who moves the asia-pacific stock

8/2/2019 Who Moves the Asia-Pacific Stock

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FinancialheReview

EFAEasternF i a n c eAssociation The Financial Review 34 (1999) 159-170

Who Moves the Asia-Pacific StockMarkets-US or Japan? EmpiricalEvidence Based on the Theory ofCointegrationAsim Ghosh*

Reza SaidiKeith H. Johnson

Saint Josephs Universiv

The Catholic University of America

University of Kentucky

AbstractTh is study examines the recent debacle of the Asian-Pacific stock markets by utilizingthe theory of cointegration to investigate which developing m arkets are moved by the marketsof Japan and the United States. The empirical evidence suggests that some countries aredominated by the US, some aredominated by Japan, and the remaining countries are dominated

by neither during the time period investigated. The appropriate error correction model isestimated and is used to perform out-of-sample forecasting.Keywords: cointegration, international, marketsJE L classification: G15

1. IntroductionThe integration of Asian-Pacific capital markets has increased over the lastseveral years with the decrease in government imposed formal barriers to the flow

*Correspondjng author: Saint Josephs University, Department of Finance, Philadelphia, PA 19131 -1395;Phone: (610) 660-1651, Fax: (610) 660-1986, E-mail: [email protected]

159


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160 A. Ghosh, R. Saidi, K.H. JohnsodThe Financial Review 34 (1999) 159-170of capital across countries. Furtherm ore, the development and grow th of derivativesecurities have stimulated financial integration. Some of this has taken place withlittle notice paid by the popular press, but the turmoil in the latter part of 1997 hasbrought worldwide attention to these developing markets.Economic theory posits that certain pairs of financial time series are expectedto move together in the long run. In the short run they may dev iate from each other,but investors tastes and preferences, market forces and governm ent regulations willbring them back to their equilibrium.Kasas (1992) investigation of five major international stock market reportsfound a single common stochastic trend in the (developed) markets of the UnitedStates, Japan, England, G ermany and C anada. However, Corhay, Rad and Urbain(1995) found no evidence of a single comm on stochastic trend in their examinationof the stock markets of Australia, Japan, Hong Kong, New Zealand and Singaporefor the period February 1972 through February 1992. Kwan, Sim and Cotsomitis(1995) studied the stock markets of Australia, Hong Kong, Japan, Singapore, SouthKorea, Taiwan, the UK , the US and Germany employing monthly data from January1982 through February 1991. Their evidence suggests that these markets are notweak form efficient as they find significant lead-lag relationships between equitymarkets. However, the investigation of the stock markets of Hong Kong, Korea,Malayasia, Singapore and Taiwan by Hung and Cheung (1995) using weekly datafrom January 1981 through December 1991 found no evidence of cointegration.Although these stud ies are not directly comparable as different differencing intervalsare employed (quarterly, mon thly, and weekly) as well as different years and differentmarket indexes as measures of the respective markets, the studies do suggest thepossibility that there may be a significant relationship between the less developedAsian-Pacific markets and the markets of the two developed markets which havemajor economic connections to these A sian countries.The purpose of this study is to investigate the recent turbulence of the Asian-Pacific stock markets to look for evidence of a relationship between either or bothof the developed markets of the US and Japan and the less-developed Asian-Pac ificmarkets of Hong Kong (HK), India (I), Korea (K), Taiwan (T), Malayasia (M),Singapore (S), Indonesia (IN), Philippines (P), and Thailand (TH). Specifically, thegoal is to determine if it is the stock market of the U nited States (US), or the stockmarket of Japan (JP), or both, that moves the Asian-Pacific markets. Since Japanis a major investor and trading partner and has political influence on many Pacific-Basin countries, it is expected that the financial markets of Tokyo and the Asian-Pacific countries may be related. Likewise, due to the size and world economicimportance of the United States markets, these markets potential influence on othermarkets cannot be ignored. A finding of a differential impact of the developedmarkets on the emerging Asian-Pacific markets can lead to further insights intosocio-economic connections. Obviously, the investigation of lead-lag relationshipsand related issues will provide useful information to both domestic and foreigninvestors.


