1
Foreign Exchange Rate Exposure of
Chinese Firms
S2265591
Yu Shi
MSc IFM
University of Groningen, Netherlands
Abstract
This paper researches the exchange rate exposure of Chinese firms over the last decade when the currency reform took place. An investigation of the relationship between firms’ stock returns and exchange rates movements reveals that most Chinese firms are significantly exposed to exchange rate exposure. Results provide support for previous studies that proved the existence of size and sign asymmetries in firms’ exchange rate exposure. Lagged responses are discovered, suggesting a
sluggish response of stock returns to movements of exchange rate. Industrial level evidence suggests that exporting industries are sensitive to the exchange rate fluctuation, with firm value negatively correlated with the appreciation of home currency.
2
1. Introduction
Exchange rate movements can have influences on the value of multinationals through affecting the expected future cash flows. Obviously, the estimate of a firm’s exchange rate exposure is of great interest to investors seeking to hedge their portfolios and to corporate managers making risk management decisions. Previous work on the exposure of firms to exchange rate risk has primarily focused on U.S. firms and, surprisingly, found that stock returns were not significantly affected by exchange rate fluctuations, which has created intensive debate about this puzzle of currency
exposure. More recent studies have increasingly drawn attention to the emerging countries, which provides main inspiration for this paper to continue to look for exchange rate exposure in Chinese market.
China’s exchange rate policy has experienced important changes in recent years. Since 2000, China's economy has remained high and maintained a steady growth. During this period, the exchange rate of RMB mainly exhibits two trends. The first phase, which dated from 2000 to 2005, is a fixed exchange rate, which is in
accordance with China’s long-held policy of pegging its currency to the US dollar. The second phase from 2005 till now has been through two foreign exchange reforms: China announced on 21 July 2005, and reiterated on 19 June 2010, the adoption of a managed floating rate system. Under the new regime, the exchange rate of the Chinese currency, the renminbi (RMB), is administered by the government but allowed to move within a fluctuation band specified by the authority (PBOC 2005, 2008). Unlike a true floating exchange rate, the RMB would be allowed to fluctuate
by up to 0.3% (later changed to 0.5%) on a daily basis against the basket1. Since this
change, China has moved gradually towards greater exchange rate flexibility, and the
1
3
renminbi has followed a trend of appreciation through a series of moderate rate changes.
This special development of Chinese currency raises questions in respect of what effects this may yield. Answers to such a question are provided in some recent studies that investigated the sensitivity of Chinese firms to exchange rate changes. The main focus is on the relationship between exchange rate changes and firm value. Zhao (2010) explore the relation between exchange rates and stock price in China. Aggarwal, Chen and Yur-Austin (2011) examine the relation between stock returns and currency risk. This paper aims to analyze the foreign exchange risks faced by listed Chinese companies, research the relationship between exchange rate
movements and stock returns of Chinese firms for the last decade and evaluate the effects of foreign exchange reform. Investigation of Chinese firms’ exchange rate exposure can provide critical input to the assessment of the desirability of China’s currency reform. There have been different opinions on China adopting a new regime of flexible exchange rates. Many expressed the fear that currency appreciation may undermine China’s export-oriented economy. This research offers empirical evidence on the effects of RMB appreciation and contributes to the debate on China’s exchange rate policy.
4
rates changes due to hysteretic behavior, pricing-to-market and hedging behavior (Koutmos and Martin, 2003). I also classify my sample firms into different industries to test the asymmetric and lagged effects in each industry,
Results from this study suggest that most of Chinese listed firms have significant exchange-rate exposure to the trade-weighted exchange rate after the currency reform which abolishes the pegging system. Some evidence of time-varying exposure is discovered. In the sub-period analysis, almost all the Chinese industries have significant exposure to trade-weighted exchange rate movements in the second sub-period, during which the pegging system was removed and large appreciation occurred. Size and sign asymmetries in exposure are detected for both trade-weighted exchange rate and bilateral exchange rate with major trading partners.
The rest of the paper is organized as follows. The next section summarizes the existing literature related to exchange rate exposure. The third section introduces the research method, and the fourth section contains data and sample description. The empirical results together with analysis are reported in section five, and the last section concludes the paper.
2. Literature review
5
the meantime, the price of imported products escalates in terms of home currency, which squeezes the profit margins of importing firms (Bodnar and Gentry, 1993). A considerable amount of literature has theoretically estimated exposures at the firm or industry level, and investigated the determinants. For instance, Jorion (1990) investigated the exposure of a sample of 287 US multinationals to foreign currency risk and showed that the foreign sales ratio, which represented the degree of
involvement in foreign operations, is a significant determinant of exchange-rate exposure. He and Ng (1998) estimated the exposure to exchange-rate fluctuations of 171 Japanese multinational firms via regression, including the lagged effects of exchange-rate fluctuations and the impacts of hedging. They also looked at the determinants of exchange-rate exposure and found that estimated exposure is
positively related to the ratio of foreign sale/total sales and negatively correlated with the use of currency derivatives for hedging. Levi (1994) supports these ideas by showing that the main impact on the value of a multinational firm is the profitability of sales in the foreign country. Furthermore, Allayannis and Ihrig (2001) and Bodnar et al. (2002) investigated the role of market structure as a determinant of exchange rate exposure. Allayannis and Ihrig established the link between exchange rate exposure and markup, and estimated their model on US manufacturing industries. And they found a negative relationship between industry's markups and exchange rate exposure. Bodnar, Dumas, and Marston (2002) related an exporting firm’s exchange rate exposure with the extent to which it passes through exchange rate changes to prices, and estimated their model on Japanese export industries. Griffin and Stulz (2001) and Williamson (2001) examined the industry level competitive effects of exchange rate movements. Griffin and Stulz found that these effects were small for a broad set of industries in Canada, France, Germany, Japan, the UK, and US, while Williamson found stronger effects for the automotive industry in Japan and United States. Moreover, Muller and Verschoor (2006) examined 817 European
6
significant inverse relation between firm size and exchange rate exposure among US firms. He and Ng (1998) also showed that foreign exposure is found to increase with firm size. Their empirical evidence suggests that keiretsu multinationals are more exposed to exchange-rate risk than non-keiretsu firms.
7
force through which firms may reduce currency exposure, either through financial instruments or through real operations such as product sourcing and international business allocation. Bartram et al. (2009) found that for a typical sample firm, pass-through and operational hedging each reduce exposure by 10% to 15%. This argument is supported empirically in a number of studies (Allayannis and Ofek, 2001; He and Ng, 1998; Muller and Verschoor, 2007). Allayannis and Ofek’s studies show that a firm's exchange rate exposure is positively related to be ratio of foreign sales to total sales, and negatively related to its ratio of foreign currency derivatives to total assets. He and Ng (1998) explained that the extent to which a firm is exposed to exchange-rate fluctuations can be explained by the level of its export ratio and by variables that are proxies for its hedging needs, such as debt ratio, quick ratio, and book-to-market ratio etc., since highly leveraged firms, or firms with low liquidity, tend to have smaller exposures.
