Foreign Exchange Exposure of Korean Firms

The purpose of this study is to examine the relationship between the movements of exchange rate and value of Korean firms, so-called foreign exchange rate exposure using newly devised model to find the strong evidence. I use weekly data on Korean Firms that are listed on Korea Stock Exchange (KSE) for the period from January 1997 to December 2000. I find that about 70% Korean Firms are actually exposed to Won-dollar exchange rate movement at 10% significance level and these results are substantially different from the previous empirical study where little statistical significance was found. In comparing the foreign exchange exposures with three different exchange rates, in Won-dollar and Won-yen exchange exposures, value of Korean firms is positively related to depreciation of Korean Won and negatively related to depreciation of Korean Won with Won-euro exchange exposure. With magnitude of three exposures, results can be interpreted that Dollar exposure seems to be the most significant among three foreign exchange exposures and Korean Firms' value is more sensitive to Won-dollar exchange rate. I also find that exchange exposure is strongly related to firm size and industry especially Electricity & Gas industry is most significantly related.


I. Introduction
In the early 1970s, the U.S. government abandoned the fixed exchange rate system and adopted floating exchange rate regime. Since that time, there have been tremendous changes and fluctuations in the foreign exchange market and in international financial market. 1 As the degree of exchange rate fluctuation was getting increased in the globally integrated financial capital market, many countries concerned about change of their countries' return which is affected by the fluctuation. So seeking ways to hedge the foreign exchange rate risk became a main issue and many researchers started to study the relationship between the exchange rate and return of companies, which is so-called foreign exchange rate exposure.
According to Chung (1997), the KRW-USD exchange rates were allowed to fluctuate freely through the 1990s the exchange rate has increased accordingly.
Through financial crisis in 1997, the volatility of the exchange rate proved itself to be so severe as to lead to major crises or even to defaults of some economies, and the importance of estimating the foreign exchange exposure came up to the surface again.
For the past decade, several researchers like Adler and Dumas (1984), Jorion (1991), Banda & Gentry (1993), and Campa (1997) have been empirically investigating the foreign exchange exposure of corporations. Up to date, it is widely believed that the movements of exchange rate affect value of companies, which means their returns are significantly exposed to exchange rate movements; however, there has been weak or low statistical evidence.
The statistical inactivity is because, first, most of the previous empirical studies estimating the foreign exchange exposure focused on economy-leading countries, which have small portion of foreign operations.
Second, most of researchers used the uniform or similar Capital Asset Pricing Model (CAPM) regression model that includes market return as an explanatory variable, and single currency in their empirical studies.
Third, in actual capital market, market return is correlated with the movement of exchange rate, which is a point many researchers connived at. It is contrary to the fundamental that market return should not have correlations with independent variables in any kind of models, and it, after all, reduces statistical significance. Inclusion of the market portfolio return variable allows researchers to control market value-relevant factors and to improve the precision of the exposure estimates, but it is faulty since market return is correlated with the exchange rate over the estimation period. 2 it is relevant to compare the three foreign exchange exposures.
However, in using Euro per dollar exchange rate, due to data availability, German Mark-dollar exchange rate was used for the first two years out of the 1997 to 2000 period instead of Euro-dollar rate.
Lastly, to identify the determinants of foreign exchange exposure, foreign exchange exposures were classified into twenty-one industry categories and firm size.
Definition and classification of foreign exchange rate exposure opens the section II.
In section III, available and relevant data set for empirical study are introduced.
Section IV presents empirical study including regression model of previous study and newly devised econometric model and its empirical findings that are estimated exchange exposure of Korean firms and three different exchange exposures. Section V reports the related factors' statistical significance in the explanation of exchange exposure. Section VI includes summary and concluding remarks.

