be used to explain why the government has chosen to increase spending and reduce taxes to stimulate economic growth.
The current federal government fiscal policy will have a direct impact on the smartwatch industry. The government’s budget deficit is a result of increased spending and reduced tax revenue. This means that the government will have to borrow money to fund its operations, leading to an increase in the national debt. This will lead to higher interest rates, which will make it more expensive for businesses to borrow money to fund operations and investments. This could lead to an increase in the cost of production for smartwatch companies, as well as a decrease in their ability to access capital. It is worth noting the smartwatch industry is a unique industry because of the nature of the products produced which are not necessities. As such the demand may not be as high as for the necessities, therefore the demand may further be affected by other macroeconomic factors such as inflation. Therefore, companies in the industry need to pay attention to the current and future fiscal policies to plan accordingly.
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