A bivariate causality between stock prices and exchange rates: evidence from recent Asianflu☆

The Quarterly Review of Economics and Finance 40 (2000) 337–354

A bivariate causality between stock prices and exchange rates: evidence from recent Asian flu夞 Clive W.J. Grangera, Bwo-Nung Huangb,* Chin-Wei Yangc a

Department of Economics, University of California, San Diego, La Jolla, CA 92093-0508, USA b Department of Economics, National Chung-Cheng University, Chia-Yi, Taiwan 621 c Department of Economics, Clarion University of Pennsylvania, Clarion, PA 16214-1232, USA

Abstract This paper applies recently developed unit root and cointegration models to determine the appropriate Granger relations between stock prices and exchange rates using recent Asian flu data. Via impulse response functions, it is found that data from South Korea are in agreement with the traditional approach. That is, exchange rates lead stock prices. On the other hand, data of the Philippines suggest the result expected under the portfolio approach: stock prices lead exchange rates with negative correlation. Data from Hong Kong, Malaysia, Singapore, Thailand, and Taiwan indicate strong feedback relations, whereas that of Indonesia and Japan fail to reveal any recognizable pattern. © 2000 Bureau of Economic and Business Research, University of Illinois. All rights reserved. JEL classification: F300; G150 Keywords: Asian Flu; Bivariate Causality between Stock Prices and Exchange Rates

1. Introduction The financial crisis sparked in Thailand in July 1997 has sent shock waves throughout southeast Asia, South Korea, and Japan. On October 27, the short-run interest rate in Hong Kong took a huge jump to maintain its exchange rate to the US dollar. As a result, the Hang Seng Index plummeted 1438 points, setting off the crash in the US with the Dow Jones 夞 Financial assistance from the National Science Council of Taiwan (NSC88-2415-H-194-002) and Fulbright Scholarship Program is gratefully acknowledged. This paper is written during my tenure as a visiting scholar at UCSD. We are grateful to two anonymous referees for valuable suggestions. However, we wish to absorb all the culpability. * Corresponding author. E-mail address: [email protected] (B.-N. Huang). 1062-9769/00/$ – see front matter © 2000 Bureau of Economic and Business Research, University of Illinois. All rights reserved. PII: S 1 0 6 2 - 9 7 6 9 ( 0 0 ) 0 0 0 4 2 - 9

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Industrial Averages down by 554.26 points. On November 24, Yamaichi—the fourth largest financial corporation—filed for bankruptcy, which gave rise to a 854-point drop in Nikkei Index. In mid-December, the Korean Won depreciated drastically from 888 wons (per dollar) on July 1 to more than 2000 wons. The currency crisis in South Korea set off a financial avalanche in its stock markets, which witnessed a 50.3% freefall. Similar debacles also occurred in other Asian markets. After the devaluation of the Thai Baht (July 1997), other southeast Asian currencies over the next 2 months abandoned their close links to the US dollar and began to depreciate. The most severe pressures in foreign exchange markets, in the third quarter of 1997, were experienced by Thailand, Malaysia, the Philippines, and Indonesia, but the currencies of Singapore and a number of other Asian countries also weakened. As the pressure spread to Hong Kong and Korea in late October after the depreciation of the New Taiwan dollar, the scale of the crisis worsened significantly. The financial tsunami continued to exert its devastating force and did not slow down until the first quarter of 1998. This is known as the Asian flu in which stock prices and exchange rates plunged in tandem. Turmoils in both currency and stock markets are of paramount interest: does currency depreciation lead to stock market downfall or vice versa? Such a study provides an opportunity for analyzing dynamics between stock prices and exchange rates. If a statistical lead-lag relation can be ascertained, practitioners can profit from the arbitrage especially during a severe financial crisis. The bridge between the exchange rate and the asset market equilibrium is the interest parity condition according to Krugman and Obstfeld, (1997, Chapt. 16): R ⫽ R* ⫹ (Ee ⫺ E)/E, where R and R* are interest rates of domestic and foreign currencies and E and Ee denote exchange rate and expected future exchange rate respectively. For asset markets to remain in equilibrium, ceteris paribus, a decrease in domestic output (hence lower R due to reduced demand for money) must be accompanied by a currency depreciation (a greater value for E). Aggarwal (1981) has argued that a change in exchange rates could change stock prices of multinational firms directly and those of the domestic firms indirectly. In the case of a multinational firm, a change in exchange rate will change the value of that firm’s foreign operation, which will be reflected on its balance sheet as a profit or a loss. Consequently it contributes to current account imbalance. Once the profit or a loss is announced, that firm’s stock price will change. This argument shows that devaluation could either raise or lower a firm’s stock price depending on whether that firm is an exporting firm or it is a heavy user of imported inputs. If it involves in both activities, its stock price could move in either direction. This is true especially when most stock prices are aggregated to investigate the effects of devaluation on stock markets. From this viewpoint, exchange rate change is expected to give rise to stock price change. Such a causal relation is known as the traditional approach. However, as capital market become more and more integrated, changes in stock prices and exchange rates may reflect more of capital movement than current account imbalance. The central point of such a portfolio approach lies in the following logical deductions: A decrease in stock prices causes a reduction in the wealth of domestic investors, which in turn leads to a lower demand for money with ensuing lower interest rates. The lower interest rates encourage capital outflows ceteris paribus, which in turn is the cause of currency depreciation.1 Under the assumption of the portfolio approach, stock price is expected to lead

