Exchange Rate Exposure: The Case
of the Chinese Steel Industry
Master Thesis
MSc Economics
by
Daan A. Hendrix
s1554956
Student at the
University of Groningen
Faculty of Economics and Business
Exchange Rate Exposure: The Case
of the Chinese Steel Industry
Daan A. Hendrix
S1554956D.A.Hendrix@rug.nl
JEL codes: F31; F37; F10
Key words: Exchange Rate Exposure, Firm Value, Chinese Steel Industry
Abstract:
This paper investigates the exchange rate exposure of the Chinese steel industry. With the help of an OLS regression model it is tested whether the percentual change of different exchange rates affects the stock returns of the Chinese steel industry, using monthly stock market returns. The time span investigated is the 1st of January 2002 up to the second quarter of 2010. The results obtained indicate that there is a significant influence of the exchange rates on the stock returns.
I.
Introduction
Section 1.1 IntroductionThe world is becoming more global. One can simply order a computer from Thailand, a bike from China or an Ipod from America. With this globalization of markets, new problems arise. One of these problems is the exchange rate exposure for companies due to the use of different currencies. Following Dumas (1978) exchange rate exposure is the effect of a change in the exchange rate on the value of a firm. It is a measure of the correlation between real assets values and real exchange rates. The problem becomes relevant when the firm sells or buys in foreign currency that has to be converted into the domestic currency. The price of conversion is given by the exchange rate. After the breakdown of the Bretton Woods system in 1973 large real exchange rate changes followed (Doidge et al., 2002). Moreover, since the mid 80s, international equity flows increased by 34% per annum (Kanas, 2000). The strong increase in international equity investments led to a higher demand and supply of foreign currency. Since the exchange rate is four times more volatile than the interest rate and ten times more volatile than the inflation rate, this led to a major source of uncertainty for firms (Jorion, 1990). Froot and Rogoff (1994) indicated that the deviations from purchasing power parity due to exchange rate movements have an average half-life time of 4 to 5 years, leading to large movements in profit margins. Exchange rate exposure can influence firms in various ways. A summary about how various disturbances affect the asset prices of a firm is given by Blanchard and Summers (1984). This paper indicates that foreign exchange rate exposure can affect (i) the value of net monetary assets with fixed nominal payoffs and (ii) the value of real assets held by the firm. Earlier papers focused on the American economy. A significant, but small explanatory effect was found. Later on, the focus was more on non-US firms. With this focus on a new set of countries, a higher explanatory effect was found.
First of all, the topic exchange rate exposure is chosen since it is still a major issue in modern economics. On the eight of October 2010 the Dutch financial news program RTL Z even issued warnings about a currency war. The problem is that some countries prevent the domestic currency from appreciating in order to maintain a competitive advantage in global trade. Relevant for this paper is the peg of the Chinese yuan to the US dollar. The US government believes that in this manner the Chinese government prevents the yuan from appreciating to stimulate domestic economic growth. Section 2.1 gives more details about this issue. A second interesting feature of this paper is that it distinguishes itself from others since it only focuses on one industry. In almost all the exchange rate literature written (if not all), a detailed industry analysis is missing. Most papers focus on different industries, although are not able to give detailed background information that is important for explaining the degree of exchange rate exposure. Details about the Chinese steel industry are given in section 2.2. Finally, only a few papers investigate the exchange rate exposure on the Chinese economy (Parsley and Popper, 2006). A remarkable fact, since the Chinese economy is the fastest growing economy with approximately 1.3 billion people. It is therefore interesting to investigate whether the Chinese steel industry is significantly influenced by foreign countries (and which countries)?
The Chinese steel industry is chosen because it is the world largest steel industry and characterized by high export and import volumes. Moreover, it is one of the most important industries for the economy of China. Guo and N’Diaye (2008) even state that the steel industry of China is seen as a benchmark for economic progress because of its critical role in infrastructure and overall economic development. In addition, the steel industry is characterized by strong government involvement. It is interesting to examine the motives for this support. Since the industry is not only domestically but also internationally focused, it is perfectly suited for the purpose of the paper.
at the different variables and examines, based on previous research, how exposure is best measured. The research question of this paper is:
Are the exchange rates of the US dollar, euro, Japanese yen, Korean won, Taiwanese baht, Indonesian rupiah , Hong Kong dollar, Vietnam dong and the Russian ruble of significant influence to the stock return of the Chinese steel industry?
These specific currencies are examined because the corresponding countries are characterized by a high degree of trade with the Chinese steel industry. More details about the relevant trade figures are given later on in table 4 and 5. This paper contains seven main sections. After the introduction, the second section provides more
information about the Chinese currency policy and the general characteristics of the Chinese steel industry. Both are important for understanding how the exchange rate influences the steel industry of China. The third section deals with the literature review of exchange rate exposure. In this section, a distinction is made between the theoretical and empirical results. In the fourth section, the methodology is discussed while the fifth section describes the data in more detail. The sixth section discusses the results obtained from the relationship between the exchange rates and the stock returns of the Chinese steel industry, where we answer the main question whether the exchange rates
investigated significantly influences the stock returns of the Chinese steel industry. Finally, section seven contains the conclusion.
II.
Foreign Trade Policy and the Steel Industry of China
Section 2.1 Currency Policy
third largest importer. However, in the late 2008s China felt the first consequences of the economic crisis resulting in a decrease in real GDP growth from 9.0% in 2008 to 7.1% in the beginning of 2009 (year on year basis) (Morrison, 2009a). To deal with the crisis, the Chinese government implemented a stimulation package of 586 billion US dollar to boost the economy. Chinese policies and economy are a major concern of many foreign policy makers due to its enormous influence on local economies. However, not only in a positive manner. China tries to implement free trade agreements around the world, however especially in Asian countries. This leads to the concerns of China trying to promote a greater Asian trading area. In addition, US policy makers already argued that the Chinese currency policy is violating the commitments of the World Trade
Organization and are harmful for the US economy (Morrison, 2009a).