Additionally, Bartov and Bodnar (1994) recognize that one of the complexities that may have caused past studies to fail to appropriately identify the relationship between exchange rates and stock prices is the asymmetries in the impact of appreciations and depreciations. Asymmetric exposure is implied in theoretical models purporting to describe actual corporate behavior, such as, pricing-to-market (e.g. Marston, 1990; Knetter, 1993), hysteresis (e.g. Ljungqvist, 1994), and asymmetric hedging (e.g. Koutmos and Martin, 2003). Knetter (1993) argues that pricing-to-market (PTM) will result in asymmetric reactions to exchange rate changes if firms attempt to build market share, or face capacity constraints and quantitative restrictions. Koutmos and
Martin (2003) conclude that pricing-to-market behavior and hysteretic behavior
8
firm may, for instance, decide to allow its markup to absorb the effect of small changes in currency movements, which leads to a low pass-through effect. In contrast, large exchange rate fluctuations may force the exporter to deviate from his policy and pass-through part of the currency change into export prices. The fact that pass-through is generally positively related to the size of the change in exchange rates (Pollard and Coughlin, 2003) shows that the impact of currency movements on firm cash-flows depends on the magnitude of these movements and tends to confirm the asymmetric
currency exposure hypothesis.Nevertheless, hysteretic behavior is strongly dependent
on the magnitude of currency fluctuations. Baldwin and Krugman (1989) show that in particular large exchange rate shocks may lead to entry or exit decisions that are not reversed when the currency returns to its previous level. On the other hand, small currency depreciations may not lead firms to extend their exporting activities to new markets, while sufficiently large appreciation swings in the domestic currency may
induce many of the new entrants to leave the new markets. Another argument
9
domestic currency appreciation as bad news which would result in a strong reaction in stock returns. On the other hand, domestic depreciations may not get as much consideration from investors. But, if the home depreciation gets large, the probability that investors pay attention to this favorable shock increases, leading to a positive valuation effect. It is, however, still possible that the positive news from the exchange rate market strengthens the future expected volatility – the volatility feedback effect – which in turn increases the required rate of return on the stock and hence lowers the stock price. This effect would, thus, dampen the positive impact of large favorable exchange rate shocks on firm value. In contrast, the increased expected volatility caused by large unfavorable currency movements might similarly increase the required rate of return and lower the stock price, amplifying the negative impact of bad exchange rate news. To test the sign and size asymmetries of exposure, Di Iorio and Faff (2001), Muller and Verschoor (2006) have constructed dummy variables to detect divergent responses of firms towards exchange rates variations and found significant result in developed markets such as the United States, Japan, and Germany.
Another strand of the empirical literature has been focused on modifying
measurements of variables in the regression model. Particularly, exchange risk factors and market risk factors, two major variables in the estimation models, are of great interest to researchers. On exchange risk factors, discussions are centered on choosing between the bilateral and trade-weighted exchange rates and between the real and nominal exchange rates (Muller and Verschoor, 2006). A lot of studies use the
10
economic exposure may underestimate corporate exposures by omitting variables needed to capture the divergent movements in currency values. On the other hand, several studies use the bilateral exchange rate of dominant trading currencies (Jorion, 1990; Dominguez and Tesar, 2001), an obvious shortcoming of which is the failure to capture a firm’s exposure to any excluded currencies. Given the inadequacy of both specifications of exchange risk factors, some recent papers use firm-specific exchange rate instead. Ihrig (2001) creates a trade-weighted exchange rate specific to each multinational corporation (MNC) considered. The currencies included are from those countries where MNCs have foreign subsidiaries. Subsequently, more firms are found to be exposed to exchange rate variation. In the same vein, Muller and Verschoor (2006) use six region-specific exchange rate indices, according to where the company has real operations, and find consistent results. With regard to choosing between the real and nominal exchange rate, Muller and Verschoor (2006) suggest that while according to economic theory the real exchange rate should be used, in practice the nominal exchange rate is widely employed. The co-movement of the nominal exchange rate with the real exchange rate justifies the practice (Bodnar and Gentry, 1993; Griffin and Stulz, 2001). By comparing real and nominal exchange rates, Muller and Verschoor (2008) suggest that the use of the real exchange rate can improve model results only marginally, and the motivation to use the real exchange rate is therefore reduced.
11
exchange rate exposure is measured. If all firms have the same exposure, the exchange risk becomes a part of market risk and the exchange rate risk coefficient would be zero. The value of zero, however, does not mean that firms are immune to exchange rate fluctuations (Doidge et al., 2006).
Under the efficient market hypothesis, stock prices should quickly reflect all the publicly available information. Therefore, only contemporaneous responses of stock prices to exchange rate changes need to be considered (Jorion, 1990; 1991). However, the relation between exchange rates and firm values is complex, and takes time for investors to interpret (Nydahl, 1999). Sometimes, it can only be interpreted when firms release relevant figures in their financial reports. Bartov and Bodnar (1994) identify that lagged changes in the dollar are a significant variable in explaining current abnormal returns of sample firms, suggesting that mispricing does occur. In addition, mispricing errors on the part of investors result in a situation where the correct relations can only be established with a time lag (He and Ng, 1998). Taking this property into consideration, it is appropriate that lag terms should be included in the regression model (Bartov and Bodnar, 1994; Nydahl, 1999)
Several studies compare results from different return horizons (Chow and Chen, 1998; Di Iorio and Faff, 2000; Bodnar and Wong, 2003; Muller and Verschoor, 2008). The general findings are that both significance and magnitude of exchange rate exposure increase with the return measurement horizon; this is known as the intervalling effect. Chow and Chen (1998) find that exchange exposure is most significant for an interval of 24 months. One month return horizon is widely employed in the empirical
literature (Bodnar and Kaul, 1995; Prasad and Rajan, 1995; Muller and Verschoor, 2007). While some scholars argue that weekly data can enlarge the datasets
12
performance of industries. Therefore, horizons longer than one week are more appropriate.
Most early studies focused on the large and developed economies, such as the U.S., Europe and Japan. Nevertheless, many studies researching other developing countries have emerged recently in accordance with the trend of globalization and the growing prominence of these economies. Chue and Cook (2008) estimated the exposure of emerging market companies to fluctuations in their domestic exchange rates by using an instrumental variable approach that identifies the total exposure of a company to exchange rate movements and found a negative relationship between exposure and debt level. Parsleya and Popper (2006) studied exposure in east and south-east Asia and they discovered that many Asia-Pacific firms show significant exposure to fluctuations in one or more of the four major currencies: the U.S. dollar, the euro (deutschmark prior to 1999), the yen, and the pound. And countries with exchange rates that are fixed against a single currency (as is usual) exhibit no less exposure against the other major currencies. Advances in the empirical study of currency exposure create a great opportunity for refining investigation of the responsiveness of Chinese firms to recent exchange rate reform. Critical insights are gained from the prior research on the choice of exchange rates, sample selection, market index, time horizons and sign and size asymmetries. These insights are very helpful to ensure a
better research design and empirical execution. Nevertheless, existing studies of
13
providing inputs for the country’s processing exports. Stock prices rise for firms competing with China in their home market but fall for firms importing Chinese products with large imported-input content. However, their study is exclusively concerned with how the change in China’s exchange rate policy influences other
countries. Zhao (2010) presents a study that directly explores the dynamic relation
between exchange rates and stock prices within China using monthly data from January 1991 to June 2009 and concludes an insignificant direct linear relationship between exchange rate and stock markets in the long run. Aggarwal, Chen and Yur-Austin (2011) document for the first time that the stock returns of Chinese firms are significantly exposed to currency risks with many firms benefiting from the rise of the RMB. They gauge the influence of China’s move to greater exchange rate flexibility on the competitiveness of Chinese exporting firms. Zhang, Miao, and Zhou (2011) investigate firms’ responsiveness to the new exchange rate flexibility in China to better understand the new Chinese exchange rate regime and its impacts. They found that 15.56% of Chinese listed firms are found to have significant exchange rate exposure to the trade-weighted exchange rate after the removal of the RMB peg to USD. And the study also provides support for previous research that has found size and sign asymmetry in firms’ exchange rate exposure. Strong evidence of lagged responses is detected, which suggests a sluggish response of stock returns to news on the exchange rate.