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exchange rate with a negative correlation. If a market is subject to the influences of both approaches simultaneously, a feedback loop will prevail with an arbitrary sign of correlation between the two variables. In the case of the US markets, empirical examinations of relation between stock returns and exchange rate changes have largely provided only weak evidence on the following relationship: exchange rate changes are significantly related to firm or portfolio stock returns (Jorion, 1990; Bahmani–Oskooee and Sohrabian, 1992; Amihud, 1993; Bartov and Badnar, 1994). Similar tests are performed by Bondnar and Gentry (1993) for Japan and Canada. In the case of emerging markets, only skimpy literature is available. For instance, the result of Abdalla and Murinde (1997) supports the traditional approach: exchange rate changes lead stock returns. However, their study includes only India, South Korea, Pakistan, and the Philippines. The recent Asian flu raises a rather puzzling question: in the wake of depressing stock and currency markets, which one is the culprit? The majority of previous studies support the traditional approach: exchange rate change leads stock return. Only a few indicate that opposite direction holds true or the feedback phenomenon exists. But they do not discuss the sign between exchange rate and stock price. Well-known in the literature, using monthly data may not be adequate in describing the effect of capital movement, which is intrinsically a short-run occurrence. In this paper, we employ daily data of the emerging markets in the hope that it will capture such effects. To further the analysis, we employ impulse response functions to explore the relation (signs) and its dynamics. In addition, we apply more advanced unit root and cointegration techniques, which account for structural break (for example, the Asian flu), to avoid the potential estimation bias. The organization of the paper is as follows: Section 2 describes empirical models; Section 3 introduces the unit root and cointegration models with structural break; Section 4 discusses the empirical result; Section 5 investigates some related problem of the model; and Section 6 provides a conclusion.

2. Discussion of empirical models Since (Nelson and Plosser’s (1982)), well-known paper, the unit-root property of macroeconomic variables has been widely accepted. As such, a unit-root test is often necessary before empirical studies. Based on the result by Dickey and Fuller (1979), the Augmented Dickey and Fuller (ADF) test is generally employed as shown below:

冘 ␪ ⌬y

k⫺1

⌬y t ⫽ ␣ ⫹ ␤ t ⫹ 共 ␳ ⫺ 1兲 y t⫺1 ⫹

i

t⫺i

⫹ at

(1)

i⫽1

where ⌬ ⫽ 1 ⫺ L; y t is a macroeconomic variable such as exchange rate or stock price; t is a trend variable; and a t is a white noise term. The null hypothesis is H 0 : ␳ ⫽ 1 and y t is said to possess the unit root property if one fails to reject H 0 . Nevertheless, the ADF test is suspect when the sample period includes some major events (for example, great depression, oil shocks). Failure to consider it properly can lead to erroneous conclusions in the case when the null is not rejected. To circumvent this problem,

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Perron and Vogelsang (1992) introduce dummy variable into Eq. (1) and recalculate the new set of critical values. However, as pointed out by Zivot and Andrew (1992, p. 251), a skeptic of Perron’s approach would argue that his choices of breakpoints are based on prior observation of the data, and hence problems associated with ‘pre-testing’ are applicable to his method.2 Consequently, they introduce an alternative formulation to overcome the pretesting problems.3

冘 ␪ ⌬y

k⫺1

⌬y t ⫽ ␣ ⫹ ␤ t ⫹ 共 ␳ ⫺ 1兲 y t⫺1 ⫹ ␥ DU t共 ␭ 兲 ⫹

i

t⫺i

⫹ at

(2)

i⫽1

where DU t ( ␭ ) ⫽ 1 for t ⬎ T ␭ , otherwise DU t ( ␭ ) ⫽ 0; ␭ ⫽ T B /T represents the location where the structural break lies; T is sample size; and T B is the date when the structural break occurred. Evident from Eq. (2), the estimation result hinges critically on the value ␭ as well. One of better ways to test the existence of a unit root is to choose the breakpoint that gives the least favorable result for the null of H 0 : ␳ ⫽ 1 using the test statistics t ␳ˆ ( ␭ ). That is, ␭ is chosen to minimize the one-sided t statistic for testing ␳ ⫽ 1, when small values of the statistic lead to the rejection of the null. Let ␭ˆ inf denote such a minimizing value for the model. Then, by definition, t ␳ˆ [ ␭ˆ inf] ⫽ inf␭僆⌳ (␭) where ⌳ is a specified closed subset of (0, 1) (Zivot and Andrew, 1992, p. 254). As is shown in this paper, a set of simulated critical values is employed for hypothesis testing during the period of the Asian flu. To investigate the stationarity assumption of several I(1) variables, majority of academicians still rely on the widely-accepted and easy-to-apply model proposed by Engle and Granger (1987) despite its normalization problem. Just as the ADF model fails to consider problems associated with structural breaks, the Engle–Granger formulation bypasses the same difficulty. Applying the similar approach by Zivot and Andrews (1992), Gregory and Hansen (1996) revise the Engle and Granger (1987) model to consider the regime shift via residual-based cointegration technique. The Gregory and Hansen model is a two-stage estimation process of which the first step is to estimate the following multiple regression: y 1t ⫽ ␣ ⫹ ␤ t ⫹ ␥ DU t共 ␭ 兲 ⫹ ␪ 1y 2t ⫹ e t

(3)

in which y 1t and y 2t are of I(1) and y 2t is a variable or a set of variables; and DU t ( ␭ ) has the same definition as that in Eq. (2). The second step is to test if e t in Eq. (3) is of I(0) or I(1) via the ADF or Phillips–Perron technique. If e t is found to be consistent with I(0), one may claim that cointegration exist between y 1t and y 2t . Once the statistical property of e t is established, one may adopt the bivariate VAR model to test the Granger causality. If the cointegration does not exist, the following formulation is needed in testing the hypotheses:

冘␣ k

⌬y 1t ⫽ ␣ 0 ⫹

冘␣ k

1i⌬y 1t⫺i ⫹

i⫽1

冘␤ k

⌬y 2t ⫽ ␤ 0 ⫹

2i⌬y 2t⫺i

⫹ ␧ 1t

i⫽1

i⫽1

(4)

冘␤ k

1i⌬y 1t⫺i ⫹

i⫽1

2i⌬y 2t⫺i

⫹ ␧ 2t

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in which y 1t and y 2t represent stock prices and exchange rates. Failing to reject the H 0 : ␣ 21 ⫽ ␣ 22 ⫽ . . . ⫽ ␣ 2k ⫽ 0 implies that exchange rates do not Granger cause stock prices. Likewise, failing to reject the H 0 : ␤ 11 ⫽ ␤ 12 ⫽ . . . ⫽ ␤ 1k ⫽ 0 suggests that stock prices do not Granger cause exchange rates. If cointegration exists between y 1t and y 2t , an error correction term is required in testing Granger causality as shown below:

冘␣ k

⌬y 1t ⫽ ␣ 0 ⫹ ␦ 1共 y 1t⫺1 ⫺ ␥ y 2t⫺1兲 ⫹

冘␣ k

1i⌬y 1t⫺i ⫹

i⫽1

冘 k

⌬y 2t ⫽ ␤ 0 ⫹ ␦ 2共 y 1t⫺1 ⫺ ␥ y 2t⫺1兲 ⫹

2i⌬y 2t⫺i

⫹ ␧ 1t

i⫽1

i⫽1

(5)

冘␤ k

␤ 1i⌬y 1t⫺i ⫹

2i⌬y 2t⫺i

⫹ ␧ 2t

i⫽1

in which ␦1 and ␦2 denote speeds of adjustment. According to Engle and Granger (1987), the existence of the cointegration implies a causality among the set of variables as manifested by 兩␦1兩 ⫹ 兩␦2兩 ⬎ 0. Failing to reject the H 0 : ␣ 21 ⫽ ␣ 22 ⫽ . . . ␣ 2k ⫽ 0 and ␦1 ⫽ 0 implies that exchange rates do not Granger cause stock prices while failing to reject H 0 : ␤ 11 ⫽ ␤ 12 ⫽ . . . ⫽ ␤ 1k ⫽ 0 and ␦2 ⫽ 0 indicates stock prices do not Granger cause exchange rates. 3. Data, time series trends, unit root, and cointegration results Included in this sample study are exchange rates and stock prices from Hong Kong (HKN), Indonesia (IND), Japan (JPN), South Korea (KOA), Malaysia (MAL), the Philippines (PHI), Singapore (SIG), Thailand (THA), and Taiwan (TWN). The sample period starts from January 3, 1986 to June 16, 1998. Daily data (5 days a week) in total of 3247 observations are from Datastream.4 Three subperiods are used to better dissect the relations between exchanges and stock prices. Period 1 (1987-Crash) covered from January 3, 1986 to November 30, 1987; Period 2 (After Crash) started on December 1, 1987 and ended on May 31, 1997; Period 3 (the Asian-Flu Period) continued from June 1, 1997 through June 16, 1998.5 All data points are transformed into logarithmic scale and are shown in time series plots (see Fig. 1). Evident from Fig. 1, all the nine economies exhibit pronounced structural breaks. That is, barring the Hong Kong dollar that maintained its peg to the US dollar, all other eight currencies suffered noticeable depreciations since July, 1997. During the period of July 1 ⬃ June 16, 1998, the Rupiah of Indonesia experienced the greatest slide in its value (144.83%), followed by Thai Bhat (55.50%), Won of South Korea (46.73%), Ringgit of Malaysia (45.82%), and Peso of the Philippines (45.52%). The rest of currencies witnessed between 18% and ⬃23% depreciation (Table 1). Similar freefalls in stock prices were witnessed ranging from 19.8% of the Taiwan market to 84.9% of the Malaysian market. As the Asian flu worsened, the performance of Pacific funds outside Japan fell 35.5% on average. Because the existence of the structural break is very clear from both Table 1 and Fig. 1, a revised model is necessary to explore the subtle relations between exchange rates and stock prices. To account for the structural change, we employ the Zivot and Andrew (1992) model to test the unit root property for the nine economies, and the result is reported in Table 2.

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Fig. 1a. Time Series of the Nine Asian Exchange Rates (in Logrithm)

Table 1 A comparison of daily stock prices and exchange rates between July 1 and June 16, 1998 Exchange rate

HKN

IND

JPN

KOA

MAL

PHI

SIG

THA

TWN

07/01/97 06/16/98 % Change Stock index 07/01/97 06/16/98 % Change

7.75 7.75 ⫺0.05% HKN 15196.79 7526.45 ⫺67.51%

2431.32 15197.57 144.83% IND 731.61 406.50 ⫺57.13%

114.90 143.33 22.02% JPN 20175.52 14720.38 ⫺31.27%

888.10 1429.59 46.73% KOA 758.03 380.91 ⫺66.22%

2.52 4.02 45.82% MAL 1078.90 435.84 ⫺84.91%

26.37 41.91 45.52% PHI 2815.54 1726.55 ⫺47.95%

1.43 1.73 18.82% SIG 494.00 289.04 ⫺52.35%

24.45 43.23 55.50% THA 527.28 263.39 ⫺66.75%

27.81 34.82 22.38% TWN 9030.28 7404.27 ⫺19.79%

HKN, Hong Kong; IND, Indonesia; JPN, Japan; KOA, Korea; MAL, Malaysia; PHI, Philippines; SIG, Singapore; THA, Thailand; TWN, Taiwan. All prices are based on daily market close; and all exchange rates are expressed as number of local currencies per US dollar.

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Fig. 1b. Time Series of the Nine Asian Stock Indexes (in Logrithm)

Table 2 Zivot and Andrew unit root results

Exchange rate Stock index

HKN

IND

JPN

KOA

MAL

PHI

SIG

THA

TWN

⫺7.28* [0.43] ⫺3.25 [0.96]

⫺6.77 [0.98] ⫺3.17 [0.17]

⫺2.47 [0.78] ⫺3.65 [0.36]

⫺4.98* [0.97] ⫺3.18 [0.95]

⫺6.38* [0.95] ⫺3.08 [0.95]

⫺4.21** [0.94] ⫺3.47 [0.92]

⫺3.10 [0.94] ⫺2.40 [0.95]

⫺11.0* [0.94] ⫺1.82 [0.88]

⫺2.95 [0.97] ⫺3.36 [0.06]

k⫺1 Note: The estimation result is based on ⌬y t ⫽ ␣ ⫹ ␤ t ⫹ ( ␳ ⫺ 1) y t⫺1 ⫹ ␥ DU t ( ␭ ) ⫹ ¥ i⫽1 ␪ i ⌬y t⫺i ⫹ a t , where DU t ( ␭ ) ⫽ 1 for t ⬎ T ␭ ; otherwise, DU t ( ␭ ) ⫽ 0. ␭ ⫽ T B /T denotes the location of the structural break, T ⫽ sample size, TB ⫽ date of structural break; * ⫽ 1% significance level; critical value for various ␭ are from Table 3 provided by Zivot and Andrew (1992). Value of ␭ are in brackets. For ␭ ⫽ 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, the 5% critical values are ⫺3.65, ⫺3.80, ⫺3.87, ⫺3.94, ⫺3.96, ⫺3.95, ⫹3.95, ⫺3.82, ⫺3.68, respectively.