From 1997 up to 21 July 2005, the Chinese authorities pegged the exchange rate of the yuan to the dollar within a narrow range (Lafrance, 2008). Between 1994 and 2005, the Chinese government pegged the currency to the dollar at about 8.28 yuan. At the 21st of July 2005, China announced to change its currency policy including stopping the peg to the US dollar and pegging the yuan to a basket of different currencies instead. In July 2005, the Chinese government appreciated the yuan with 2.1% to the dollar. However, in order to maintain a target rate with the US dollar, the Chinese government maintained restrictions on capital transactions and made large scale purchases of the US dollar (Morrison, 2009b).
weighted basis and an appreciation of 15 up to 25 percent would be desirable. According to the bank of China the yuan appreciated 18.7% (from 8.11 to 6.83 yuan per dollar) between the 21st of July 2005 up to the 17th of September 2009. In 2009, the Chinese government kept the yuan at 6.83 per dollar, indicating it no longer accepts the currency to gradually appreciate (Morrison, 2009b). Chinese officials argue that its currency policy is not to favor exports over imports, but to foster domestic economic stability.
Section 2.2 The Chinese Steel Industry
Previous research mostly focused on a broad set of countries and industries. However, it is important to know more about the industries examined before making conclusions about possible exposure. Without knowing about regulations, trade flows, developments and so on, one cannot draw decent conclusions. Bodnar and Gentry (1993) focus in their paper on the differences between industry characteristics. The results indicate that exposure differs per industry. Some industries face no exposure, sometimes an appreciation has a positive effect on industry value while others face a negative
relationship. Unfortunately, a well specified analysis of the industries characteristics and the possible effect on exposure is missing. From a theoretical point of view, a net
importing industry goes well by appreciation, while a net exporter is likely to find a negative relationship between the degree of exposure and firm value. Firms broadly focused on the domestic market are likely to face less exposure than an industry with a high degree of international trade. So it is important to know more about a particular industry to explain exposure. This section gives some additional information about the Chinese steel industry which is helpful in explaining the outcomes of this investigation.
steel production. In addition, in the period 2003-2007, China was responsible for 70% percent of the global increase in world steel production. During this period, steel production in China had an annual compounded growth rate of 22%. In contrast, the growth rate of steel production outside China was 3% (Guo and N’Diaye, 2008). China moved from a steel trade deficit of 18.5 billion dollar in 2003 towards a trade surplus of 23.5 billion in 2007. While consumption grew by 14% annually, production increased by 22% per year during 2003-2007. China was also responsible for 58% percent of global steel consumption during 2002-2006 (DeSapio et al., 2008). However, the strong increase in steel production was also due to fulfill domestic demand. Mainly to support new capital investment and infrastructure as well as to support the growth of China’s manufacturing industries. Due to new investments, the sector is now able to produce more value added and higher priced steel products. Foreign sales are high and of real importance for the industry (Guo and N’Diaye, 2008). Based on this information, one can expect a significant degree of exchange rate exposure in the steel industry of China.
investment projects to increase the competitive position of state owned enterprises (DeSapio et al., 2008). Loans were granted by state owned banks at a preferred rate for companies such as the Anshan steel group, Baoshan and Maanshan. Besides, the Chinese government uses taxation to support the steel industry. One could think about tax
exemptions, tax reductions, tax credits or export oriented taxation advantages for companies that export 70% of its production or that purchase domestic rather than imported materials. Additionally, value added tax reductions were refunded by the Chinese government when it was used for capital requirements which were not available in China. In this manner, the government hoped to attract advanced foreign technology and capital equipment. In 2006 and 2007, due to increasing trade tensions with the United States, China agreed to reduce export value added tax rebates.
Despite the fact that the steel sector is subsidized, China is not known as a low cost steel manufacturer. China faces high transport costs of exporting steel to markets such as Europe and the United States. It also faces high transportation costs for importing raw materials such as iron ore from Australia and Brazil, which are needed for the production of steel. Due to the strong growth in domestic demand and relatively increase in net export, the scrap value in China is relatively low. The large amount of iron ore needed for its steel industry means more dependency on import, making the Chinese industry more sensitive to exchange rate movements from a theoretical point of view.
With respect to foreign trade, iron and steel products were relatively the fourth and sixth largest export products in China in 2008. In the same year, iron and steel was the tenth largest import product (Morisson, 2009). The main export markets in 2007 for steel were the European Union (19%), Korea (18%) and the United states (8%). The main import markets in 2007 were Japan, Taiwan, Korea and the European Union, together
One can conclude from the industry analysis that there is a high level of international trade. Both export as well as import is of real importance for the industry. However, also the domestic market is an important sales market for the Chinese steel industry. The amount of government involvement is high, decreasing the degree of competition. This leads to a better competitive position and a higher amount of trade. It is obvious that the Chinese section is not characterized by perfect competition. Is there a relationship
between the high degree of government support and the influence of foreign countries on the latter industry? Due to its enormous size compared to other foreign steel industries, the degree of foreign exchange rate exposure is likely to be considerable. Before testing whether this industry is significantly influenced by foreign currencies, one should know more about exchange rate exposure in general.
.
III.
Literature review exchange rate exposure
Section 3.1 Theoretical Review
Table 1.The effect of an appreciation of the home currency on the value of an industry (Bodnar and Gentry, 1993).
Activity Sign of effect
Non-traded good producer (+)
Exporter (-) Importer (+) Import competitor (-) User of internationally-priced inputs (+) Foreign investor (-)
Exchange rate exposure of a multinational operating firm is thus determined by the proportion of export sales, level of foreign competition and the substitutability between imported and local goods. There are several methods to decrease the amount of exchange rate exposure. High inelastic consumer demand gives the producer the opportunity to pass price changes due to exposure on to consumers (Bodnar et al., 2002). Dumas (1978) accounts for the firm’s responsiveness to exchange rate changes. He focuses on the unique ability of multinational firms to move production from one country to another and thereby reducing their exchange rate exposure. The article of Allayannis and Ofek (1997) shows that the use of foreign currency derivatives can reduce exposure. Adler and Dumas (1984) show how firms can hedge their exchange rate risk. This is best shown with an example:
all alike and domestic currency risk is zero. However, the French inflation rate and consequently the exchange rate are random. An US investor receives 1000 FF in three months. The question is what his exposure will be on the target date three months away? 1000 FF will exactly represent the sensitivity of the future dollar value to variations in the exchange rate. Assume a forward contract that allows the investor to sell the 1000 francs against the forward rate F. Then the pay off, $1000(F-S), is exactly the value needed to shield the dollar from unanticipated variations in the exchange rate. The dollar value of the hedged position remains constant at $1000F independent of the economic state of nature. For an example, see table 2.