3. Research method
14
15
establish the type of industry that is more exposed to foreign impacts.
The exposure of firms can be obtained from the coefficient on the exchange variable of a bivariate regression. I adopt the most commonly used model to estimate exchange exposure including an extra variable, which is the market portfolio return. This extra variable not only controls for macroeconomic influences but also reduces the residual variance of the bivariate regression of Adler Dumas. The multivariate regression used to estimate exchange rate exposure is shown in the following equation:
Rit = αi + βixXt + βimRmt + εi (1)
Rit is the return of firm i’s stock in month t
Xt is the change in RMB/foreign currency in month t
Rmt is the rate of return on the benchmark market (in this case is the Shanghai Stock
Exchange A share Index (SSE A share Index))
A positive value of Xt indicates an appreciating RMB, while a negative value of Xt
indicates a depreciating RMB. Then, βix is the foreign exchange exposure, which
measures the association between changes in RMB and stock returns of a specific firm, positive for a firm whose stock value increases (decreases) with RMB appreciation (depreciation) and negative for a firm whose stock value increases (decreases) with RMB depreciation (appreciation).
The lagged effects of exchage rate exposure
As discussed in the literature review, the relation between exchange rates and firm values is complex, and takes time for investors to interpret (Nydahl, 1999). Taking this property into consideration, it is appropriate that lag terms should be included in the regression model (Bartov and Bodnar, 1994; Nydahl, 1999). Hence I investigate the lagged effects by adding lagged variables to the previous equation as shown in the following regression:
16
Sign and size asymmetries
Koutmos and Martin (2003) provide evidence that appreciations and depreciations can asymmetrically impact stock returns as a result of pricing-to-market behavior,
hysteretic behavior, and hedging behavior. Their studies emphasize the sign asymmetry by using three dummies defined for conditions where exchange rates increase, decrease and remain, while in some other studies, divergent responses between large and small exchange rate variations have been proven to be more relevant than sign asymmetry (Muller and Verschoor, 2006).
In order to test the sign and size asymmetries, I formulate two hypotheses:
Hypothesis 1: A firm has only significant exposure to appreciation or depreciation of RMB. (Hypothesis of sign asymmetry)
Hypothesis 2: A firm has only significant exposure to large exchange movements or small movements. (Hypothesis of size asymmetry)
Following Di Iorio and Faff (2000), in which dummy variables are defined for
different ranges of exchange rates changes, in this study, I further divide the dummies to include the sign of change to examine the possible divergent responses between large and small exchange rate variations.
Table 1 Frequency table of exchange rate changes
Range USD EURO YEN HKD POUNDS TRADE-WEIGHTED
17
Total 119 119 119 119 119 119
Note: X represents monthly exchange rate changes of RMB against other foreign currencies as well as trade-weighted exchange rate changes.
According to the frequency table of exchange rates changes, I use a filter parameter of 1% for the construction of dummy variables with an exception of a filter of 0.01% for the RMB/USD exchange rate changes. By introducing four dummy variables to the first equation, the model then becomes:
Rit = αi +β1iDp1Xt + β2i Dp2Xt + β3i Dn1Xt + β4i Dn2Xt + βimRmt + εit (3)
Dp1 is a dummy which takes the value of one when the exchange rate appreciates less
than 1% (0.01% with US dollar) during period t and zero otherwise.
Dp2 is a dummy which takes the value of one when the exchange rate appreciates
more than 1% (0.01% with US dollar) during period t and zero otherwise.
Dn1 dummy takes the value of unity when the exchange rate depreciates less than1%
(0.01% with US dollar) during period t, and zero otherwise.
Dn2 is a dummy which takes the value of one when the exchange rate depreciates
more than 1% (0.01% with US dollar) during period t and zero otherwise.
If the hypothesis of sign asymmetry is rejected, a firm should have significant
exposure to both the appreciation (Dp1 or Dp2) and depreciation (Dn1 or Dn2) of the
RMB. If the hypothesis of size asymmetry is rejected, no difference should be
detected in terms of sensitivity towards large exchange movements (Dp2 or Dn2) and
small ones (Dp1 or Dn1).
4. Data and sample
18
dead stocks which are active during the sample period. Firms are classified into 19 different industries based on the classification of China Securities Regulatory Commission (CSRC).
Table 2 Industry classification
Industry code Industry name No. of firms
A Agriculture 16
B Mining 41
C Manufacturing 513
D Utilities 47
E Construction 32
F Wholesale and retail 97
G Transportation 62
H Hotels and catering 2
I Information 28
J Finance 32
K Real estates 70
L Leasing and commerce service 8
M Scientific research and technology service 1
N Water conservancy, environment and public facilities management
5
P Education 1
Q Hygienism and social work 1
R Culture, sports and entertainment 11
S Comprehensive 15
Total 982
19
The trade-weighted exchange rates are obtained through DATASTREAM sourcing from JP Morgan. The bilateral exchange rates of China’s major trading partners are gathered from the Bank of China, including US dollar, Euro, HK dollar, Japanese Yen, and British pounds. All the exchange rates are indirect quote, which is the amount of foreign currency per unit of RMB. The calculation of exchange rates changes is simply dividing the exchange rates difference between the current month and the previous month by the exchange rate of the previous month. Thus a positive exchange rates change indicates an appreciation of RMB while a negative exchange rates change implies a depreciation of RMB. The table below presents the descriptive statistics of RMB exchange rates:
Table 3 Descriptive Statistics of RMB Exchange Rates Changes
USD EURO HKD YEN POUNDS TRADE_WEIGHTED
Mean 0.002407 0.001228 0.002356 7.88E-05 0.002934 0.000979 Median 0.000659 3.70E-05 0.001037 -0.00094 0.001175 0.000398 Maximum 0.021084 0.101864 0.021532 0.083835 0.103483 0.04137 Minimum -0.00887 -0.10011 -0.01293 -0.0743 -0.08712 -0.04609 Std. Dev. 0.004623 0.031399 0.004796 0.027179 0.027692 0.014742 Skewness 1.57773 0.348806 1.177229 0.131527 0.485282 -0.06307 Kurtosis 6.242002 4.629546 5.963638 3.201848 5.138548 3.519771 Range 0.02995 0.201978 0.034458 0.158131 0.190606 0.087455 Observations 119 119 119 119 119 119
Note: This table reports descriptive statistics for changes in six RMB exchange rates: the exchange rates of RMB against the USD, HKD, yen, and pounds, and the trade-weighted RMB exchange rate
20
Figure 1 Movements of RMB exchange rates
.