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Table 3 Results of Gregory and Hansen cointegration test

y 1t on y 2t y 2t on y 1t

JPN

SIG

TWN

⫺3.08 ⫺3.59

⫺4.65 ⫺3.11

⫺3.00 ⫺3.17

Note: The first-stage estimation is based on y 1t ⫽ ␣ ⫹ ␤ t ⫹ ␥ DU t ( ␭ ) ⫹ ␪ 1 y 2t ⫹ e t . The second-stage estimation is to apply traditional ADF or Phillips–Perron approach to test if e t is of I(0) or I(1). The y 1t on the first row represents exchange rate and y 2t denotes stock price. The reverse is true for the second row. The critical values are taken from Table 1 in Gregory and Hansen (1996).

A perusal of Table 2 reveals that the null hypothesis of a unit root in stock indexes cannot be rejected using the Zivot and Andrew approach. Nonetheless, the same cannot be said about exchange rate markets.6 In particular, the null hypotheses of a unit root are easily rejected for the markets of Hong Kong (Dollar), Indonesia (Rupiah), Korea (Won), Malaysia (Ringgit), the Philippines (Peso), and Thai (Bhat). The prevalence of lacking a unit root may be attributed to the fact that these currencies are pegged to the US dollar before the crisis. The lack of the unit root property within the sample periods enables us to apply the statistical model readily in terms of exchange rates and rate of change in stock price. Note that from prior studies, rate of change in exchange rates was found to mirror exchange rate exposure, hence it may have better economic interpretation. It is to be pointed out that the cointegration analysis is not needed for the time periods in which the logarithmic exchange rates are of I(0). Given the outcome of the unit root test, we present the cointegration results based on the Gregory and Hansen model (1996) in Table 3. The first line in Table 3 lists the statistics when y 1t (stock price) is regressed on y 2t (exchange rate) whereas the statistics of the reverse-order regressions are reported in the second line. In all cases of y 2t ⫽ stock prices, y 1t ⫽ exchange rates, we fail to reject the null hypothesis that no cointegration exists between exchange rates and stock prices.7 This being the case, we employ Eq. (4) to explore the relations between exchange rates and stock prices in the Asian markets.

4. Discussion of empirical results Despite the structural break in these Asian markets, traditional Granger causality test or Eq. (4) would suffice for studying the relations between exchange rates and stock prices. Based on the Schwarz Bayesian (SB) criterion, the optimum lag (k), except for Hong Kong (k ⫽ 3), is one for all other markets. The causality results for the three sub-periods are reported in Table 4. During Period 1 (Jan. 3, 1986 ⬃ Nov. 30, 1987), there existed little interaction between currency and stock markets except for Singapore. An examination indicates changes in the exchange rates lead that of the stock prices in Singapore. Our result is in agreement with the majority of prior studies using monthly data: only a few found either exchange rates lead stock prices or there is no interaction.

0.0835 0.1086 0.7331 0.9916 0.2814 0.9745 0.6587 0.8065 0.4240 0.5389 0.3622 0.3046 0.2266 0.0949 0.5027 0.3208 0.0396 0.6552

3.3090** 1.3396 0.7344 1.2347 0.1392 1.3756 0.0008 17.8604* 2.6366*** 3.6616** 2.5880 1.2832 5.2427** 13.5657* 6.8189* 3.7305** 10.9414* 7.4313*

0.0700 0.2481 0.3922 0.2675 0.7094 0.2419 0.9781 0.0000 0.1056 0.0567 0.1088 0.2583 0.0228 0.0003 0.0095 0.0545 0.0011 0.0068

3.7826*** 0.9360 0.7351 0.7009 0.2729 2.6785*** 0.0019 18.8471* 2.5562 2.5767 2.8004*** 1.2201 5.2784** 12.2910* 6.6287** 3.8431** 11.8221* 7.1519*

0.0528 0.3342 0.3920 0.4032 0.6018 0.1029 0.9650 0.0000 0.1110 0.1096 0.0954 0.2703 0.0224 0.0005 0.0106 0.0510 0.0007 0.0079

3.8322** 2.3427 0.7278 1.2733 0.0887 1.3870 0.0057 17.9082* 2.6290*** 3.6450*** 2.6042 1.6981 5.0330** 13.8289* 6.7110* 3.8607** 11.0286* 7.4039*

0.0513 0.1271 0.3943 0.2602 0.7661 0.2399 0.9399 0.0000 0.1061 0.0573 0.1078 0.1937 0.0257 0.0002 0.0101 0.0505 0.0010 0.0069

3.3467*** 1.4824 0.7388 1.2338 0.1196 1.3440 0.0045 17.7958* 2.6469*** 3.6592** 2.2793 1.3077 5.2446** 12.0027* 6.6796* 3.7794** 11.1004* 7.6613*

0.0684 0.2245 0.3908 0.2677 0.7297 0.2474 0.9466 0.0000 0.1049 0.0568 0.1323 0.2538 0.0228 0.0006 0.0103 0.0529 0.0010 0.0060

3.4998*** 1.3413 0.7445 1.5493 0.1430 1.5992 0.0003 19.0854* 2.7164*** 3.7230*** 2.6089 1.3863 6.0148** 13.5391 7.0278* 3.6829** 10.8029* 7.7743*

0.0625 0.2478 0.3890 0.2143 0.7056 0.2071 0.9872 0.0000 0.1005 0.0547 0.1074 0.2401 0.0148 0.0003 0.0085 0.0560 0.0011 0.0057

Note: –/3 implies does not Granger cause. * ⫽ 1% significance level; ** ⫽ 5% significance level; *** ⫽ 10% significance level. The abbreviation for each market denotes exchange rate variations (e.g. HKN ⫽ rate of changes of the Hong Kong dollar or simply Hong Kong exchange rate variations) of the market. The prefix S represents rate of changes in stock prices of the market (e.g. SHKN ⫽ rate of changes of Hong Kong stock prices or simply stock prices changes of the Hong Kong market). a Changes in the U.S. stock index are included. b Federal fund rates are included. c Difference of federal fund rates is included. d Error correction term s t⫺1 ⫺ e t⫺1 is included where s t : logrithmic stock prices, e t : logrithmic exchange rates.