Table 2. Exposure of 1000 Franc to be received in the future, with three economic states of nature (Adler and Dumas, 1984).
Future Quantities State 1 State 2 State 3
FF Balance: P* 1000 1000 1000 Exchange rate: S =$/FF 0.25 0.225 0.200 Dollar Value: SP* $250 $225 $200 Proceeds of Forward Sale: $1000(F-S) $1000(F-0.25) $1000(F-0.225) $1000(F-0.20) Dollar Value of Hedged Position $1000F $1000F $1000F
One can see that the amount of exposure is perfectly hedged, so why is the discussion and research on exchange rate exposure still essential in modern economics?
Exposure is not limited to non-traded financial assets or liabilities with fixed nominal foreign currency payoffs on the maturity date of the hedge. The latter is easy to
that date. More difficult are assets or liabilities (physical or financial) whose value in future foreign currency is uncertain. The value may also be sensitive or correlated with exchange rate fluctuations and should indeed be considered exposed. This leads to the notion of exposure as a statistical property what makes it interesting to investigate. The degree on which firms are able to hedge exchange rate exposure is still not clear. Brown (2001) finds that firms hedge for many speculative reasons that are not consistent with financial theory. Bodnar et al. (1998) show that less than half of the payables and receivables are hedged and that most hedges are short term. Guay and Kothari (2003) even argue that in the case of perfect hedging, derivatives positions held by US non-financial firms are only around 1/15th of the size estimated effect on firm market value. It should be noted that all these studies do not account for operational hedges, which are probably more important for mitigating exposure. Despite the latter discussion, it is clear that these hedging activities will decrease the correlation between stock returns and exchange rates. To improve the ability of multinational firms to manage their exchange risk exposure, it is important to understand more about the volatility of stock return due to changes in exchange rate. Previous empirical findings with respect to the relation of stock returns and exchange rate exposure are discussed in the following part.
Section 3.2 Empirical review
As mentioned in section 3.1 theoretical papers, such as Bodnar et al. (2002), predict a significant degree of exchange rate exposure. However, empirical studies often fail to find a strong relationship between stock prices and the exchange rate. They found some economic evidence, but the economic importance is often small.
U.S. multinational firms. He found that a high amount of foreign involvement resulted in a higher degree of exchange rate exposure. In addition, the exchange rate exposure of US firms without a high amount of foreign operations does not appear to differ across
domestic firms.
However, the results of Jorion (1990) are not strong. He found that only 15 of the 287 US multinational corporations investigated faced significant exchange rate exposure during the period 1971-1987. In contrast, for the same period, Bartov and Bodnar (1994) did not found significant evidence for exchange rate exposure on U.S. multinationals. Bartov and Bodnar (1994) reexamined the relation of expected changes in the dollar value and equity in the hope of getting more significant results. With respect to previous research, they tried to deal with the problem of sample selection and only included companies with the same expected exchange rate exposures. Secondly, they tried to deal with mispricing using lagged variables and contemporaneous changes in the US dollar and firm value. Despite their extra effort, they failed to find better results. Possible explanations given by the article were complexity of the relationship between currency changes and firm
performances, liabilities and assets. Goldberg and Knetter (1997) also conclude that local currency prices of foreign products do not fully respond to exchange rate changes. This argument of lagged reaction is contradicted by the article of Doidge et al. (2006), who found that the economic magnitude of the lagged variables is small and overall generally insignificant.
uniformly related to these national figures, which is in their opinion obviously not the case. The third possible reason for the weak relationship could be the fact that firms shield themselves against exchange rate risk.
After the research of Jorion (1990), also non-US firms were investigated. Bodnar and Gentry (1993) compared the US industry with Japan and Canada over the period 1979-1988. They used a model of stock market returns to industry portfolios for Canada, Japan and the USA. Exchange rate changes were examined in relation to industry profitability and value. They found a statistically significant exchange rate effect on common stock returns of 20% up to 35% of the industries investigated. However, a larger effect was found for Japan and Canada than for the United States. Dominguez and Tesar (2001, 2005) found a relationship between foreign activities and exchange rate exposure for eight non US listed firms. In addition, they split the data in three separate subperiods and examine the consistency of the coefficient measuring the exchange rate exposure during these subsamples. In general, they find that the extent of exposure is about the same in the full period as in the three subperiods. So the exposure findings are not driven by a particular sub sample. The article concludes that exposure is correlated with firm size, multinational status, foreign sales, international assets, competitiveness and trade at the international level.
Table 3. Overview of empirical papers dealing with Exchange Rate Exposure. Paper Countries investigated Time Span Exposure found Yes/No Degree of exposure Weak/moderate/ Strong
Factors of influence on the degree of exposure
Jorion (1990) USA 1971-1987 -Yes -Weak -Degree of foreign operations (+) Bodnar and Gentry
(1993) -Canada -Japan -US 1979-1988 -Yes -Yes -Yes -Moderate -Moderate -Moderate/Weak -Import/export ratio (+) -Foreign assets to total assets (+)
-degree trade industry (+) Bartov and Bodnar
(1994)
US 1978-1989 -No -Very weak, only small lagged relation found
-
Dominguez and Tesar (2001,2005)
8 Non US firms 1980-1999 Yes -Strong -Firm size (-)
-Multinational status (+) -Foreign sales (+) -International assets (+) -Competitiveness (+) -trade at industry level (+) Griffin and Stulz (2001) -US
-Canada -Uk -France -Germany -Japan
1975-1997 No -Very weak relation, negligible
-Traded good industry(+)
Williamson (2001) -US -Japan
1973-1995 -Yes -Yes
-Strong -Foreign Sales (+) -Degree of competition (+) -Hedging (-)
-Foreign production domestic country (-) De Jong et al. (2002) -Netherlands 1994-1998 -Yes -Strong -Total assets (+)
-Foreign sales ratio(+) -Foreign loans (-) -Foreign operations (-) Doidge et al. (2006) -18 Countries 1975-1999 -Yes -Moderate -Firm size(+)
The main conclusion of the literature review is that most articles do find some relation between exchange rate changes and the stock value of firms. With respect to the United States, the results are less convincing, probably due to relative closeness of the economy. Empirical research so far failed to find a strong significant relationship. Important factors that do influence the degree of exchange rate exposure are firm size and the level of international sales. Not all results found are of high economic value, mostly due to lack of data or possible hedging strategies of companies. Interesting to see is that, despite the critique of Doidge et al. (2006), the model of Adler and Dumas first used by Jorion (1990) is still leading in most papers. Section 4 explains why the model of Adler and Dumas (1984) is still important.