Breakpoint analysis
The whole sample spans the period from January 2003 to December 2012 and is further divided into four sub-periods according to the breakpoint analysis of exchange rate changes. The selection of the total sample period is based on the data availability and the timeline of the Chinese currency. In order to capture the long-term trend of exchange rate changes, I intend to incorporate the most recent decade to capture the fluctuations. Furthermore, the Chinese currency has undergone a very unique path in the past decade because of the currency reforms. China’s exchange rate policy has
80 85 90 95 100 105 110 115 120 1/1 /200 3 7/1 /200 3 1/1 /200 4 7/ 1 /2 00 4 1/1 /200 5 7/1 /200 5 1/1 /200 6 7/1 /200 6 1/1 /200 7 7/1 /200 7 1/1 /200 8 7/1 /200 8 1/1 /200 9 7/ 1 /2 00 9 1/1 /201 0 7/1 /201 0 1/1 /201 1 7/1 /201 1 1/1 /201 2 7/1 /201 2
RMB TRADE WEIGHTED INDEX
-.010 -.005 .000 .005 .010 .015 .020 .025 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
21
undergone important changes in recent years. A timeline of Chinese currency policy is included in the Table 10 in the Appendix. The Chinese currency RMB was pegged to USD from 1997 to 2005. Departing from this long-held pegging policy, on 21 July 2005, the People’s Bank of China announced that its exchange rate regime has moved into a managed floating exchange rate regime, under which the exchange rate of the Chinese currency is administered by the government but allowed to move within a fluctuation band specified by the authority (PBOC 2005, 2008). Until 2008, due to the global financial crisis, Chinese central bank effectively pegged the RMB to the US dollar to protect China’s economy. On 19 June 2010, China reiterated the adoption of a managed floating rate system. Since this change, China has moved gradually
towards greater exchange rate flexibility, and the RMB has followed a trend of appreciation through a series of moderate rate changes. Therefore, after checking the data availability on the database, I decide to use this time span of ten years from 2003 to 2012. And as discussed in the literature review, I choose the monthly data, which is widely employed in the empirical studies and most appropriate to test my model. Additionally, I use breakpoint analysis to divide the whole period into four
22
Appendix ). Based on the bilateral exchange rate between the RMB and the US dollar over the whole sample period, the breakpoint analysis provides the following result:
Table 4 Summary of the breakpoint analysis
Breakpoints methods Numbers of breaks Break dates Bai-Perron tests of L+1 vs. L
sequentially determined breaks
2 2007M10, 2010M12
Bai-Perron tests of 1 to M globally determined breaks
5 2005M01, 2006M07, 2008M01, 2009M10, 2011M04
Compare information criteria for 0 to M globally determined breaks
5 2005M01, 2006M07, 2008M01, 2009M10, 2011M04
This table suggests that different methods exhibit similar results, and especially the last two methods have exactly the same break dates. As for the first method, the break dates are also close to those in other two methods. In accordance with the actual graph (Figure 1), I choose 2005M01, 2008M01, and 2010M12 to be the break dates.
Coincidentally, those dates are matching with the time when the major changes occurred according to the timetable of Chinese currency reform in the Appendix (Table 10): On Dec. 8, 2004, Prime Minister Wen Jiabao said that China would move gradually to a flexible currency regime. Moreover, in May 2007, China widened the RMB's daily trading band against the dollar to 0.5 percent from 0.3 percent. And in June 2010, China said it was going to resume reforming the RMB exchange rate and increase currency flexibility after keeping the RMB tightly linked to the US dollar for nearly two years during and after the global financial crisis. The timing of these major changes is correspondent with the break dates from the breakpoint analysis. Hence, the whole period is divided into four sub-periods, which are 2003.1-2005.1,
23
5. Results
As illustrated in the research method, I use Eviews to run the three regressions to present the exchange rate exposure of Chinese firms with regard to different
currencies, to detect the lagged response in Chinese market, and to test sign and size hypotheses.
Results from estimating the first equation for the exchange rate exposure are recorded
in Table 5. Table 5 reports the distribution of the exchange rate exposure βix, with
p-value in parentheses for all firms as well as a selection of ten industries. Since some industries contain only few firms (Table 2), it may not be sufficient to generalize a linear relationship between exchange rate exposure and firm stock returns. Therefore I only include ten industries each consisting of more than 15 firms throughout the sample period 2003 to 2012.
Table 5 Coefficient βix of exchange rate exposure
USD EURO HKD YEN POUNDS TRADE-WEIGHTED No. of firms
24 (0.0000) (0.0000) (0.0245) (0.0000) (0.0000) (0.0000) Industry G -1.4629 (0.0000) -0.2166 (0.0000) 1.8182 (0.0000) 0.1842 (0.0001) -0.0555 (0.3016) 0.0476 (0.0000) 62 Industry I 0.1670 (0.7204) -0.1756 (0.0168) 0.2598 (0.5637) -0.0665 (0.5136) -0.5296 (0.0000) -0.2463 (0.1065) 28 Industry J -2.0455 (0.0000) -0.4444 (0.0001) 2.3210 (0.0000) 0.0439 (0.7730) -0.6472 (0.0003) -0.5091 (0.0620) 32 Industry K -1.1462 (0.0001) -0.3539 (0.0000) 1.3341 (0.0000) -0.2391 (0.0000) -0.5744 (0.0000) -0.5526 (0.0000) 70
Thistable presents the results of coefficient βix representing exchange rate exposure.
Positive coefficients indicate that an appreciation of RMB against other foreign currencies has a positive impact on the stock returns of Chinese firms. For almost all industries, the exposure is negative in terms of trade-weighted exchange rate. The whole sample firms’ stock returns are negatively related with RMB appreciation in terms of trade-weighted exchange rate changes, indicating that an appreciation of RMB does hurt the profitability of many Chinese firms. For instance, the
25
imports, depreciation of home currency will enhance exporting firms’ competitiveness. In addition, Table 5 displays the contemporaneous reactions of firm value to exchange rate movements. But investors need time to interpret the relation between stock
returns and exchange rate changes. To test for the presence of lagged responses in the Chinese market, I add time lags to the first equation. Table 6 presents the summary of the results. The detailed results are shown in the Table 12 in the Appendix.
Table 6 Summary of lagged responses of Chinese firms
Lag(0) Lag(1) Lag(2)
RMB/USD 7 7 5 RMB/EURO 10 5 10 RMB/HKD 4 8 7 RMB/YEN 7 5 7 RMB/POUNDS 8 5 7 TRADE-WEIGHTED 5 4 5
Note: The numbers in the table represent the number of industries that have significant negative exposure coefficients.
26
investors need time to assimilate their knowledge about the relation between stock returns and exchange rate changes.
To further test the hypotheses of sign and size asymmetries, I run the third regression and the results are shown in Table 7 below.