2.2231*** 2.0227 0.1163 0.0001 1.1606 0.0010 0.1952 0.0600 0.6395 0.3777 0.8305 1.0546 1.4630 2.7911*** 0.4493 0.9862 4.2392** 0.1995

0.9281 0.6170 0.6356 0.6669 0.2305 0.1646 0.2971 0.7399 0.6208 0.6591 0.9209 0.8816 0.2379 0.0295 0.7115 0.3567 0.1891 0.3293

0.0082 0.2504 0.2249 0.1855 1.4411 1.9373 1.0895 0.1104 0.2451 0.1948 0.0099 0.0222 1.3964 4.7670** 0.1370 0.8512 1.7295 0.9534

SHKN –/3 HKN HKN –/3 SHKN SIND –/3 IND IND –/3 SIND SJPN –/3 JPN JPN –/3 SJPN SKOA –/3 KOA KOA –/3 SKOA SMAL –/3 MAL MAL –/3 SMAL SPHI –/3 PHI PHI –/3 SPHI SSIG –/3 SIG SIG –/3 SSIG STHA –/3 THA THA –/3 STHA STWN –/3 TWN TWN –/3 STWN

p Value

97.6.1 ⬃ 98.6.16d (7)

p Value F Value

97.6.1 ⬃ 98.6.16c (6)

p Value F Value

97.6.1. ⬃ 98.6.16b (5)

p Value F Value

97.6.1. ⬃ 98.16a (4)

p Value F Value

97.6.1 ⬃ 98.6.16 (3)

p Value F Value

87.12.1 ⬃ 97.5.31 (2)

p Value F Value

86.13 ⬃ 87.11.30 (1)

H0: exch –/3 stock F Value

H0: stock –/3

Table 4 The causality test between changes in exchange rates and changes in stock prices for the nine Asian economics

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In Period 2 (or After-Crash period), there is no definitive pattern of interaction between the two markets. As is shown in Table 4, changes in exchange rates lead that in stock prices in the case of Singapore (␣ ⱖ 10%) again. Additionally, changes in stock prices are found to lead exchange rate changes in Taiwan (␣ ⬍ 5%), and in Hong Kong (␣ ⱖ 10%). Known for its volatility, the Taiwan stock market reached its peak at 12 000 points in February 1990, and fell to as low as 4000 points in the ensuing recession. It rebounded again back to about 10 000 points, and fell to 7100 points in the midst of the Asian flu. The exchange rate of the Taiwan dollar, built on large foreign exchange surplus from export-led economic growth, had remained fairly stable (at approximately 38 to 40 dollars per US dollar) before 1988. Since then, its currency began to appreciate in value owing to the pressure from the US. The appreciation took place gradually so to minimize the impact on firms. Such a policy was indeed considered suitable to alleviate adverse effects for domestic manufacturers, but served as an open invitation to “hot money” that flowed into Taiwan’s financial market. The overflow of the hot money inevitably led to the overheated stock market and overvalued prices. In addition, the overflow of the hot money noticeably increased the demand for the Taiwan dollar that in turn exerted the pressure for its currency appreciation. Fig. 2 illustrates a picture of short-term capital inflows into Southeast Asia for the past 10 years. In the first part of 1990s, before the Asian financial crisis, capital inflows were on the rise. The increased capital inflows strengthen the local currency such as the New Taiwan dollar hence its stock prices, as is anticipated under the flexible exchange rate. However, the capital flight occurred in the fourth quarter of 1994, when China lobbed missiles around the island. Consequently, the stock market experienced severe setbacks, which triggered currency depreciation. This phenomenon is consistent with the portfolio approach. In contrast, Singapore did not experience significant amount of capital inflows. And with her current account in black frequently, its export-led economy, leads the stock market (see Fig. 3). This seems to be in agreement with the traditional approach. No definitive pattern has been identified between currency and stock markets according to the previous literature. However, such is not the case during Period 3 (the Asian flu period). Based on our analysis, seven of the nine nations suggest significant relations between the two markets. In the case of South Korea, changes in the exchange rates lead that in stock prices.8 The reverse direction is found in Hong Kong and the Philippines. The rest of the markets (Malaysia, Singapore, Thailand, and Taiwan) are characterized by feedback interactions in which change in exchange rates can take the lead and vice versa. Given the definitive patterns, the Granger causality test does not provide signs of these relations. To examine the short run dynamic relations of the Asian markets during Period 3, we take advantage of the impulse response (IR) functions and calculate standard deviation for each forecasting period.9 The IR (10 periods) from shocks of each variable are shown in Table 5. An inspection of Table 5 reveals that the results from the IR analysis are in conformity with that of the Granger causality test. This is to say, if the Granger causality test indicates change in stock prices leads that in exchange rates, the responses of stock prices from one-unit shock of exchange rates are insignificant. Such is the case for Hong Kong and the Philippines as shown in lower part of Table 5 (panel B). Overall, three distinguishing patterns can be identified from Table 5 from the IR analysis of the nine economies in Asia. First, one-unit shocks from the exchange rates have very discernible negative responses on