IV.
Methodology
Section 4.1 The model discussed
Adler and Dumas (1984) show that exposure can be estimated in a linear regression framework. Their article tries to replace the accounting approach with a new technique to measure the economic exchange rate exposure. Following the article, a currency is not necessarily risky because of devaluations. The risk is that the power of a home or foreign currency is different than its anticipated value on a given future date. Adler and Dumas (1984) state that from a financial perspective, a measure of exposure to currency risk should meet the following three criteria to be practical: (i) its measurement should be an amount of currency, domestic for domestic currency risk and foreign for foreign currency risk (ii) from an investor’s viewpoint, it should be the risk on any asset or liability
Adler and Dumas (1984) show in their article that exposure can best be measured with a statistical regression technique. The amount of exposure is the coefficient of the
exchange rate in a multiple linear regression with the asset’s future domestic currency return and the contemporaneous foreign exchange rate(s). As already mentioned, assets or liabilities (physical or financial) whose value in future foreign currency is uncertain may be correlated with exchange rate fluctuations and should indeed be considered exposed. Adler and Dumas (1984) show that the company’s exposure in terms of a certain foreign currency can be measured by the value of the foreign currency future contracts investors must sell in the future to minimize their variance of a mixed hedging portfolio of stocks and currencies. The exchange rate exposure of investors is the slope of the linear regression of the value of foreign cash flows in home currency on the
percentual change of the relevant exchange rate. In this manner, the regression parameter (or slope) is the units of forward foreign currency that must be sold to hedge exchange rate exposure. They show that after minimizing the variance of the hedged position, the residual randomness of the hedged position is independent of the exchange rate. The advantage of measuring exposure with a statistical regression technique is that it decomposes the probability distribution of a risky asset’s domestic currency price at a future instant into two parts, namely one that is correlated with the exchange rate(s) and a second that is independent. The first component can be defined as exposed as its
variability can be removed by hedging. For the second component, residual variability remains but is not exposed as it cannot be further reduced by hedging.
4.3 up to 4.6 the variables for determining the degree of exposure are examined. Finally, section 4.7 describes the model used.
Section 4.2 The basic model
Determining the exposure on industries is a difficult task, since there is still a lot of discussion going on how to exactly measure exchange rate exposure. As previous studies already illustrated, finding results with a high economic value is often complicated and difficult. Not all the data about trade flows and foreign activity is available, there is the difficulty of different exchange rate effects on import and export, firms have the ability of hedging their exposure and so on. This leading to sometimes weak results. In addition, which variables to include and how to weight the latter is still not crystal clear. It all begins with the efficient market hypothesis. The efficient market hypothesis states that changes in the market value of an industry reflects changes in current or expected conditions that are relevant for the profitability of the industry. The exchange rate is added to the market return of the industry to determine an industry exchange rate exposure (Bodnar and Gentry, 1993). As already mentioned above, Adler and Dumas (1984) theoretically showed that exposure is best measured with a linear regression equation. So exposure can be investigated with the following equation:
it s st s i it R R
9 1 0 (1)Where R is the return of the ith company’s common stock and it R is the percentual st
change in the different exchange-rates. The specification is appropriate when changes in the exchange rate and stock prices are in general unanticipated. In the case of a constant expected rate of return on the common stock and a constant expected rate of return on the exchange rate, the intercept will reflect the expected values. In addition, the 0i
rates on stock returns (Jorion, 1990). An alternative specification to (1), which explicitly controls for market movements is:
it mt s st s i it R R R
10 9 1 0 (2)Were R is the rate of return on the market index. Including the return on the market mt
means that the coefficient still reflects the change in returns that can be explained by s
changes in the exchange rate, however after conditioning on the market return. If the coefficient is equal to zero this does not mean that there is no exchange rate exposure, s
but that the exposure of the firm is equal to the exposure of the market. Exposure is thus marginal since it is measured relatively to the market average.
The next step is to shed light on the variables used. As already indicated, previous empirical literature do find a relation but one that is not very strong. From an academic perspective, it is very interesting to examine why there is only a weak relation. Even more interesting and surprising is that Dominguez and Tesar (2005) is one of the only empirical papers that investigate how the amount of exposure is affected by the use of different variables. This paper also focuses more on the use of different variables and hopes to give a better indication of which variables are relevant for explaining the degree of exchange rate exposure.
Section 4.3 Specification Market Return
importance of the latter argument, regression (2) is used for further investigation. The next problem is whether to include an equally weighted or value weighted market return. Empirical tests of the CAPM model typically use the value weighted market index to proxy for the market (Dominguez and Tesar, 2001). Bodnar and Wong (2003) argue that when including the value weighted return it first removes the macroeconomic effects. Secondly, they argue that including the value weighted market return removes the more negative effect of exchange rates on larger companies. The latter companies are more likely to experience a higher negative cash flow reaction to yuan appreciations than other Chinese firms due to a higher degree of international activity. Bodnar and Wong (2003) argue that including the value weighted return would likely bias tests toward finding no exposure. In addition, Dominguez and Tesar (2005) argue that in a world of perfectly integrated capital markets the market return is better estimated by a global portfolio of stocks rather than a national portfolio. Dominguez and Tesar (2001, 2005) show that the difference between using a value or equally weighted market return is negligible. Due to the latter arguments, an equally weighted market return is used for the remaining
analysis.