Table 7 Sign and size asymmetries of exchange rate exposure
The table reports the slope coefficients on the four exchange rate dummy variables, representing estimates of the exchange rate exposure in different regions. P-value is shown in parentheses for all sample firms throughout the sample period 2003 to 2012, from the third regression:
Rit = αi +β1iDp1Xt + β2i Dp2Xt + β3i Dn1Xt + β4i Dn2Xt + βimRmt + εit
Dp1 is a dummy which takes the value of one when the exchange rate increases less
than 1% (0.01% with US dollar) during period t and zero otherwise. Dp2 is a dummy
which takes the value of one when the exchange rate increases more than 1% (0.01%
with US dollar) during period t and zero otherwise. Dn1 dummy takes the value of
unity when the exchange rate decreases less than 1% (0.01% with US dollar) during
period t, and zero otherwise. Dn2 is a dummy which takes the value of one when the
27
The results recorded in the table above support the effect of sign asymmetry by
showing that the coefficient on DP2 is significant with respect to various exchange
rates while the coefficient on DN2 is not statistically significant for most of the
exchange rates. This means that firms have significant exposure to appreciation of RMB. This is consistent with the scenario proposed by Bollerslev et al., (1992, 1995), and Hentschel (1995) that investors are likely to react more to bad news than towards positive shocks. When valuing an exporting firm, for instance, investors consider a domestic currency appreciation as bad news which would result in a strong reaction in stock returns. Furthermore, the size asymmetry is supported since difference is
detected in terms of sensitivity towards large exchange movements or small ones. The
coefficients of DN2 are mostly statistically insignificant while those of DN1 are
significant, suggesting firms are more sensitive to small changes in exchange rates. The result clearly deviates from the expectation in the literature that investors tend to ignore small exchange rate movements. Therefore, it provides support to the efficient market hypothesis which asserts that the financial market is informationally efficient. The panels in Table 13 in the Appendix present the results of the same regression for each industry. P-value is shown in the parentheses and the value without parentheses is significant at 10% level. The results differ in individual industry and vary with distinct exchange rates. Little evidence suggests the existence of size asymmetry in most industries. The sign asymmetry is blurred with variations among different industries. Some industries are significantly exposed not only to the appreciation but also the depreciation of RMB.
Sub-periods analysis
28
and then resumed in 2010, allow more flexible currency movements, mainly with a trend of appreciation. In order to trace these important policy changes, I set up four sub-periods for investigation. Instead of dividing the whole sample period into equal periods, I exploit breakpoint analysis techniques to find the breakpoints of the movements of RMB exchange rates against the US dollar. The sub-periods are February 2003 to January 2005, February 2005 to January 2008, February 2008 to December 2010, and January 2011 to December 2012.
The sub-periods analysis reveals some evidence of time-varying exchange rate exposure, which is shown in the Table 8. Table 8 summarizes the sub-periods results for exposure coefficients for the whole sample firms based on the first regression. The exposure coefficient is not significant for the first sub-period in terms of
trade-weighted exchange rate movements, which is probably due to the pegging currency policy. During this period, RMB/USD exchange rate was fixed by the Chinese government while other international currencies remained fluctuating. The rest of the sub-periods have significant exposure for all the bilateral exchange rates as well as trade-weighted exchange rate. The exposure coefficient in the second period is significant and larger than that in other periods, probably because of the greater exchange rate flexibility after policy changes. After the announcement of central bank of China, the Chinese currency has stepped into a new regime which allows for greater flexibility. Table 14 in the Appendix shows the sub-periods exposure in each industry. Overall, the results are in line with the whole sample results, almost all industries show significant exposure during the second sub-period with all bilateral exchange rates as well as trade-weighted exchange rate. However, the exposure is not statistically significant in the third period for some industries, such as industry J, the financial industry. This is due to government intervention to fix the exchange rate in order to insulate the adverse impacts of the global financial crisis.
Table 8 Sub-periods exchange rate exposure of Chinese firms
29 USD -304.9506(0.0000) -1.1393 (0.0000) -1.0210 (0.0000) -1.2447 (0.0000) EURO -0.1175(0.0000) 0.1820 (0.0000) -0.1174(0.0129) 0.0056 (0.8504) HKD -2.2009(0.0000) -1.1002 (0.0000) -3.20644(0.0000) -0.6281 (0.0000) YEN 0.0039(0.8602) 0.2847 (0.0000) 0.5070 (0.0000) 0.2235 (0.0000) POUNDS 0.0092(0.6483) -0.1164 (0.0000) -0.51250(0.0000) -0.0569 (0.1122) WEIGHTED 0.0023(0.9599) -0.5728 (0.0000) -0.0775 (0.3077) -0.1491 (0.0089) Periods 24 36 35 24
Note: The table exhibits the results of estimating exchange rate exposure for the four sub-periods based on equation (1).
Table 9 shows the results of different sub-periods exchange rate exposure in different industries. Strong lagged responses of almost all industries to changes in
trade-weighted exchange rate are detected in the second sub-period. During the second period, the currency reforms has started, which raise the value of RMB gradually. Manufacturing industry exhibits negative exposure to the appreciation.
Particularly, the coefficient on Dp2 of manufacturing industry is significant while the
coefficient on Dp1 is not statistically significant, indicating that manufacturing
industries are more sensitive to large movements in exchange rate. For exporting industries, like manufacturing, appreciation of home currency raises the price of the products and reduces competitiveness in the international market. Thus, the investors respond to this bad news accordingly, resulting in the negative relations between firm value and exchange rate changes. There is little evidence for sign and size
asymmetries at the industrial level, which indicates that Chinese firms’ reaction to appreciation is mostly mild, especially when the appreciation is moderate. Therefore, it is rather sensible that the Chinese government allows the RMB to appreciate, but only moderately. This is a gradual strategy to achieve exchange rate adjustments with minimal disruption to Chinese firms.
30
33
6. Conclusion
This paper investigates exchange rate exposure of Chinese firms through a period of time during which the country exchange rate system has undergone some momentous changes. I construct econometric models to test the existence of significant exchange rate exposure, lagged responses and sign and size asymmetries in exposure of Chinese firms. Extending previous studies, I apply breakpoint analysis to divide the whole sample period into four sub-periods and conduct industrial analysis. Empirical evidence gained from this study reveals that most of Chinese listed firms have significant exchange-rate exposure to the trade-weighted exchange rate after the currency reform which abolished the pegging system. Some evidence of time varying exposure is discovered. In the sub-period analysis, almost all the Chinese industries have significant exposure to trade-weighted exchange rate movements in the second sub-period, during which the pegging system was removed and large appreciation occurred. Size and sign asymmetries in exposure are tested for both trade-weighted exchange rate and bilateral exchange rate with major trading partners. The evidence unearthed indicates that Chinese firms’ responses to RMB appreciation and
depreciation and to large and small movements of exchange rates are divergent cross industries. The results suggest a negative relation between firm value and exchange rate movements, especially true for the exporting industries, such as manufacturing. Thus, the appreciation of home currency did hurt the profitability of exporting firms and reduce the trade surplus of China.
34
conceivable that a four-digit industry analysis would be more convincing. Last but not the least, related literature and previous studies have found that firms’ hedging
activities and pass-through effect can considerably affect exposure. But China’s financial market is still very young and immature, which makes it difficult to conduct such research. With the integration into international markets over time, investigation of the hedging and pass-through effects on Chinese firms’ exchange rate exposure will be very important.