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347

Fig. 2. Current Account and Portfolio Investment (Liability) of The Eight Asian Economies

their corresponding stock prices. This is clearly the case for South Korea. The IR analysis provides further short-run dynamic relations (sign and timing) above and beyond that obtained from the Granger causality test. For instance, it takes Korean stocks within the first 3 days to respond (negatively). The negative responses are found to be in Day 1, 2, and 3 after the one-unit shock was administered in Korea. The IR analysis for Korea confirms the validity of the traditional approach. According to the traditional approach, exchange rate leads stock price, but the correlation can go either way depending on whether the individual firm is an exporter (output) or an importer (raw material). The net effect of the aggregation (stock index) cannot be determined hence the sign is arbitrary. In the Hong Kong and the Philippines markets, one standard deviation decrease in stock prices gave rise to the depreciation of the Hong Kong dollar and Philippines Peso in the first day and first 2 days, respectively. In the markets of Malaysia, Singapore, Thailand, and Taiwan, the feedback relations prevailed. The portfolio approach (stock price leads exchange rate) suggests a negative correlation while the traditional approach (exchange rate leads stock price) implies an arbitrary correlation. As a result, the net correlation under the feedback relation—a simultaneous combination of the two approaches—is uncertain. Barring Thai-

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Fig. 3. Exchange Rates and Stock Indexes of the Nine Asian Economies (Quarterly Data)

land, one unit change in exchange rates has largely negative impacts on stock prices with the length of two periods (for example, Malaysia, Singapore, and Taiwan as shown in Table 5). In the case of one unit change in the stock prices, we observe the negative impacts on the corresponding exchange rates: Thai Baht (three periods), Taiwan Dollar (two periods), Malaysian Ringgit (one period), and Singapore Dollar (one period). The result of our analysis indicates that (1) there existed no relation between the stock and the exchange rate markets for Japan and Indonesia; (2) the exchange rate led the stock price in Korea; and (3) stock prices led exchange rates to some extent for the rest of the markets. Given these results, capital flow seems to be a major cause for currency depreciations. The capital flight from Mexico in the Peso crisis (1994) went to equity markets of southeast Asia (emerging markets) high growth area. The enormous amount of capital inflow, however, does not cause exchange rates to change as most of local currencies are pegged to the US dollar. This explains why we do not observe the phenomenon consistent with the portfolio approach in Period 2. Unfortunately, trade deficit as well as inefficient banking system brought about massive capital outflow during the crisis period. Central banks, unable to

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Table 5 Estimation result of impulse response function Panel A: response of exchange rates from one-unit shock in stock price change Period HKN KOA MAL PHI SIG 1 2 3 4 5 6 7 8 9 10

⫺0.0071*** ⫺0.0060 0.0032 ⫺0.0014 0.0006 ⫺0.0003 0.0001 0.0000 0.0000 0.0000

0.0000 0.0054 0.0014 0.0003 0.0001 0.0000 0.0000 0.0000 0.0000 0.0000

0.0000 ⫺0.1729*** ⫺0.0130 ⫺0.0034 ⫺0.0004 ⫺0.0001 0.0000 0.0000 0.0000 0.0000

⫺0.2139* ⫺0.1867** ⫺0.0659 ⫺0.0201 ⫺0.0059 ⫺0.0017 ⫺0.0005 ⫺0.0001 0.0000 0.0000

0.0000 ⫺0.0978* 0.0005 ⫺0.0040 0.0000 ⫺0.0002 0.0000 0.0000 0.0000 0.0000

Panel B: response of stock prices from one-unit shock in exchange rate change Period HKN KOA MAL PHI SIG 1 2 3 4 5 6 7 8 9 10

0.0000 0.1837 ⫺0.0918 0.0397 ⫺0.0167 0.0070 ⫺0.0029 0.0012 ⫺0.0005 0.0002

⫺0.7670* ⫺0.8979* ⫺0.2258* ⫺0.0488 ⫺0.0102 ⫺0.0021 ⫺0.0004 ⫺0.0001 0.0000 0.0000

⫺1.2877* ⫺0.4687* ⫺0.0531 ⫺0.0105 ⫺0.0015 ⫺0.0003 0.0000 0.0000 0.0000 0.0000

0.0000 ⫺0.1455 ⫺0.0561 ⫺0.0175 ⫺0.0051 ⫺0.0015 ⫺0.0004 ⫺0.0001 0.0000 0.0000

⫺0.4740* ⫺0.4343* ⫺0.0167 ⫺0.0175 ⫺0.0006 ⫺0.0007 0.0000 0.0000 0.0000 0.0000

THA

TWN

⫺0.2073*** ⫺0.3353* ⫺0.0896* ⫺0.0160 ⫺0.0020 ⫺0.0001 0.0000 0.0000 0.0000 0.0000

⫺0.0853* ⫺0.1321* ⫺0.0170 ⫺0.0065 ⫺0.0013 ⫺0.0004 ⫺0.0001 0.0000 0.0000 0.0000

THA

TWN

0.0000 0.2965** 0.0844** 0.0156 0.0020 0.0001 0.0000 0.0000 0.0000 0.0000

0.0000 ⫺0.2636* ⫺0.0278 ⫺0.0123 ⫺0.0023 ⫺0.0007 ⫺0.0002 0.0000 0.0000 0.0000

Note: * ⫽ 1% significance level; ** ⫽ 5% significance level; *** ⫽ 10% significance level.

maintain the pegged rates, can do nothing but let their currency values slide: a floating exchange rate system in which we witness the result predicted under portfolio approach. For detailed economic and financial descriptions readers are refereed to Kochhar et al. (1998).

5. Other related problems of the model One of the frequently addressed problems in security price is the existence of predictable portion of changes in stock prices. Based on the Granger causality, in five of the nine nations, variations in exchange rates could be used to predict stock prices changes in Period 3 as shown in Table 6.10 Reported in Table 6 are comparisons of adjusted R-squares (R៮ 2 ) between the regression in which current stock price change is the dependent variable and its lagged (k ⫽ 1) variables are independent and the regression in which current stock price change is the dependent variable and lagged (k ⫽ 1) variables of exchange rate changes and stock price changes are independent variables.11 An examination of Table 6 indicates that past information (within a day) on exchange rate variations is useful in predicting stock price changes in the five markets. This is reflected in increased adjusted R៮ 2 from 18% to 293%. That is, investors can better predict stock price change based on exchange rate changes by adding changes of lagged (by one period) exchange rates.