Section 4.4 Specification Exchange rate
Secondly, this section discusses the best exchange rate to use for investigating the amount of exchange rate exposure. In the empirical literature, not only the exchange rate itself is mentioned, but also the forward premium. Following Bilson (1981), the forward rate is a biased predictor of the future spot rate and does not outperform the contemporaneous spot rate. Jorion (1990) also concludes that the actual variation in the spot rate explained by forward premium is quite small, indicating that most of the actual change in spot rate is unanticipated. From previous papers, it is clear that one should use the exchange rate and not the forward rate.
multilateral exchange rate to avoid the problem of multicollinearity that arises because many exchange rates are fixed with each other. Furthermore, the exchange rates used by Jorion (1990) were trade weighted. Despite the fact that many, if not most, studies use the trade weighted exchange some are sceptic about the variable. Following Williamson (2001), the main shortcoming of using a currency basket of trade weighted exchange rates is the lack of power when companies are only exposed to a small number of currencies. As shown by Dominguez and Tesar (2005), firms within the same industry have very different exchange rate exposure coefficients indicating that one needs very detailed firm specific information to isolate which exchange rate is important. For example, Japan exports the largest fraction of its automobiles to the US, suggesting that the US dollar is the right currency to include in the Japanese exposure regression. If, however, some firms in Japan are specialized with trade to Germany, the only exchange rate exposure found is due to the correlation of the dollar-yen rate with the euro-yen rate. As already mentioned, de Jong et al. (2002) indicate the fact that most studies use trade weighted exchange rate indices derived from national trade figures with foreign
countries. One assumes that individual firms are uniformly related to these national figures, which in most cases does not hold. A possible but difficult solution could be to create firm- and industry specific exchange rates (Dominguez and Tesar, 2005). It is still not clear which exchange rate is preferred.
Section 4.5 Data Frequency
Data frequency is important in determining the degree of exposure in an industry. Several papers use monthly stock returns and show that the amount of exposure is increasing in the return horizon, especially when controlling for macroeconomic and market wide capital-market effects (Bartov and Bodnar, 1994; Allayanis, 1997; Bodnar and Wong, 2003). Following Wan (2006), observations over lower data frequency erase more noise and thus explicitly highlight the basic relations between stock values and exchange rates. So daily data are not suitable for investigating exchange rate exposure since it understates the true extend of exposure. Using semi-annual data one should examine a longer period of time to have an acceptable sample size. However, due to policy changes and structural breaks, using a too long time period makes it more difficult to relate specific
developments to the amount of exposure found (Dominguez and Tesar, 2005). In line with previous papers, this investigation examines monthly stock returns.
Section 4.6 Import and Export effect on direction Exposure
As mentioned in section 3.1, theory predicts a different effect between importing and exporting countries on the sign of the exchange rate coefficient. From a theoretical point of view, an appreciation helps the import sector and hurts the export and import
competing sector. The currency of a country were China mostly imports its steel should have a positive effect on the Chinese stock returns with an appreciating yuan (Bodnar and Gentry, 1993). This investigation uses the percentual change of the yuan with respect to the foreign currency. An appreciating yuan means a decrease in the percentual change. In the case of China importing steel from a country, economic theory predicts a negative sign of the exposure coefficient . In this manner a positive effect is found on the stock s
With respect to the countries investigated, the US, Europe, Japan, South Korea and Thailand both have high import and export numbers. From a theoretical point of view, it is difficult to predict the sign of the latter countries. China is importing a relatively large amount of steel from Russia, so the predicted sign of the beta coefficient in the case of an appreciating yuan is negative. On the other hand, China exports a relatively high amount of steel to Indonesia, Hong Kong and Vietnam. The sign of the latter beta’s in the case of an appreciation of the yuan is expected to be positive. The import and export numbers are given in table 4 and 5, respectively. Before testing the possible import and/or export effect, the final model used is shortly discussed in section 4.7.
Table 4. Steel product import of the Chinese steel industry in tonnes per country
2002 2003 2004 2005 2006 2007 2008 2009 2010 Europe 1,045,523 2,068,017 1,828,247 1,390,629 1,166,320 1,064,194 929,340 799,197 472,271 US 70,992 875,795 191,579 220,005 115,210 149,294 217,059 212,580 79,161 Japan 7,640,182 7,601,145 8,120,390 6,432,012 6,795,520 6,883,651 7,118,988 6,395,341 3,959,378 Taiwan 5,045,236 6,088,523 4,922,889 4,936,898 3,967,448 3,515,560 2,581,016 2,784,071 1,223,777 South Korea 3,344,009 5,263,944 5,011,429 4,504,996 3,902,320 3,687,024 3,603,124 4,774,501 2,169,166 Russia 3,975,223 5,028,320 2,905,651 2,662,652 538,159 183,499 173,091 2,589,965 198,849 Hong Kong 30,086 36,992 35,624 21,982 22,361 53,436 20,508 22,252 12,006 Indonesia 149,380 315,793 194,248 102,077 52,737 12,455 5,072 5,426 326 Vietnam 1661 8298 4217 598 4625 10277 4715 4633 49369
Table 5. Steel product export of the Chinese steel industry in tonnes per country
Section 4.7 The final model
Due to the previous discussion about the different variables to use, the following two models are examined. Including the equally weighted market return and the nominal weighted exchange rate, equation (3) is used:
it mt s st s i it R R R
10 9 1 0 (3)Including the equally weighted market return and the nominal trade weighted exchange rate, equation (4) is used:
it mt wt i it R R R 0 1 2 (4)
Where R is the return of the ith company’s common stock and it R is the equally mt
weighted rate of return on the market index.R is the percentual change in the different st
nominal exchange-rates and R is the percentual change in the nominal trade weighed wt
exchange rate obtained from JP Morgan. Based on the discussion of the relevant literature, the following null-hypothesis and alternative hypothesis are tested:
0
H : the exchange rate i does not affect the stock returns of the Chinese steel industry.
1
H : the exchange rate i does affect the stock returns of the Chinese steel industry.
Note that i equals the US dollar, euro, Japanese yen, Korean won, Taiwanese baht, Indonesian rupiah, Hong Kong dollar, Vietnam dong and the Russian ruble.
V.