References
Adler, M., Dumas, B 1984. Exposure to currency risk: definition and measurement. Financial
Management, pp. 41–50.
Aggarwal, R., Chen, X., Yur-Austin, J 2011. Currency risk exposure of Chinese corporations.
Research in International Business and Finance, Vol. 25, pp. 266-276.
Allayannis, G 1995. The time-variation of the exchange rate exposure: An industry analysis. 57th
Annual American Finance Association Conference,(New Orleans), January. Vol. 5.
Allayannis, G., Ofek, E 2001. Exchange rate exposure, hedging and the use of foreign currency derivatives. Journal of International Money and Finance, Vol. 20, pp. 273–296.
Andrews, D.W.K 1993. Tests for parameter instability and structural change with unknown change point. Econometrica: Journal of the Econometric Society, pp. 821-856.
Andrews, D.W.K., Ploberger, W 1994. Optimal tests when a nuisance parameter is present only under the alternative. Econometrica: Journal of the Econometric Society, pp. 1383-1414.
Bai, J., P. Perron 1998. Estimating and testing linear models with multiple structural changes.
Econometrica, pp. 47–78.
Bai, J., P. Perron 2003. Computation and analysis of multiple structural change models. Journal of
Applied Econometrics, Vol. 18, pp. 1–22.
Baldwin, R., Krugman, P 1989. Persistent trade effects of large exchange rate shocks. The Quarterly
Journal of Economics, Vol. 104, pp. 635-654.
Bartov, E., Bodnar, G. M 1994. Firm valuation, earnings expectations, and the exchange rate exposure effect. Journal of Finance, Vol. 49, pp. 1755–85.
Bartov, E., Bodnar, G. M., Kaul, A 1996. Exchange rate variability and the riskiness of US multinational firms: evidence from the breakdown. Journal of Financial Economics, Vol. 42, pp. 105–32.
Bartram, S.M., G.W. Brown and B. Minton 2009. Resolving the Exposure Puzzle: The Many Facets of Foreign Exchange Exposure. Journal of Financial Economics, Vol.95, pp.148-173.
Bodnar, G. M., Dumas, B., Marston, R.C 2002. Pass-through and Exposure. Journal of Finance, February, pp. 199 - 232.
35
Bodnar, G. M., Wong, M. H. F 2003. Estimating exchange rate exposures: issues in model structure.
Financial Management, pp. 35–67.
Bollerslev, T., Chou, R.Y., Kroner, K 1992. ARCH modeling in finance: a review of the theory and empirical evidence. Journal of econometrics, Vol. 52, pp. 5-59.
Choi, J. J., Prasad, A. M 1995. Exchange risk sensitivity and its determinants: a firm and industry analysis of US multinationals. Financial Management, Vol. 24, pp. 77–88.
Chow, E. W., Chen, H 1998. The determinants of foreign exchange rate exposure: evidence on Japanese firms. Pacific-Basin Finance Journal, Vol. 6, pp. 153–74.
Chow, Edward H., Wayne Y. Lee, and Michael E. Solt 1997. The Exchange-Rate Risk Exposure of Asset Returns. Journal of Business, Vol. 70, pp. 105–123.
Chow, G. C 1960. Tests of equality between sets of coefficients in two linear regressions. Econometrica:
Journal of the Econometric Society, pp. 591-605.
Chue, T. K., Cook D 2008. Emerging market exchange rate exposure. Journal of Banking & Finance Vol. 32, pp. 1349-1362.
Chue, T. K., Cook, D 2008. Emerging market exchange rate exposure. Journal of Banking & Finance, Vol.32(7), pp.1349-1362.
Di Iorio, A., Faff, R 2001. The effect of intervaling on the foreign exchange exposure of Australian stock returns. Multinational Finance Journal, Vol. 5, pp. 1–33.
Doidge, C, Griffin, J., Williamson, R 2002. Does Exchange Rate Exposure Matter? Working Paper, Arizona State University.
Doidge, C, Griffin, J., Williamson, R 2006. Measuring the economic importance of exchange rate exposure. Journal of Empirical Finance, Vol. 13, pp. 550-576.
Dominguez, K. M., Tesar, L. L 2001. A re-examination of exchange rate exposure. American Economic
Review, Vol. 91, pp. 396–99.
Eichengreen, B., Tong, H 2011. The external impact of China’s exchange rate policy: Evidence from firm level data, IMF Working Papers, pp. 1-32.
Griffin, J. M., Stulz, R. M 2001. International competition and exchange rate shocks: a cross-country industry analysis of stock returns. Review of Financial Studies, Vol. 14, pp. 215–41.
He, J., Ng, L 1998. The foreign exchange exposure of Japanese multinational corporations. Journal of
Finance, Vol. 53, pp. 733–53.
Hentschel, L 1995. All in the family nesting symmetric and asymmetric garch models. Journal of
Financial Economics, Vol.39, pp. 71-104.
Ihrig, J 2001. Exchange rate exposure of multinationals: Focusing on exchange rate issues. International Finance Discussion Papers, no.709.
Jorion, P 1990. The exchange rate exposure of US multinationals. Journal of Business, Vol. 63, pp. 331–45.
Jorion, P 1991.The pricing of exchange rate risk in the stock market. The Journal of Financial and
Quantitative Analysis, Vol. 26, 363 -376.
Knetter, M.M 1993. International comparisons of pricing-to-market behavior. American Economic
Review, Vol. 83, pp. 473-486.
Koutmos, G., Martin, A. D 2003. Asymmetric exchange rate exposure: theory and evidence. Journal of
International Money and Finance, Vol. 22(3), pp. 365-383.
36
48.
Ljungqvist, L 1994. Hysteresis in international trade: a general equilibrium analysis. Journal of
International Money and Finance, Vol. 13, pp. 387-399
Luo, H., Jiang, C 2010. The exchange rate exposure of listed Chinese firms under current reforms on the exchange rate system. Journal of Zhongnan University of Economics and Law, Vol. 163, pp. 73-81.
Marston, R.C 1990. Pricing to market in Japanese manufacturing. Journal of International Economics, Vol. 29, pp. 217-236.
Marston, R.C 2001. The effects of industry structure on economic exposure. Journal of International
Money and Finance, Vol. 20, pp.149–164.
Miller, K.D., Reuer, J.J 1998. Firm strategy and economic exposure to foreign exchange rate movements. Journal of International Business Studies, Vol. 29, pp. 493–514.
Muller, A., Verschoor, W.F.C 2006. European Foreign Exchange Risk. European Financial
Management, Vol. 12(2), pp. 195–220
Muller, A., Verschoor, W.F.C 2007. Asian foreign exchange risk exposure. Journal of Japanese
International Economies, Vol. 21, pp. 16–37.
Muller, A.,Verschoor, W.F.C 2006. Asymmetric foreign exchange risk exposure: Evidence from U.S. multinational firms. Journal of Empirical Finance, Vol.13, pp. 495–518.
Nydahl, S 1999. Exchange rate exposure, foreign involvement and currency hedging of firms: some Swedish evidence. European Financial Management, Vol. 5.2, pp. 241-257.