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A. Ghosh, R. Saidi, K.H. JohnsodThe Financial Review 34 (1999) 159-170 161Our study differs from the previously mentioned studies in several ways. Thisstudy includes two developed markets with nine emerging A sian-Pacific markets.Many of the emerging markets included in this study have not been previouslyexamined in conjunction with each other o r with the two developed m arkets. Also,value-weighted indexes from the major stock exchanges are employed (as opposedto price-weighted or other indexes) as well as the utilization of daily return data.Finally, the purpose of assessing whether the m arket of Tokyo or New Y ork hasthe more profound impact on these regional markets that are closely related to eachother from both a socio-economic and geographical perspective is unique to thisstudy.Evidence presented in this study suggests that the m arkets can be grouped intothree categories: those that move with the US but not with JP, those that move with

JP but not with the US, and those that move with neither. The implication of theseobservations is that an attempt to predict the stock index of one country based onthe stock index of an economically dominant country is expected to be fruitful insome, but not all cases. S imple generalizations as to market relatedness are inappro-priate. The evidence presented in this study reinforces this premise by means ofout-of-sample forecasting.The paper is organized as follows. Section 2 presents the data, the model, thediscussion of unit roots and the theory of cointegration. Section 3 examines theempirical evidence. Here it is shown that the stock indexes of Hong Kong, India,Korea, Taiwan, Malayasia, Singapore, Thailand, Indonesia, Philippines, the UnitedStates, and Japan are all integrated processes. The stock indexes of HK, I, K, andM are cointegrated with the US. The stock indexes of S , IN, and P are cointegratedwith JP. And the remaining countries, T and TH, are not cointegrated with either.An app ropriate error correction model is constructed and it is show n to be statisticallysignificant and potentially useful for forecasting the stock indexes of HK, I, K, IN,M, S, and P. Section 4 summarizes and concludes the paper.

2. Data and methodologyThe data used in this study consist of the daily closing values of the stockmarket index of the major exchange in each country. Specifically, the indexessampled include the Hang Seng (HK), India BSE National (I), Korea SE Composite(K), Taiwan SE Weighted (T), Kuala Lum pur Composite (M), Singapore All Share

(S), Bangkok S.E.T. (TH), Jakarta SE Composite (IN), Philippines SE C omposite(P), S& P 500 Com posite (US), and the N ikkei 225 Stock A verage (JP) all expressedin terms of local cu rrencies. The data are obtained from Datastream and cover theperiod March 26, 1997 through Decem ber 31, 1997; 201 observations are used forinvestigation. The natural logarithms of the indexes are used of which 141 areemployed for m odel estimation and the remaining 60 are applied for out-of-sampleforecasting.


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162 A. Ghosh, R. Saidi, K.H. Johnson/The Financial Review 34 (1999)159-170Economic theory is based o n equilibrium relationships among a set of variablesand statistical analyses applied to financial time series are employed to investigate

such relationships. Classical statistical inference will be valid if the tim e series arestationary but misspecification results if the series are not stationary. To achievestationarity, financial economists difference the variables and then use these d iffer-enced series in statistical analysis so that valid statistical inference will be achieved.The concept of cointegration, introduced by Granger (1981, 1986) and furtherdeveloped by Engle and Granger (1987), incorporates the presence of nonstationarity,long-term relationships and the short-run dynamics in the modeling process. Sincea lengthy, detailed description of cointegration can be found in m any textbooks (seeEngle and Granger, 1991; Davidson and MacKinnon, 1993; Banerjee, Dolado,Galbraith and Hendry, 1993; Hamilton, 1994), a brief overview is sufficient here.A financial time series is said to be integrated of order one i.e., I(l), if it becomesstationary after differencing once. If two series are integrated of order one, theymay have a linear combination which is stationary without requiring differencingand, if they do, they are said to be cointegrated.Let us consider two time series, x, and yl, which are both I(1). Usually, anylinear combination of x,and yt will be I(1). But if there exists a linear combinationz, = yt - Y - p x,which is I(O), then x, nd yt are cointegrated with the cointegratingparameter g. Thus, cointegration links the long-run relationship between integratedfinancial series to a statistical model of those series.In order to test whether the two market ind ices are cointegrated, it is necessaryto first determine that each index is I(1). Testing for unit roots is conducted byperforming the augmented Dickey-Fuller (ADF) (1981) regression, which can bewritten as:

where p is selected large enough to ensure that the residuals E, are white noise.However, the ADF test loses power for sufficiently large values of p. Because ofthis, an additional, alternative test proposed by Phillips and Perron (PP) (1987)which allows weak dependence and heterogeneity in disturbances is performed usingthe following regression:Yt = bo + b, Y t- I + ui (2)

where u, is serially correlated.Testing for cointegration is undertaken once it is found that each series containsone unit root. Test statistics utilize residuals from the following cointegrating re-gression:C, = a + b F,+ c t + el (3)

where C , and F, are the regressand and the corresponding regressor and t is a trend.


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A. Ghosh, R. Saidi, K.H. Johnson/The Financial Review 34 (1999) 159-170 163If the two series are cointegrated, then e, will be I(0).The AD F test is performedon the estimated residuals, e, , from Eequation (3):

9Ae, = ae,-I+ +lAe,, + v, (4)J= Iwhere q is large enough to make v, white noise. The estimated residuals are alsosubject to the following PP test:e, = cx + p e , _ l+ 7, ( 5 )

where 7, s serially correlated.Our interest is to uncover who moves the Asian-Pacific stock markets. Japanis considered the dom inant regional market force whereas the United S tates is thedominant world market. Tests will be performed with US as well as JP as theinfluential market forces. This will enrich our understanding with regard to long-run polarization of these markets. Once it is established that the series are cointe-grated, their dynamic structure can be exploited for further investigation. Engle andGranger (1987) show that co integration implies, and is implied by, the existence ofan error correction representation of the ind ices involved. An error correction model(ECM) abstracts the short- and long-run information in the m odeling process. TheECM to be estimated is given by:n mAct= a. + ae,-I+ 2 y, AF,-)+ S,AC,, + u, (6 )t = I J = Iwhere n and m are large enough to make u, white noise.Engle and G ranger propose a two-step estimation procedure for the estimationof the parameters of Model (6). First, OLS is employed to regress C, on F, and thetrend variable and the residuals are estimated using Equation (3). The ECM (6) isestimated by OLS in the second stage. The appropriate values of n and m a re chosenby the Akaike (1974) information criterion (AIC).The existence of an error correction model implies some Granger causalitybetween the series, which means that the error correction model can be used forforecasting. The error correction model is expected to provide better forecasts thanthat obtained from a nai've model. The forecasting performance of the e rror correctionmodel is compared to that of a benchmark nai've model by means of root meansquared error (RM SE).For forecasting, this nai've model uses the most recent information availableconcerning the actual value. The forecasting equation is:

F,+,= x,where:F, + ,= forecast for period t + i

t = present periodi = number of periods ahead being forecastx = latest actual value (for period t)

(7)


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164 A . Ghosh, R. Saidi, K.H. Johnson/The Financial Review 34 (1999) 159-1 70Table 1Tests for integrated financial time seriesThis table displays the augmented Dickey-Fuller and Phillips Perron tests for unit roots in theautoregressive representations of the daily stock indexes and on the first-differences of the index seriesof the 11 markets studied. Critical values for the sign ificanc e tests are from M acKinnon (1991). Afinancial series is considered integrated I ( 1 ) if it is stationary after differencing once.