Data Description
Section 5.1 Data
The sample includes monthly data of stock returns of China listed steel companies and only working days are examined. The time span investigated is the first quarter of 2002 till the second quarter of 2010. As already mentioned, monthly data are examined since most papers agree on the fact that the data frequency affects the chance of finding significant results (Bartov and Bodnar, 1994; Allayannis, 1997; Bodnar and Wong, 2003). The data of the market return is gathered from BRIC DS Steel price index. The yearly import and export numbers of the Chinese steel industry are obtained from ISSB ltd. The nominal exchange rates are obtained from datastream, with the Chinese bank as source. The nominal trade weighted exchange rate is obtained from datastream with JP Morgan as source (2000 =100). The trade weighted exchange rate measures a home good price relatively to the average price of goods of trading partners, using the share of trade with each country as weight for that country. A higher index means that the purchasing power of the yuan is higher. A lower index means that the yuan depreciates. Appendix B and C show the descriptive statistics of the Chinese steel companies examined. Appendix D shows the descriptive statistics of the percentual change in nominal and trade weighted exchange rates. In the formula, the percentual change of exchange rate is used, with the change in value of the Chinese yuan relatively to the foreign currency. The latter is calculated with the following simple formula:
To compare the descriptive statistics of the world index with the indices of the Chinese steel companies, the simple return is calculated of the price indices using the following formula1: 1 1 t t t P P P (6)
First, one can conclude from appendix B that the average return on the market is higher than the average return of the Chinese steel companies. The second interesting conclusion is that the standard deviation of almost all Chinese steel firms2 relatively to the market return are higher, indicating a higher variance for the Chinese steel industry returns relatively to the market return of steel. Finally, the mean of the change in the exchange rate is important to examine. The mean of the euro, Taiwan baht and the Japanese yen are all positive. This indicates that on average the yuan depreciated against these currencies. On the other hand, the mean of the US dollar, South Korean won, Russian ruble, Hong Kong dollar, Indonesian rupiah and the Vietnam Dong are all negative, indicating that the Chinese yuan appreciated against these latter currencies during the time span
investigated. With respect to the nominal trade weighted exchange rate, a positive mean is found. So on average, the yuan appreciated against the trade weighted basket of currencies. Next, it is tested whether the data is stationary. Table 6 shows the results of the unit root test with and without a trend.
1
Note that this is only calculated to compare the descriptive statistics, in the formula discussed stock returns are used and not the percentual change of these stock returns.
2
Table 6. Unit Root Test Stock Returns
Unit Root Test With Intercept With Intercept&Trend Statistic Probability Statistic Probability
Levin, Lin & Chu -3.04593 0.0012 -2.06488 0.0195
Im, Pesaran and Shin W-stat -5.14352 0.0000 -3.69835 0.0001
ADF Fisher Chi-square 97.3889 0.0000 76.4818 0.0009
PP Fisher Chi-square3 76.2996 0.0009 43.1321 0.4227
For the listed Chinese steel firms investigated the null hypothesis of a unit root without and with a trend is not accepted at a 5% significance level, with the exception of the PP Fisher Chi Square test4. From these results one can conclude that the data of the Chinese steel companies are stationary. In table 7, the results of the unit root test with respect to the world market return of steel are shown.
Table 7. Unit Root Test Market Return
Unit Root Test With Intercept With Intercept&Trend Statistic Probability Statistic Probability
Augmented Dickey-Fuller Test
-1.140896 0.6972 -2.932569 0.1570
Constant 6.979236 0.1290 0.954851 0.8623
Trend - - 0.369427 0.0252
The Augmented Dickey Fuller test indicates there is a unit root and therefore non stationary. In addition, the trend included is significant. Nonetheless, the graph of the market return of steel illustrated in appendix G shows a break resulting in reduced power
3
Probabilities for Fisher tests are computed using an asymptotic Chi-square distribution. All other tests assume asymptotic normality.
4
of the augmented Dickey-Fuller test. This, together with the fact that the world market return of steel is bounded, one can make the assumption of a stationary world market return. So due to the low explanatory power of the Augmented Dickey Fuller test the market return is not adjusted for non stationarity. Furthermore, the trend included is significant. Therefore, the OLS is performed with and without a trend.
VI.
Results
Section 6.1 Results
Table 8. Regression Results Nominal Exchange Rate5 Dependent variable:
Chinese steel Returns
Coefficient without trend Coefficient with cross section trend
Constant 3.352587*
(0.409818)
4.099029*
(0.010916)
Exchange rate US Dollar -9.407619
(14.92473)
39.26925**
(16.82201)
Exchange rate Euro 14.01763*
(1.970266)
9.932245*
(1.787410)
Exchange rate Yen 5.731729*
(0.532065)
6.720372*
(0.623451)
Exchange rate Won -3.709205*
(1.141412)
-2.711587**
(1.068184)
Exchange rate Baht -5.942012*
(1.433804)
-1.790750***
(1.066628)
Exchange rate Russian Rubles
1.591945**
(0.643641)
-2.959743*
(0.834194)
Exchange rate Hong Kong Dollar
-95.32401*
(14.23431)
-41.72506*
(12.07487)
Exchange rate Vietnam Dong
26.23265*
(5.982417)
4.745065
(6.106626)
Exchange rate Indonesian Rupiah
0.926420
(1.732398)
0.036410
(1.679498)
World market return 0.014583*
(0.002356) 0.024823* (0.002284) Common Trend - -6 Included observations Adjusted R-squared S.E. of regression 102 0.449943 2.701332 102 0.589263 2.334298
Note: The standard errors are in the parenthesis
*Significant at 1% level **Significant at 5% level ***Significant at 10% level
5
Table 9. Regression Results of the Nominal Trade Weighted Exchange Rate7 Dependent variable:
Chinese steel Returns
Coefficient without trend Coefficient with cross section trend
Constant 3.284675*
(0.413616)
4.141169*
(0.018419)
Trade Weighted Exchange rate
-19.08170*
(1.468638)
-15.75473*
(1.331166)
World market return 0.015698*
(0.002456) 0.024796* (0.002658) Common Trend - -8 Included observations Adjusted R-squared S.E. of regression 102 0.436259 2.734727 102 0.586215 2.342943
Note: The standard errors are in the parenthesis
*Significant at 1% level **Significant at 5% level ***Significant at 10% level
In the introduction, there are three main reasons for writing this paper from a practical perspective. Section 6.2 examines the first reason, namely whether the Chinese economy is significantly influenced by foreign countries. Secondly, the relationship between industry characteristics and the degree of exchange rate exposure is further discussed in section 6.3. Thirdly, the reasoning behind the Chinese currency policy and the amount of exposure is examined in section 6.4. Finally, the methodology and results are discussed in section 6.5. So what can be concluded from the results obtained?
Section 6.2 China and Exchange Rate Exposure
First of all, it is interesting to answer the question whether the Chinese economy is significantly influenced by foreign countries. With respect to the Chinese steel industry, one can conclude from table 8 that the returns of the Chinese steel industry are strongly
7
influenced by the nominal exchange rate of foreign countries. The main countries
influencing the steel industry of China, at a 5% significant level with a trend9, are the US, Europe, Japan, South Korea, Russia and Hong Kong. It is also shown that it is important to choose the relevant exchange rates carefully in order to find exposure. For instance, there is no strong degree of exchange rate exposure found for the Indonesian rupiah, Vietnam dong and the Taiwan baht. Two-third of the currencies investigated show significant influence on the stock returns of the Chinese steel industry. Furthermore, the values of the adjusted R squared are relatively high compared to previous papers (Jorion, 1990; Bartov and Bodnar, 1994), meaning that the explanatory power of the market index and the exchange rates on the investigated stock returns is high.