II. Defining Exchange Rate Exposure
Exchange exposure, defined as the sensitivity of corporation's value to a change of exchange rate, is classified into three categories; Transaction, Translation and Economic Exposure. 4 (1) Transaction Exposure Transaction Exposure originates from the possibility when future income, which is expected to be earned by foreign currency denominated contract, changes during the time period of commitment to a transaction and an actual transaction. However, this kind of exposure usually is well defined and it can be hedged quite easily using derivatives.
(2) Translation Exposure Translation exposure or accounting exposure is the difference between assets and liabilities that are exposed to the fluctuation of a certain currency. Generally, to evaluate the balance sheet of subsidiaries that are operating in foreign countries in the 4 See Jorin (1990) and Stefan Nydahl (1999) foreign currencies, some constant exchange rates would have to be applied to each item in the balance sheet. At this moment, the value of subsidiaries varies on account of applying current or historical exchange rate.
(3) Economic Exposure Economic exposure measures the degree to which exchange rate movements affect a firm's value. So, economic exposure depends on the operations of the firm, but is much more important and complicated than transaction exposure or translation exposure in terms of long-term management of firms.
However, it is very difficult and complex to distinguish the difference between transaction exposure, translation exposure and economic exposure. 5 So in this paper, economic exposure will be regarded as the combination of transaction exposure and translation exposure. 6

III. Data Set
The data for the empirical research in this paper contains five sets of variables: weekly is structural change before and after the crisis. For that purpose, each period is designed to have three sub-periods that are pre-crisis (Dummy crisis variable equals to zero), in-crisis (Dummy crisis variable equals to one) and post-crisis (Dummy crisis variable equals to two), respectively.
Firm size: Large firms are expected to be more significantly exposed to exchange rate movements, so firm size was chosen as an explanatory variable. Total market value was calculated with the data from KSE by multiplying the number of outstanding shares with market price, and the companies' size were sorted by total market value. We define the top 10% companies of total market value as a large firm and the bottom 10% as a small firm.

Industry variables:
To identify the determinant of exchange exposure, industry variables were considered with the expectation that all the industry does not have the same level of exposure. Each company was put into twenty-one industries classification, 10 and the industry codes are presented in Table 6. The economic exposure is a coefficient (β 1 ) of exchange rate and can be obtained from following regression model, where R t is the return on the individual firm's rate of return, ΔS t is the percentage change of exchange rate, and R mt is the return on market portfolio and e t is the error term.
β 1 refers to the economic exposure coefficient explaining relationships between change of exchange rate and value of firm. However, in this regression model, it raises interaction problem between the market return and the exchange rate and it reduces statistical significance. 11 The result of the Usual regression Table 1 and Figure 3 summarize the sign and magnitude of the KRW-USD exchange exposure profile using usual regression model. 79 firms out of 790 (10%) are significantly exposed to movements of KRW-USD exchange rate at the 10% level.
And among the firms with significant coefficients, 59 firms (75%)  (2) Newly devised econometric model To mitigate this interaction problem between market return and exchange rate, the exposure coefficient β 1 was estimated from newly devised regression model. In the new econometric model, ∧ ε it is used as an independent variable.
The below shows the process of deriving newly adjusted regression model.

First process is the estimation of coefficients through simple but intuitive Ordinary
Least Squares (OLS).
The next is the calculation of the residual ( ∧ ε it ) from the below numerical formula, where ∧ ε it is the remainder that exclude foreign exchange rate factors from the factors that have effect on market return.
The final step is to put the calculated error terms into the model as dependent variables and regress them using OLS. And the coefficient of exchange rate change can be said to be the degree of exchange rate exposure.
The result of newly devised regression  For the KRW-euro exchange exposure, 55 of 791 firms (7%) are significantly exposed to exchange rate movements at 1%, 112(14%) at 5% level and 171(22%) at 10% significance level. Compared to KRW-USD exposure and KRW-JPY exposure, the number of significant coefficients of KRW-euro exchange rate is small and also the magnitude of exposure is relatively small.
Totally different thing is that most of KRW-euro exposures have positive signs. Figure 4b shows that most exposure coefficients are concentrated on positive signs.
That means appreciation of Korean won against euro leads to increase of Korean firms' value. Even though 22% significance is not really small, compared to previous results, value of Korean firms is less affected by euro. It might be trade volume and portion of investment in EURO is increasing but still small. are significantly exposed to the rate's movement at 1% significance level.
255(32%) firms and 353(45%) were proven to be significant at the 5% and 10% significance level, respectively. It also has negative sign on exposure coefficients, but as shown in Figure 4c, magnitude of exposure is less severe than KRW-USD exposure.
With the results focusing only on the number of companies having significant exposure, Dollar exposure can be said to be the most significant among the three exchange exposures and are negatively affected by the depreciation of the KRW against USD.
Search for the extent and sign of significant exposure coefficient was done, but it is relevant to consider total and insignificant exposure coefficients altogether. The figures indicate that the magnitude of exposure -6.11429 ~ 7.46443 in KRW-USD, -1.12613 ~ 1.175248 in KRW-euro, and -2.02008 ~ 3.38385 in KRW-JPY.  Korean and other Asian Crises, it is out of interest in this paper. We more focus on structural change before and after the crisis on exchange exposures. In this section the question "Is there any structural change before and after crisis in exchange exposure?" will be answered. To find out structural change before and after crisis, dummy variable was put into the newly adjusted model and get new regression model.
where the dummy variable D equals to 0 for pre-crisis period (from January 1997 to October 1997), and 1 for in-crisis period (from November 1997 to December 1998) and 2 for post-crisis period (January 1999 to December 2000).
The full period was divided into three sub-periods on the basis of change of exchange rate and market return. In Figure 2a and 2c, KRW-USD, euro and JPY start to fluctuate abruptly from November 1997 and Figure 5 also shows the lowest KOSPI in In-crisis period, thus in-crisis period start in that month.