Tests on Index Levels Tests on First DifferencesTest Statistic Test Statistic

Country Index ADF PP ADF PPHong KongIndiaKoreaTaiwanMalaysiaSingaporeThailandIndonesiaPhilippinesUnited StatesJapan

-0.92-1.24

1.64-1.480.97-1.04

0.620.14

-0.95-2.180.15

-0.79-1.30

0.61-1.46-0.64-1.52-0.51-0.18-1.30-1.47-0.41

-3.00*-4.24*-3.57*-2.98*-4.83*-3.67*-3.45*-3.28*-3.23*-3.99*-3.77*

- 535'-12.87*-11.97*-14.59*-12.01*-12.72*-1 1.34*-10.12*-11.74*-15.10*-16.07*

~

* Indicates statistical significance at the 0.10 level.

3. Empirical analysisAll the stock index series are tested to ensure they are I(1). The results of theADF and the PP tests are shown in the first part of Table 1 labeled Tests on IndexLevels. The stock index series tests demonstrate that each has a unit root in itsautoregressive epresentation.This indicates that each series is nonstationary necessi-tating the calculation of first differences and the difference series are then checkedfor the presence of a unit root. From the last two columns of Table 1, we see thatthe ADF and the PP tests clearly reject the null hypothesis of the presence of a unitroot for each series implying that the difference series are indeed I(0). Therefore,we conclude the stock indices are 1(1) for all indices.Since it is established that each series is I(1), the next step is to test whetherthere exists a linear combination of two corresponding indices that is I(0). If thisis found, the two series must be cointegrated. Results of the tests of cointegrationare presented in Table 2 . The ADF test and/or the PP test rejects the null hypothesisof no cointegration for all series except for Taiwan (T) and Thailand (TH) at the

0.10 level of significance. This observation reinforces the notion that cointegrationunites the long-run relationship between the relevant variables for some of thecountries.From the significance tests performed and displayed in Table 2 , we see thatthe market of Hong Kong appears to have a long-run equilibrium relationship with


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A. Ghosh, R. Saidi, K.H. Johnson/The Financial Review 34 (1999) 159-170 165Table 2Tests for cointegrationof the US market and the Japanese marketwith other A sian-Pacific marketsAugmented Dickey-Fuller (ADF) and Phillips Perron (PP) tests for unit roots based on the relationshipgiven by Equation (3) employing a developed market (U S or JP) as the regressor and individual less-developed markets as the regressand. The critical values for the tests are from MacKinnon (1991).

Regressor~~ ~ ~~us JP

Regressand ADF PP ADF PPHKIKTMSTHINP

-4.00 *-3.21 *-3.39 *-1.45-4.17 *-1.66-1.38-1.02-1.33

-4.71 *-1.19-3.33 *-1.41-4.28 *-2.11-1.24-0.95-1.83

-1.94-1.88-1.86-1.96-3.00-3.22 *-2.42-3.05 *-3.25 *

-2.29-1.90-2.86-1.91-2.97-3.22 *-2.08-3.30 *-2.74 *

* Indicates statistical significance at the 0.10 level.the US market but not with Japan. Likewise, the US market appears to have asimilarly unique relationship with the markets of India, Korea and Malaysia. How-ever, the m arkets of Singapore, Indonesia and the Philippines are singularly relatedto the Japanese stock market. Last, we observe that the markets of Taiwan andThailand are not cointegrated with either of the developed markets.Cointegration implies that the series have an error correction representationand, conversely, an ECM implies that the series are cointegrated (Engle andGranger). The ECM (6) provides a better representation of the stochastic dynamicrelationship between the series by enlarging the information set. For example,the last periods equilibrium error is incorporated through the error correctionterm. Short-run deviations in one period are adjusted through lagged variablesin the next period.Table 3 presents the estimates of the parameters of Model (6) for all series.The intercepts from Model (6) are found to be less than twice their standard errorsfor all series. This implies the absence of a linear trend in the data generatingprocess. The error correction term is negative (and always more that twice itsstandard error) which indicates a tendency towards mean reversion. This evidenceis consistent with expectations. If the change in the corresponding regressand (suchas AHK,) is above its average value, the error correction term is positive. In thiscase, AHK, will move downw ard to follow the long-run equilibrium attractor, makingthe coefficient negative. If AHK, is below its average position, the error correctionterm is negative. However, it will move upward to follow the long-run equilibriumattractor, and the coefficient will be negative. These coefficients measure the speed