Not only the nominal exchange rate but also the trade weighted exchange rate is
investigated. This regression is also characterized by a relatively high adjusted R square. Despite the critique of de Jong et al. (2002), who argue that weak results are found due to using a value weighted exchange rate, table 9 shows a significant effect of the trade weighted exchange rate on the stock returns of the Chinese steel industry. At a 1% significance level, a high negative relationship is found. As previously mentioned, an increase of the trade weighted index means a higher purchasing power of the yuan
(appreciation). A lower index indicates that the yuan depreciates. So from table 9 one can conclude that an appreciation of the yuan leads to a decrease in the Chinese stock
returns10. The relation found of the yuan with respect to the investigated stock returns in table 9, is in line with the effect of exchange rate changes in the euro, US dollar and the Japanese yen in table 8 (including a trend). Since the most important nominal exchange rates for the domestic steel industry are investigated in table 8, it is most likely that the euro, US dollar and the Japanese yen are of significant influence for determining the trade weighted exchange rate.
9
The unit root test of the market return shows that the included trend in table 7 is significant. In addition, including a trend increases the adjusted r-squared. Therefore, the conclusions of this paper are based on the model with a cross section trend.
10
Section 6.3 The Chinese Steel Industry and Exchange Rate Exposure
The second interesting question is how the degree of exchange rate exposure relates to the industry characteristics of the Chinese steel industry. One of these characteristics is the enormous amount of import and export volumes. Firms broadly focusing on the domestic market are likely to face less exposure than an industry with a high degree of international trade. One can conclude from table 8 that there is indeed a high degree of exchange rate exposure in the Chinese steel industry. Besides, the involvement of the Chinese government is also important for explaining the degree of exchange rate exposure. As already mentioned, the industry receives subsidized discounted electricity rates, subsidized iron ore and support to decrease production costs and to become more competitive. Furthermore, the Chinese government implemented a policy of capital consolidation as part of the 11th Chinese five year plan in 2005. Through mergers and transfer of ownerships a smaller number of large, internationally competitive steel producers were created. Steel production in China had an annual compounded growth rate of 22% during the period 2003-2007. In contrast, the growth rate of steel production outside China was 3% (Guo and N’Diaye, 2008). The Chinese steel sector is able to maintain and improve its competitive position due to the enormous amount of
government support. It is therefore able to produce more value added and higher priced steel products, which is necessary to maintain the position of top steel producer in the world. With the domestic government support, the world steel industry is characterized by unequal competition. In this manner, the Chinese steel industry is able to expand their amount of foreign trade. A higher amount of foreign trade means an increase in exchange rate exposure.
Section 6.4 The Chinese currency policy and Exchange Rate Exposure
weighted basis and an appreciation of 15 up to 25 percent would be desirable. Chinese officials argue that its currency policy is not to favor exports over imports, but to foster domestic economic stability. With the results obtained, one can seriously take in doubt the reasoning of the Chinese government to maintain its currency policy. Table 8 shows that especially currency changes in the US and the Hong Kong dollar influence stock returns in the Chinese steel industry11. Since the Chinese steel industry is particularly influenced by the US dollar rate, one can conclude that exposure does not appear to diminish due to an exchange rate peg. Only focusing on the US dollar currency and the Chinese yuan, a strong appreciation of the Chinese yuan would mean a significant decrease in stock returns of the domestic steel industry. The latter result give a possible explanation for why the central bank of China is still increasing its pressure on financial institutions to offer more sophisticated and less expensive instruments for exchange rate risk reduction (Lafrance, 2008). Given the discussed consequence, it is unlikely that the Chinese central bank will change its “go-slow” policy in the near future and appreciates its domestic currency.
Section 6.5 Methodology and Exchange Rate Exposure
With respect to the methodology, there are also several uncertainties. For instance, is there a clear impact of import and export on the exposure coefficient? Purely focusing on the direction of trade, one should expect the exposure coefficient of Hong Kong to be positive. Earlier research explains that it is hard to determine the direction of the exposure coefficient, since it is also influenced by other variables. Former research (Flood and Lessard, 1986; and Hekman, 1985) state that corporate cash flow sensitivity to exchange rate fluctuations is determined by the extent in which it import and exports, but also by its involvement in foreign operations, currency domination of its competition, and the competitiveness of its input and output markets. Wan (2006) indicates that these factors may as well influence the degree of exchange rate exposure. This could indicate
11
why the coefficient has a different sign than was expected focusing on the import and export statistics. Moreover, due to the globalization of production chains and financial markets, the foreign exchange rate exposure is becoming less predictable and the general rules may well deviate from reality (Wan, 2006). Dominguez and Tesar (2005) also show that the direction of exposure varies over time, indicating that firms dynamically adjust their behaviour in response to exchange rate risk. Allayannis et al. (2003), found evidence of large net foreign liabilities for East Asian firms. Due to the latter arguments, it is not surprising that no import/export effect is found on the exposure coefficients.
VII.
Conclusion
Section 7.1 Conclusion
This investigation distinguishes itself from previous research in several ways. First, it investigates exchange rate exposure related to the Chinese economy. Since relatively little empirical investigations deal with exchange rate exposure in China, it gives new and refreshing insights. This paper shows that there is a significant amount of exchange rate exposure found in the Chinese steel industry. Secondly, this paper shows that it is important to not only investigate whether there is exposure but also how it is related to the industry characteristics. Most important to notice is that the steel industry is
From an academic perspective, the paper gives a better overview on how exchange rate exposure is best measured. Since the exact methodology is still not clear, important progress can be made. Previous research, with the exception of Dominguez and Tesar (2001, 2005), is not aware of the importance of the variables used. For instance, it is argued that if one uses a value weighted market return the macroeconomic effects are removed. In this manner, results are biased towards finding no exposure. The latter could be one of the reasons why previous empirical literature found such weak results. De Jong et al. (2002) argued that weak results were found due to using a value weighted exchange rate, since these are based on national trade figures. De Jong et al. (2002) state that using the latter rate decreases the possibility of finding significant returns. This paper showed that with the nominal exchange rate as well with the nominal trade weighted exchange rate significant exchange rate exposure is found.