Result of newly devised model with crisis variable.
Table 3 and Figure 6 report the estimates of KRW-USD exchange exposure for the three sub-periods and distribution of exposure coefficients, respectively. The first thing to note is the change of sign on exposure coefficients and its implication that there actually was some structural change before and after crisis. Before the crisis, number of firms with positive exposure coefficients was 304(40%). However, after the crisis, the number went down to 159(21%). That can be interpreted that before the crisis, depreciation of KRW affected the value of 40% firms negatively, and after crisis most value of Korean firms are affected positively by depreciation of Korean won. This can also be explained by numerical evidence in Table 3b But there is some difference in number due to the size of sample selection. Table 3a Estimates of KRW-USD Exposure Coefficients β 3 with Crisis Variable

581(79%)
Parenthesis is percentage of positive and negative.

V. Determinant of Exchange Exposure
In the previous section, it has been proved that the estimated exposure coefficients varied substantially across companies. The purpose of this section is to identify whether exchange rate exposure is related to the size of firms and industries that the firms are in. Many previous researchers 13 empirically studied the link between exchange exposure and firm size and industry. Some study found systematic relationship but some didn't. 14 But we expect that most of Korean industries that depend on export and import would be highly exposed to exchange rate movements.
Each firm was divided by their size into two groups, that is, small and large with the criterion of total market value. Since industry code of Korea was revised on November 6 th 2000 from KSE, market price and the number of listed shares outstanding of November 3rd 2000 were used to keep consistence.
Large firms are the companies with greatest market value from the top to upper 10 percent and small firms are the companies that are in the lower 10 percent band. In the Table 4 and Table 5, all exposure coefficients are sorted by firm size and industry level. 13 Dominguez, Chang-Young Chung, Byung-Joo Lee, Gordon M.Bonar M.H. Franco Wong. 14 In , "We find that exposure is not systematically related to firm size.
Even though small firms and large firms have the same negative sign and similar magnitude, that is, they are negatively affected by depreciation of KRW-USD exchange rate; size plays a significant role in explaining the KRW-USD exchange exposure. Table 5 reports that 55 out of 70 large companies (83%) are exposed to KRW-USD exchange rate movements and 26 out of 70 small companies (37%) at 10 percent significance level. That can be interpreted that the bigger the firm is, the more exposure to exchange rate movement the firm has, and thus the exchange exposure has positive relationship with firm size.
To verify that larger firm is more exposed to exchange rate movement, we conduct two-tailed t-test using absolute mean of exposure coefficients. As the mean is the offsetting value between the positive and negative exposure coefficients, it is relevant to use absolute mean to examine relationship between the magnitude of exposure and firm size. Null hypothesis (H 0 ) is that the mean of small size firms equal to the mean of large and alternative hypothesis (H 1 ) is not equal. If we assume that μS is the Small firms' mean of foreign exchange exposure and μL is the mean of large firms' foreign exchange exposure, the hypotheses can be restated as following.
Therefore, the foreign exchange exposure is different by size. And this result is contrary to  where they didn't find systematic relationship between the foreign exchange exposure and firm size. reveals that as much as 25% of fishing industry is exposed to KRW-USD exchange rate movement, and, interestingly, communications industry is entirely out of exchange rate movement. And Electricity & Gas industry is wholly exposed to movement of exchange rate due to huge foreign debt and import.       1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 Year Amount Invested(Mill USD)    Exposure # of firms