Parsley, D. C., Popper, H. A 2006. Exchange rate pegs and foreign exchange exposure in East and South East Asia. Journal of International Money and Finance, Vol. 25(6), pp. 992-1009.
Parsley, D. C., Popper, H. A 2006. Exchange rate pegs and foreign exchange exposure in East and South East Asia. Journal of International Money and Finance Vol. 25, pp. 992-1009.
Pollard, P. S., Coughlin, C. C 2003. Size matters: asymmetric exchange rate pass-through at the industry level. Federal Reserve Bank of St. Louis Working Paper Series.
Prasad, A.M., Rajan, M 1995. The role of exchange and interest risk in equity valuation: A comparative study of international stock markets. Journal of Economics and Business, Vol. 47, pp. 457-472. Quandt, R. E 1960. Tests of the hypothesis that a linear regression system obeys two separate regimes.
Journal of the American statistical Association, Vol. 55, pp. 324-330.
Shapiro, A 1975. Exchange rate changes, inflation, and the value of the multinational corporation. The
Journal of Finance, Vol. 30, 485-502.
The People’s Bank of China Announcement 2005, No. 153, Available at: http://www.pbc.gov.cn/publish/chubanwu/554/1080/10809/10809_.html
Williamson, R 2001. Exchange rate exposure and competition: Evidence from the automotive industry.
Journal of Financial Economics, Vol.59, pp. 441-475.
Wong, T.C., Wong, J., Leung, P 2009. The foreign exchange exposure of Chinese banks. China
Economic Review, Vol. 20, pp. 174-182.
Zhao, H 2010. Dynamic relationship between exchange rate and stock price: Evidence from China.
37
Appendix
Table 10 Timetable of Chinese currency reforms
Time Reforms
1988 China sets up semi-official currency swap centers around the country to allow enterprises to trade the RMB at a rate that more closely reflects market demand. 1994, Jan.1 China unifies its dual exchange rates by bringing the official and swap center rates into
line, officially devaluing the RMB by 33 per cent overnight to 8.7 to the dollar as part of reforms to embrace a "socialist market economy".
1994, April.18
China sets up its first interbank currency market -- the China Foreign Exchange Trade System -- in Shanghai. The central bank intervenes in the market to keep the RMB stable.
1996, Dec. 1 China allows the RMB to be fully convertible under the current account 1994-1996 The RMB strengthens steadily from 8.7 to the dollar to around 8.28.
1997-1999 China wins wide praise for keeping the RMB stable during the Asian financial crisis despite pressure to devalue. The RMB was boxed between 8.2770 and 8.2800 for about three years through frequent central bank intervention.
2000 The RMB is allowed to close slightly outside the firm end of its 30-basis point band, which is later widened by another 10 points to 8.2760-8.2800 against the dollar. 2001,
December
China joins the World Trade Organization and pledges to adjust its currency regime gradually.
2003 International pressure begins mounting for the RMB to appreciate to help balance global trade, including China's huge trade surplus with the United States and the rest of the world.
2004, Dec. 8 Prime minister Wen Jiabao says that China would move gradually to a flexible currency regime.
2005, July 21
China revalues the RMB by 2.1 per cent and revises the rules governing its currency system, saying it had shifted to "a managed floating exchange rate based on market supply and demand with reference to a basket of currencies".
2007, May China widens the RMB's daily trading band against the dollar to 0.5 percent from 0.3 percent.
2008, July China's central bank effectively pegs the RMB against the dollar close to 6.83 to protect China's economy as it confronted a slowdown due to the global financial crisis. 2010, June China said it was going to resume reforming the RMB exchange rate and increase
currency flexibility after keeping the RMB tightly linked to the US dollar for nearly two years during and after the global financial crisis.
Source:http://articles.economictimes.indiatimes.com/2010-06-19/news/27612673_1_RMB-exchange-rate-central-bank-china-sets
Figure 2: Breakpoint analysis techniques:
38 -.6 -.4 -.2 .0 .2 .4 .6 6.0 6.5 7.0 7.5 8.0 8.5 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
Residual Actual Fitted
Bai-Perron tests of 1 to M globally determined breaks
-.4 -.2 .0 .2 .4 6.0 6.5 7.0 7.5 8.0 8.5 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
Residual Actual Fitted
Compare information criteria for 0 to M globally determined breaks
-.4 -.2 .0 .2 .4 6.0 6.5 7.0 7.5 8.0 8.5 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
39
Table 11 Correlation between independent variables
RMB/EUR O
RMB/USD RMB/HK$ RMB/YEN MARKET_ RETURN RMB/POU NDS TRADE-WE IGHTED RMB/EURO 1.000000 -0.160026 -0.085664 0.174942 0.311836 0.601922 0.800557 RMB/USD -0.160026 1.000000 0.939175 0.097414 0.134505 0.076386 0.072492 RMB/HK$ -0.085664 0.939175 1.000000 0.145929 0.147724 0.113442 0.127250 RMB/YEN 0.174942 0.097414 0.145929 1.000000 -0.144010 0.084202 0.592463 MARKET_R ETURN 0.311836 0.134505 0.147724 -0.144010 1.000000 0.385553 0.245873 RMB/POUN DS 0.601922 0.076386 0.113442 0.084202 0.385553 1.000000 0.557734 TRADE-WEI GHTED 0.800557 0.072492 0.127250 0.592463 0.245873 0.557734 1.000000
Table 12 Lagged response of Chinese firms to exchange rates changes
Total sample Lag (0) Lag (1) Lag (2)