Section 7.2 Limitations and Recommendations
Despite the contributions of this paper, already discussed in previous sections, I also have to acknowledge several key limitations. The first limitation is that, due to the limited availability of stock returns in datastream, this investigation only examines 21 Chinese steel companies. Including more firms would improve the explanatory power of the paper. The second limitation is the unavailability of the description of how the trade weighted exchange rate of JP Morgan is exactly measured. Consequently, it was not possible to give currency specific conclusions based on the trade weighted exchange rate. A possible extension for further research is the focus on the determinants of exchange rate exposure in the Chinese steel industry. This paper already showed that industry characteristics are important for explaining the degree of exposure. However, due to data limitations, this paper did not focus on how firm specific characteristics are related to the degree of exposure found. One could also examine why and particularly how results change when using different time horizons (daily/monthly/quarterly/semi-annual), exchange rates (bilateral/trade weighted/nominal) and market returns (equally/value weighted). Finally, further research could focus on the degree of exposure in other
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Appendix A
Table 10. Description, abbreviation and Market Value of Chinese Steel Companies (Datastream Thomson)
Company Market Value in
Millions (currency: Yuan) 27-8-10
Company profile
ANGANG STEEL 'A' (Ang)
49868.43 Angang Steel Company Limited. The Group's principal activities are producing and selling
steel products, which include hot hot rolled sheets, cold rolled sheets, galvanised steel, seamless tubes, wire rods, thick plates and large section steel products.
ANYANG IRON & STEEL 'A' (Any)
8832.7 AnYang Iron & Steel Inc. is principally engaged in manufacture and distribution of iron and steel products. The Company provides plates, construction materials and profiles. It distributes its products in domestic and overseas markets.
BAOSHAN IRON & STL.'A'
(Bao)
110676.1 Baoshan Iron & Steel Co., Ltd. is principally engaged in the manufacture and trading of steel products. The Company's major products fall into three categories: carbon steel, stainless steel and special steel.
BEIJING SHOUGANG 'A' (Bei)
13052.71 Beijing Shougang Co., Ltd. is a China-based company principally engaged in smelting,
rolling and processing of iron and steel products. The Company operates its businesses through metallurgy, chemical, construction materials and electronics production.
BENGANG STEEL PLATES 'A' (Ben)
15896.15 Bengang Steel Plates Co., Ltd. is principally engaged in the smelting of steel and the rolling and processing of steel plates. The Company primarily provides steel plates, steel billets and other products.
HUNAN VALIN STEEL 'A' (Hun)
12045.65 Hunan Valin Steel Co., Ltd. is a China-based company primarily engaged in smelting,
manufacture and sale of iron and steel products, as well as nonferrous metal products. The Company provides wide and heavy steel plates, hot-rolled steel plates and cold-rolled steel plates.
INNER MONGOLIA BAOTOU STEEL UNION 'A' (Inn)
21583.43 Inner Mongolia BaoTou Steel Union Co.,Ltd is a China-based company primarily engaged
in steel industry. The Company provides steel pipes, steel plates, steel profiles, steel wires, steel rods and steel billets.
JIANGSU FASTEN 'A'
(Jia)
2148.77 Jiangsu Fasten Company Limited is primarily engaged in the manufacture and sale of metal
products, optical communications products and new-type pipe products.
LAIWU STEEL 'A' (Lai)
7783.98 Laiwu Steel Corporation is principally engaged in the manufacture and sale of pig iron,
steel billets, steel ingots, steel products, coke and granulating slag and the supply of electricity and heat.
MAANSHAN IRON & STL. 'A'
(Maa)
20708.08 Maanshan Iron & Steel Company Limited. The Group's primcipal activities are
manufacturing and selling iron and steel products. The manufacturing process primarily involves iron-making, steel-making and steel rolling projects.
NANJING IRON & STEEL 'A' (Nan)
6537.02 Nanjing Iron & Steel Co., Ltd. is principally engaged in smelting and processing of ferrous metal. The Company's major products are iron and steel products, including steel bars, steel strips, steel medium plates, steel medium and heavy plates.
SHANXI TAIGANG STL. 'A'
(Sha)
Table 10. Description and Market Value of Chinese Steel Companies (Datastream Thomson) continued WUHAN IRON AND
STEEL 'A' (Wuh)
36760.93 Wuhan Iron and Steel Company Limited. The Company's principal activities are
manufacturing and selling of cold-rolled carbon steel and silicon steel products.
DAYE SPC.STEEL 'A'
(Day)
5896.23 Daye Special Steel Co., Ltd. is principally engaged in steel smelting, steel rolling, metal remanufacture, the processing and the inspection of steel materials.
FUSHUN SPECIAL STEEL 'A' (Fus)
3198 Fushun Special Steel Co., Ltd.. The Group's principal activities are the smelting and
processing of special steel products and its materials.
GUANGZHOU IRON&STEEL 'A' (Gua)
4848.92 Guangzhou Iron & Steel Co., Ltd.. The Group's principal activities are manufacturing and selling of steel and iron products. Other activities include manufacturing and processing metallurgy products, coke chemical products, gases and furnace materials.
HANGZHOU IRON & STL. 'A'
(Han)
4387.64 Hang Zhou Iron & Steel Co., Ltd. is principally engaged in the production and sale of iron, steel and related rolled products, as well as coke and its byproducts.
HEBEI IRON & STEEL 'A' (Heb)
27094.5 Hebei Iron and Steel Co., Ltd is principally engaged in smelting, processing and distribution of iron and steel products. The Company's major products are iron, steel, steel materials and vanadium slag.
LINGYUAN IRON & STEEL 'A'
(Lin)
5949.61 Lingyuan Iron & Steel Co., Ltd.. The Group's principal activities are manufacturing, operating and developing metallurgy product and its by-product. It offers steel bars, welded steel pipes, mid-wide hot rolled steel strips and mid-wide cold rolled steel.
NINGXIA HENGLI STEEL A (Nin)
1567.14 Ningxia Hengli Steel Wire. The Group's principal activities are the manufacture and sale of
steel wires, steel wire ropes, steel stranded wires, scrap steel, coal and related products.
XINYU
IRON&STEEL 'A' (Xin)