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166 A . Ghosh, R. Saidi, K.H. JohnsodThe Financial Review 34 (1999)159-170Table 3Parameter estimates for the estimated error correction modelsEstimates of the parameters and standard errors of the error correction model, Equation (6), for the lessdeveloped markets. Significance is not explicitly stated since only variables with coefficient estimatestwice their standard errors are included.Regressand Regressor Coefficient SEAHK

A1

AK

AIN

AM

AS

AP

Constante,.,Constantel.,AIN,.,A1Nt.z

0.0011-0.0541-0.28180.44480.25700.00040.1738

-0.0618

0.0001-0.0357-0.001 1-0.07810.4299

-0.1795-0.001 1-0.21700.33380.1990

-0.0004-0,1488

0.2346-0.0027-0.07970.2241

0.00120.01930.07960.12350.12690.00100.02610.08190.00100.01330.00130.02460.07420.07520.00150.04310.07920.07940.00080.03630.07810.00160.02840.0825

with which the system moves toward its equilibrium relationship in the long run.The coefficients on the error correction term, being negative and statistically signifi-cant, imply they are restoring an equilibrium relationship in the long run.The short-run influences are judged by the lagged variables of AHK, AUS, AI,AIN, AM, and AS. We find some of these influences to be statistically significant.Of particular interest is that the less-developed markets of Indonesia, Malaysia,Singapore, and the Philippines all exhibited a significant positive one-period lag,indicating a tendency for market movem ent to be persistent in the short run. Inclusion


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A. Ghosh, R. Saidi, K.H. Johnson/The Financial Review 34 (1999) 159-170 167Table 4One-step-ahead forecast evaluationsForecast comparisons of naive forecasting and time-series forecastingby country based on mean error(ME), standard deviation (SD) and root mean square error (RMSE) of forecast errors utilizing 60 one-step-ahead forecasts. The variable being forecasted is the change in each countrys market index.

StatisticSeries Model ME SD RMSEAHK ECM(6) 0.0046 0.0139 0.0420AHK Naive(7) -0.0053 0.0435 0.0674A1A1

ECM(6) 0.0006 0.0037 0.0132Naive(7) -0.001 1 0.0133 0.0180

AK ECM(6) 0.0011 0.0040 0.0438AK Naive(7) -0.0085 0.0441 0.0576AIN ECM(6) -0.0017 0.0155 0.0287AIN Naive(7) -0.0041 0.0293 0.0362AM ECM(6) -0.0041 0.0189 0.0370AM Naive(7) -0.005 1 0.0375 0.0501ASAS

ECM(6) 0.0003 0.0065 0.0182Naive(7) -0.0013 0.0183 0.0257

AP ECM(6) -0.0026 0.0055 0.0200AP Naive(7) -0.0040 0.0206 0.0270

of the sho rt-run variables is essential to mitigate the possibility of misspecificationof the model. The ECM (6) incorporates both the short- and the long-run informationin modeling the data.

Estimation of an error correction model implies the existence of causalitybetween the respec tive changes in the stock indices which, in turn, mplies that thestock indices are predictable. The estimated error correction model for each m arketindex series is used to develop 60 one-step-ahead forecasts for each index. Theseforecasts are then compared with naYve univariate forecasts and these results aresummarized in Table 4.We see that the average forecast error and standard deviationof the ECM (6) are smaller than those from the naYve model. Likewise, the rootmean square error (RMSE) obtained employing the Error Correction Model (6) issmaller than that from the NaYve M odel (7) in all cases with the percent improvementin RMSE ranging from 21% to 38%.

To summ arize, the evidence presented indicates that the Error Correction Model(6) is better than the (nalve) Model (7) in predicting the change in indices. Out-of-sample forecasts reinforce the supremacy of Model (6). This evidence supports thepremise that the stock prices are predictable in Asian-Pacific countries.