RMB/USD -0.627539 (0.0000) -0.200934 (0.0212) -1.817264 (0.0000) RMB/EURO -0.095914 (0.0000) 0.069117 (0.0000) -0.006619 (0.6278) RMB/HKD -0.487140 (0.0000) -0.188647 (0.0459) -1.537812 (0.0000) RMB/YEN 0.246431 (0.0000) 0.115487 (0.0000) 0.044525 (0.0053) RMB/POUNDS -0.162130 (0.0000) -0.099789 (0.0000) -0.234102 (0.0000) Trade-Weighted Index -0.294087 (0.0000) -0.164863 (0.0000) -0.252248 (0.0000) Periods 116 116 116 No. of firms 982 982 982 Panel A RMB/USD
Industry Lag(0) Lag(1) Lag(2)
40 E -1.193296 (0.0319) -0.300619 (0.3978) 2.798916 (0.0000) F -1.885281 (0.0000) -0.529920 (0.0235) 3.368521 (0.0000) G -1.421292 (0.0000) -0.684278 (0.0097) -0.600484 (0.0309) I -0.781365 (0.1244) -3.208030 (0.0000) -5.818371 (0.0000) J -3.229577 (0.0004) -3.134090 (0.0029) 5.688930 (0.0141) K -1.231598 (0.0000) -1.692764 (0.0000) 1.516926 (0.0005) Panel B RMB/EURO
Industry Lag(0) Lag(1) Lag(2)
A -0.748721 (0.0000) -0.229860 (0.0054) -0.464873 (0.0000) B -0.290945 (0.0002) -0.349978 (0.0000) -0.345157 (0.0000) C -0.475467 (0.0000) -0.027735 (0.2890) -0.108284 (0.0000) D -0.572620 (0.0000) 0.192722 (0.0006) -0.155955 (0.0011) E -0.281575 (0.0047) -0.054607 (0.5058) -0.341948 (0.0000) F -0.405165 (0.0000) -0.391281 (0.0000) -0.050670 (0.1675) G -0.238438 (0.0000) 0.032026 (0.4894) -0.344609 (0.0000) I -0.194933 (0.0064) -0.177355 (0.0143) -0.102001 (0.0284) J -0.463345 (0.0000) 0.019720 (0.0000) -0.452694 (0.0000) K -0.395217 (0.0000) -0.305800 (0.0000) -0.267926 (0.0000) Panel C RMB/HKD
Industry Lag(0) Lag(1) Lag(2)
41 E -0.122886 (0.8040) 1.717621 (0.0000) -3.109739 (0.0000) F -0.876966 (0.0002) -1.268017 (0.0000) 3.197347 (0.0000) G -1.610260 (0.0000) -0.931724 (0.0001) -0.490469 (0.0537) I -0.538527 (0.2661) -3.324672 (0.0000) -5.340133 (0.0000) J -2.685817 (0.0001) -3.050640 (0.0051) -4.499504 (0.0249) K -0.950744 (0.0005) -1.982267 (0.0000) -0.696554 (0.1159) Panel D RMB/YEN
Industry Lag(0) Lag(1) Lag(2)
A -0.482448 (0.0000) 0.116879 (0.2992) -0.281633 (0.0008) B -0.685652 (0.0000) 0.015620 (0.7864) -0.114474 (0.1170) C -0.348999 (0.0000) -0.389316 (0.0000) -0.217924 (0.0000) D -0.406236 (0.0000) -0.063908 (0.2899) -0.192055 (0.0015) E -0.522856 (0.0000) 0.108053 (0.1116) 0.389224 (0.0000) F -0.164687 (0.0000) -0.129416 (0.0032) -0.431350 (0.0000) G 0.192833 (0.0001) -0.162889 (0.0002) -0.069666 (0.1150) I -0.054363 (0.5876) -0.021008 (0.8200) -0.169558 (0.0048) J 0.059699 (0.6931) 0.169442 (0.2589) -1.023957 (0.0000) K -0.201896 (0.0002) -0.307012 (0.0000) -0.352911 (0.0000) Panel E RMB/POUNDS
Industry Lag(0) Lag(1) Lag(2)
42 E 0.025479 (0.7293) -0.228273 (0.0100) -0.453027 (0.0000) F -0.444047 (0.0000) 0.097960 (0.0079) -0.469766 (0.0000) G 0.042646 (0.4093) -0.090666 (0.0283) -0.596012 (0.0000) I -0.433153 (0.0000) 0.147166 (0.0219) -0.469072 (0.0000) J -0.560593 (0.0031) 0.493291 (0.0006) -1.322873 (0.0000) K -0.518138 (0.0000) 0.014782 (0.8038) -0.399655 (0.0000)
Panel F TRADE-WEIGHTED INDEX
Industry Lag(0) Lag(1) Lag(2)
A 1.285126 (0.0000) 0.128321 (0.5042) 0.887910 (0.0000) B 0.699083 (0.0000) 0.693209 (0.0000) 0.513131 (0.0000) C -0.946474 (0.0000) -0.374272 (0.0000) -0.347441 (0.0000) D -1.096279 (0.0000) -0.348004 (0.0029) -0.886853 (0.0000) E -0.804066 (0.0000) -0.090454 (0.5656) -0.575197 (0.0000) F -0.528624 (0.0000) -0.454830 (0.0000) -0.814067 (0.0000) G 0.025639 (0.7924) 0.080740 (0.3686) 0.848307 (0.0000) I 0.319030 (0.0429) 0.021451 (0.9037) 0.856353 (0.0000) J -0.846983 (0.0049) -0.519572 (0.0329) -2.397284 (0.0000) K 0.643594 (0.0000) 0.467489 (0.0001) 1.068278 (0.0000)
44 Panel 5 RMB/POUNDS Industry DP1 DP2 DN1 DN2 A -3.355775(0.0000) -0.803860(0.0000) 4.507027(0.0000) -0.281248(0.0830) B -2.736102(0.0000) -0.740994(0.0000) 4.425790(0.0000) 0.135151 (0.1772) C -4.114134(0.0000) -1.032709(0.0000) 6.183610(0.0000) 0.278650(0.0000) D -7.623963(0.0000) -1.046422(0.0000) 5.039672(0.0000) 0.581900(0.0000) E -5.449398(0.0000) -0.952971(0.0000) 7.295121(0.0000) 0.687768(0.0000) F -3.369372(0.0000) -0.621419(0.0000) 1.645409(0.0000) -0.400585(0.0000) G 0.108038(0.8513) -0.307203(0.0030) -0.531966(0.4008) 0.150765(0.1225) I 1.728083(0.1058) -0.219389(0.1859) -0.616080(0.4994) -0.825174(0.0000) J 2.498713(0.1665) 0.389829(0.1951) -4.379854(0.0000) -1.328065(0.0000) K 2.389166(0.0000) -0.549514(0.0000) -4.905051(0.0000) -0.563771(0.0000)
Panel 6 TRADE_WEIGHTED INDEX
Industry DP1 DP2 DN1 DN2 A 2.259275(0.0822) 0.644109(0.1629) 3.640249(0.0000) 1.512145(0.0000) B -0.394811(0.7115) -0.861757(0.0000) 5.892906(0.0000) 1.906600(0.0000) C -1.34952(0.0000) -0.355956(0.0000) 6.871874(0.0000) 1.876055(0.0000) D -1.021851(0.2541) 1.736250(0.0000) 2.736611(0.0000) 0.241488(0.3075) E -0.831575(0.4987) 1.708721(0.0000) 1.736082(0.0000) -0.363308(0.2641) F -6.633544(0.0000) 0.654491(0.0000) 0.710669(0.1192) 0.794503(0.0000) G -3.787202(0.0000) -0.223453(0.2469) -1.149162(0.0000) 0.597008(0.0000) I -4.122313(0.0000) 0.603571(0.0000) -4.056356(0.0000) 0.693817(0.0000) J -7.190802(0.0000) 2.169117(0.0000) -4.998323(0.0000) -0.425284(0.3253) K -3.569420(0.0000) 1.558362(0.0000) -3.433385(0.0000) 0.182752(0.2842)
Table 14 Sub-periods analysis of exchange rate exposure in each industry
46 Industry E -0.181760(0.1322) -0.466962(0.0000) 0.272015(0.0000) 1.902743(0.0000) Industry F -0.804220(0.0000) -1.015155(0.0000) 0.028700(0.6724) 0.531526(0.0000) Industry G -0.552614(0.0000) -0.416672(0.0000) 0.308666(0.0000) 0.975703(0.0000) Industry I -1.342433(0.0000) -0.919485(0.0000) 0.137475(0.1661) -0.397069(0.1397) Industry J 0.528125(0.1174) -1.468051(0.0000) -0.146588(0.6424) -0.187583(0.4370) Industry K -0.454486(0.0000) -1.574853(0.0000) -0.143365(0.1194) -0.576764(0.0000)
Panel 6 Trade-weighted Index