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Table 6 A comparison of predictable portion of stock price changes (1) R៮ 2 (2) R៮ 2 (2) vs. (1)

KOA

MAL

SIG

THA

TWN

0.0147 0.0851 141.1%

0.0127 0.0223 54.9%

0.0181 0.0616 109.2%

0.0474 0.0569 18.2%

⫺0.0037 0.0196 293.1%

Note: R៮ 2 ⫽ adjusted R-square. (1): R៮ 2 from the regression in which current stock price change is the dependent variable and its lagged values (k ⫽ 1) are independent variables. (2): R៮ 2 from the regression in which current stock price change is the dependent variable and lagged values (k ⫽ 1) of exchange rate variations and stock price changes are independent variables. (2) vs. (1) ⫽ percentage increases in R៮ 2 percentage ⫽ ((2) ⫺ (1))/(((2) ⫹ (1))/2) ⫻ 100%.

To test for robustness of the results, we have tried several alternative specifications in which stock prices changes of the US and Japan are included. Trades with the US and Japan historically play an important role in the region. A recent study by Wei et al. (1995) indicates that US stock market exerts more impact on Asian stock markets than does the Japanese market. For this reason, we include stock price changes of the US market in Eq. (4) to reexamine the Granger causality model (Table 4, column 4).12 As is evident from Table 4, with the exception of Japan and Malaysia, the lead-lag structures remain virtually unchanged. During the period of Asian financial crisis, it seems worthwhile to include a variable reflecting fundamental shocks other than the US and Japanese stock prices. Because daily data of other economic variables are difficult to obtain, we use the interest rates (federal fund rate) as a proxy to reflect fundamental shocks. In addition, interest rates are very much related to exchange rates and stock prices, and as such mirror economic prospect to some extent. Often used in the literature, difference of interest rates between the US and southeast Asian economies reveals the capital flow between the two nations. For this reason, we incorporate the interest rates and the differences into Eq. (4) to perform the Granger causality test. The result shown in Table 4 [column (5) and (6)] indicates that the conclusions remain about the same except in the case of the Philippines, in which the significance level (stock price leads exchange rate) decreases somewhat. The overall lead-lag relations remain unchanged in general with added interest rate variables. No cointegration relation between stock prices and exchange rates is found, perhaps due to the noise from using daily data, as indicated by the statistical analysis. However, by including the error correction term in Eq. (4), we are able to examine the potential short-term causality and long-term equilibrium relations. Adding the error correction term ( y 1t⫺1 ⫺ y 2t⫺1 ) into Eq. (4), where y 1t and y 2t denote stock price and exchange rate, we have:

冘␣ k

⌬y 1t ⫽ ␣ 0 ⫹ ␦ 1共 y 1t⫺1 ⫺ y 2t⫺1兲 ⫹

i⫽1

冘 k

⌬y 2t ⫽ ␤ 0 ⫹ ␦ 2共 y 1t⫺1 ⫺ y 2t⫺1兲 ⫹

i⫽1

冘␣

2i⌬y 2t⫺i

冘␤

2i⌬y 2t⫺i

k

1i⌬y 1t⫺i ⫹

⫹ ␧ 1t

i⫽1

(6)

k

␤ 1i⌬y 1t⫺i ⫹

i⫽1

⫹ ␧ 2t

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351

Table 7 Estimation result of impulse response function (reverse order) Panel A: response of exchange rates from one-unit shock in stock price change Period HKN KOA MAL PHI SIG 1 2 3 4 5 6 7 8 9 10

0.0000 ⫺0.0091*** 0.0045*** ⫺0.0020 0.0008 ⫺0.0003 0.0001 ⫺0.0001 0.0000 0.0000

⫺0.7892* ⫺0.1602 ⫺0.0329 ⫺0.0068 ⫺0.0014 ⫺0.0003 ⫺0.0001 0.0000 0.0000 0.0000

⫺0.7782* ⫺0.1971*** ⫺0.0256 ⫺0.0047 ⫺0.0007 ⫺0.0001 0.0000 0.0000 0.0000 0.0000

0.0000 ⫺0.1540*** ⫺0.0593 ⫺0.0185 ⫺0.0054 ⫺0.0016 ⫺0.0005 ⫺0.0001 0.0000 0.0000

⫺0.2040* ⫺0.0829*** ⫺0.0078 ⫺0.0033 ⫺0.0003 ⫺0.0001 0.0000 0.0000 0.0000 0.0000

Panel B: response of stock prices from one-unit shock in exchange rate change Period HKN KOA MAL PHI SIG 1 2 3 4 5 6 7 8 9 10

⫺0.2504 0.1995 ⫺0.0913 0.0389 ⫺0.0164 0.0069 ⫺0.0029 0.0012 ⫺0.0005 0.0002

0.0000 ⫺0.8285* ⫺0.2164* ⫺0.0472 ⫺0.0099 ⫺0.0020 ⫺0.0004 ⫺0.0001 0.0000 0.0000

0.0000 ⫺0.3400*** ⫺0.0255 ⫺0.0066 ⫺0.0009 ⫺0.0002 0.0000 0.0000 0.0000 0.0000

⫺0.2819** ⫺0.2141 ⫺0.0745 ⫺0.0226 ⫺0.0066 ⫺0.0019 ⫺0.0005 ⫺0.0002 0.0000 0.0000

0.0000 ⫺0.3773* 0.0021 ⫺0.0153*** 0.0002 ⫺0.0006 0.0000 0.0000 0.0000 0.0000

THA

TWN

0.0000 ⫺0.3250* ⫺0.0926** ⫺0.0171 ⫺0.0022 ⫺0.0002 0.0000 0.0000 0.0000 0.0000

0.0000 ⫺0.1201* ⫺0.0127 ⫺0.0056 ⫺0.0010 ⫺0.0003 ⫺0.0001 0.0000 0.0000 0.0000

THA

TWN

⫺0.2531*** 0.2365 0.0746 0.0145 0.0020 0.0002 0.0000 0.0000 0.0000 0.0000

⫺0.2320* ⫺0.2613* ⫺0.0358 ⫺0.0130 ⫺0.0026 ⫺0.0007 ⫺0.0002 0.0000 0.0000 0.0000

Note: * ⫽ 1% significance level; ** ⫽ 5% significance level; *** ⫽ 10% significance level.