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168 A. Ghosh, R. Saidi, K.H. Jo h n s o d Th e Financial Review 34 (1999) 159-1704. Summary and conclusions

This study investigates the recent debacle of the Asian-Pacific stock marketsand examines whether these markets are driven by the stock markets of US and/orJapan using the theory of cointegration. Daily stock index series are used in thisinvestigation.

Each series is tested for the presence of a unit root in its autoregressiverepresentation and it is found that each series is integrated of order one. Each indexis then tested for the existence of a long-run equilibrium relationship in a bivariateframework where it is found that the stock markets of Hong Kong, India, Korea,and Malayasia share a long-run equilibrium relationship with the stock market ofUS. The stock markets of Indonesia, Philippines, and Singapore are linked with thestock market of Japan. However, the stock markets of Taiwan and Thailand do notappear to be influenced by the stock markets of the US or Japan.A plausible explanation for the findings is that the presence of US multinationalcompanies is more pronounced in Hong Kong, India, Korea, and Malayasia than inIndonesia, Singapore, and the Philippines. The economic relationships and regulatorystructures of Indonesia, Singapore, and the Philippines are closely related to thoseof Japan, which may explain why these countries are more influenced by the Japanesemarket.

Cointegration implies and is implied by the existence of an error correctionmodel. As stated previously, the ECM integrates the short- and the long-run infonna-tion in modeling the data and proves to be a superior modeling technique comparedto the nake method. Evidence in this study indicates that the ECM can be utilizedto forecast stock market indices with potential benefit accruing to investors whoemploy this framework in designing their trading strategies.

The dominance of Model (6) over Model (7) is established by means of out-of-sample forecasts. ECM reduces the RMSE of the forecasted stock indices forthe seven developing markets investigated in this study by a considerable margin.Further investigation is needed to determine if this procedure is applicable to otheremerging markets in the world.

ReferencesAkaike, H., 1974. A new look at statistical model identification, IEEE Trans. Auto . Control 19, 716-723.Banerjee, A . , J. Dolado, J. Galbraith and D. Hendry, 1993. Co-Integration, Error Correction, and theCorhay, A, , A. Rad and J . Urbain, 1995. Long-run behavior of Pacific-Basin stock prices, AppliedDavidson R. and J. MacKinnon, 1993. Estimation and Inference i n Economefrics (Oxford UniversityDickey, D. and W. Fuller, 1981. The likelihood ratio statistics for autoregressive time series with a unitEng le, R. and C . Granger, 1987. Cointegration and error correction representation, estimation, and testing,

Econometric Analysis of Non-stationary Data. (Oxford U niversity Press, New York).Financial Economics 5, 11-18,Press, New York).root, Econometrica 49, 1057-1072.Econometrica 55 , 25 1-276.


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A . Ghosh, R. Saidi, K.H. Johnson/The Financial Review 34 (1999) 159-170 169Engle, R. and C. Granger, 1991. Long-run Economic Relationships Readings in Cointegration . (OxfordGranger,C . , 1981. Some properties of time series data and their use in econometric model specification,Granger, C., 1986. Developments in the study of cointegrated economic variables, Oxford Bulletin ofHamilton, J. , 1994. Time Series Analysis (Princeton: Princeton University Press).Hung, B and Y. Cheung, 1995. Interdependenceof Asian emerging equity markets, Journal of BusinessKasa,K., 1992. Common stochastic rends in international stock markets,Journal of Mon etary EconomicsKwan, A., A. Sim and J. Cotsomitis, 1995. The causal relationships between equity indices on worldMacKinnon, J., 1991. Critical values forcointegration ests, in Long-run Economic Relationships Readin gsPhillips, P. and P. Perron, 1988. Testing for a unit root in time series regression, Biometrika 75, 335-346.

University Press, New York).Journal of Econometrics 16, 121-130.Economics and Statistics 48, 213-228.

Finance & Accounting 22, 281-288.29, 95-124.exchanges,Applied Economics 27, 33-37.in Cointegration (Oxford University Press, New York).

who moves the asia-pacific stock

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