To test whether exchange rates Granger-cause stock prices, we examine the null hypothesis H 0 : ␦ 1 ⫽ 0 and ␣ 21 ⫽ ␣ 22 ⫽ . . . ⫽ ␣ 2k ⫽ 0. Conversely, to test whether stock prices Granger-cause exchange rates, we examine H 0 : ␦ 2 ⫽ 0 and ␤ 21 ⫽ ␤ 22 ⫽ . . . ⫽ ␤ 2k ⫽ 0. Including the error correction terms does not alter the lead-lag relations noticeably [Eq. (7) in Table 4]. Besides, estimated ␦1 and ␦2 are insignificant (not shown), suggesting a lack of long-term equilibrium relations between stock prices and exchange rates. It is to be pointed out that these results are derived from estimating reduced form equations, and as such are open to questions of the endogeneity of the two variables. In that case, structural equations may be preferred. On the other hand, proponents of VAR models do not favor the structural form approach (Canova 1995, pp. 103–109). One problem associated with an unrestricted VAR model is the sensitivity of the results in response to different order of variable appearances. To examine the robustness of impulse responses under different contemporaneous identifying assumptions, we alter the order of variable appearances, and report the result in Table 7.13 An examination of Table 7 indicates the impulse response functions (mostly negative correlation) are similar to that of Table 5 save that the lengths of impact are different. In the case of Korea, as we reverse the order of the two variables, the Granger causality that exchange rate change leads stock price change becomes a feedback relation: a relation that

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dominated the southeast Asian economies. In general, the Granger causality remains robust in the period of the Asian flu.

6. Conclusion Prior studies based on monthly data have found either little relation can be established between the two markets or exchange rate market leads stock market. In this paper, we apply the recently advanced statistical techniques with daily data to analyze the problem in the Asian economies. The result indicates markets were largely characterized by the phenomenon predicted under the portfolio approach. Most markets exhibit either changes in stock prices lead that in exchange rates or either market can take the lead (feedback interaction). Built on the result of the Granger causality test, the IR analysis lends further support to the importance of stock market as the leader or the existence of feedback interaction during the Asian flu period. Likewise, the inclusion of exchange rate variations (within a day) can improve predictable portion of stock price changes in the five Asian markets. In the early 1990s as barriers to capital movement were gradually removed, the role of the portfolio approach cannot be downplayed. Capital movement into and out of the Asian economies is as beneficial as it is detrimental. The Asian flu certainly has put the stock and the exchange rate markets in a spotlight that suggests financial markets in the Asian economies need an overhaul. Although the main objective of this paper is to explore short-term dynamic relations between stock prices and exchange rates, it also provides a potential avenue for future research in the spillover effects in southeast Asia. It needs to be pointed out that although the Granger-Causality and IR may exhibit some statistical relations, it is sometimes difficult to interpret the underlying fundamental economic relation based on those results. It is likely that the results may be generated from other structure relations, i.e., via interest rate parity condition or IS-LM-related policies. For example, some recessionary shocks or unfavorable information on the country will cause a stock price decrease and an exchange rate depreciation. In this case the timing relation between the stock price and exchange rate will be generated from the relative efficiency of the stock market and foreign exchange market. In this case, both approaches may not play any role in generating the timing relation.14

Notes 1. For an intuitive explanation of the portfolio approach, see Krueger (1983, pp. 81–91). Other related works include Dornbusch (1975), Frenkel and Rodriguez (1975), and Boyer (1977). 2. The strength of the Zivot and Andrew approach lies in endogeneizing the breakpoint, albeit a single one. Recently, tests with multiple break points are made possible by Bai and Perron (1998), Garcia and Perron (1996), Liu et al. (1997), and Andrews et al. (1996). 3. There are three different models provided by Zivot and Andrew (1992). We adopt the

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4. 5. 6.

7. 8.

9. 10.

11. 12.

13.

14.

353

unit-root model as shown in Eq. (2) due to the existence of a trend in the time series. Similar results are obtained using the other two models proposed by them. We are grateful for the generosity extended by the economics department of UCSD in providing the data. After the convention in event study, the breakpoint for Period 3 would be approximately 1 month before the depreciation of the Thai Baht. Note that ␭ values in 11 of the 18 unit root tests exceed the 95% value, indicating that the Asian financial debacles have indeed caused structural changes. Such structures break toward either end of the data could cast a doubt on the power of the Zivot and Andrew unit root test statistic. It remains an interesting research topic in the future. The cointegration test is performed only on the national stock prices and exchange rates with the property of I(1), i.e., including that of Japan, Singapore, and Taiwan. The significance level is greater than 10% in the case of the Philippines. The impulse response functions indicate that change in stock price leads that in exchange rate. As such, the Philippines market can be characterized by the portfolio approach. As the cointegration relations are not found between the variables, we calculated the IR functions based on Eq. (4). In the case of Hong Kong, the analysis indicates that exchange rate changes lead that of stock prices, and as such the predictable portion of stock price changes cannot be improved upon noticeably. Harvey (1995) employs adjusted R-square to investigate the predictability of stock prices for both emerging and developed markets. To conserve space, we present only the results in which changes in the US stock prices are included. The lead-lag structures remain unchanged as well if Japanese data are included in Eq. (4). The variable order of the IR functions in Table 5 is based on that from the causality tests shown in Table 4. For instance, changes in stock prices lead that in exchange rates in the Hong Kong market. Hence, the variable order in which the IR functions are calculated (Table 5) is stock price changes– exchange rate changes. The order is reversed to exchange rate–stock price and the results of the IR function calculations are reported in Table 7. We are indebted to one of the reviewers for pointing this possibility out.

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