time-varying beta and the asian financial crisis: evidence from the asian industrial sectors

Japan and the World Economy 22 (2010) 228–234

Time-varying beta and the Asian financial crisis: Evidence from the Asianindustrial sectors§

Taufiq Choudhry *, Lin Lu, Ke Peng

School of Management, University of Southampton, Highfield, Southampton SO17 1BJ, UK

A R T I C L E I N F O

JEL classification:

G1

G12

G15

Keywords:

Time-varying beta

GARCH

BEKK model

Asian financial crisis

Volatility

A B S T R A C T

This paper empirically investigates the effects of the Asian financial crisis of 1997–98, and the period

immediately afterwards, on the time-varying beta of four industrial sectors (chemical, finance, retail and

industry) of Indonesia, Singapore, South Korea, and Taiwan. We apply daily data from 1992 to 2002 and

the bivariate MA-GARCH model (BEKK) to create the time-varying industrial betas. Results provide

evidence of the influence of the Asian financial crisis, and the period after, on the time-varying industrial

betas of these countries. These results may have implications for investors who are interested in portfolio

risk management.

� 2010 Elsevier B.V. All rights reserved.

Contents lists available at ScienceDirect

Japan and the World Economy

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1. Introduction

It is now considered an empirical fact that the beta of a riskyasset or portfolio is time varying and not constant (Fabozzi andFrancis, 1978). Movement in the time-varying beta maybe due toboth macroeconomic and/or microeconomic factors (Bos andNewbold, 1984). These factors could include operational changesin the company, or changes in the business environment peculiarto the company, the rate of inflation, general business conditionsand expectations about relevant future events.1

This paper investigates the effects of the Asian financial crisis of1997–98 on the time-varying beta of industrial sectors of fourcountries of the Far East. To our knowledge no other studyinvestigates the influence of the Asian crisis on the time-varyingbeta of Asian industrial sectors.2 During the Asian financial crisis of

§ We would like to thank an anonymous referee for several useful comments and

suggestions. We also thank the seminar participants at the All China Economics

International Conference, Hong Kong 2009 for several useful comments on an

earlier version of the paper. Any remaining errors and omissions are our

responsibility alone.

* Corresponding author. Tel.: +44 2380599286; fax: +44 2380593844.

E-mail address: [email protected] (T. Choudhry).1 See Rosenberg and Guy (1976a,b) for a detailed discussion of the factors.2 Maroney et al. (2004) explore risk and return relations in six Asian equity

markets affected by the 1997 Asian financial crisis. Their results show national

equity betas increased and average returns fell substantially after the start of the

crisis, and they think this is due to leverage increases. Choudhry (2005) provides a

study of the affect of the crisis on the beta of Malaysian and Taiwanese firms. Results

indicate a rise in the beta during the crisis, especially in the case of Malaysian firms.

0922-1425/$ – see front matter � 2010 Elsevier B.V. All rights reserved.

doi:10.1016/j.japwor.2010.06.003

1997–98, the exchange rates and the stock prices of some of the FarEast countries fell dramatically in value. There was also a dramaticdecrease in the capital inflow to the Far East countries from $93billion in 1996 to �$9.4 billion in 1998. The average daily changesin the stock markets were negative for most Asian stock marketsduring 1996–98, indeed during this period 10 percent dailychanges in the Asian stock markets became common (Kaminskyand Schmukler, 1999). The crisis slowed worldwide economicgrowth; increased risk premiums in debt markets; stock marketsbecame more volatile; and confidence indicators fell in manycountries (Choudhry, 2005). Given the current on-going globalfinancial crisis, it is of empirical interest to investigate the potentialeffect of a past financial crisis on the time-varying beta of riskyassets. We know that the Asian crisis of 1997–98 had a drasticnegative effect on the economy and the financial markets of the FarEast region, but less is known of the effects of this crisis on the betaof Asian industrial sectors. Studies have investigated the effect ofthe Asian crisis at the general market and firm level but not yet atthe industrial level despite the fact that portfolio managers dooften take into consideration industrial level stock in financialportfolios. Therefore, the focus of this paper is to investigate theeffects of a financial crisis at the industrial level.

As stated by Chen and So (2002) during the Asian financial crisisthe observed volatility of the financial markets around the world alsoincreased.3 The rise in the volatility during the crisis of 1997–98could be due to two factors: firstly, inflammatory statements by

3 Market volatility peaked in the months of October 1997 through January 1998.

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T. Choudhry et al. / Japan and the World Economy 22 (2010) 228–234 229

government officials; and secondly, by the introduction (orelimination) of restrictions on financial market transactions(Kaminsky and Schmukler, 1999). The higher implied volatilityduring the crisis period indicates that investor uncertainty aboutfuture stock market returns had increased: a jump in the stockmarket volatility may be perceived by the investors as an increase inthe risk of equity investment. According to Chen and So (2002) whenstock returns are more volatile, all other things being equal, marketrisk (beta) is expected to be larger, with greater stock price responsesfor firms that have greater exposure to the source of the risk. If so,investors may shift their funds to less risky assets, such as bonds. Thisreaction would tend to raise the cost of funds to firms issuing stock.

2. The (conditional) CAPM and the time-varying beta

One of the assumptions of the capital asset pricing model(CAPM) is that all investors have the same subjective expectationson the means, variances and covariances of returns.4 According toBollerslev (1988), economic agents may have common expecta-tions on the moments of future returns but these are conditionalexpectations and therefore random variables rather than con-stant.5 The CAPM that takes these conditional expectations intoconsideration is sometimes known as conditional CAPM. Theconditional CAPM provides a convenient way to incorporate thetime-varying conditional variances and covariances (Bodurtha andMark, 1991).6 An asset’s beta in the conditional CAPM can beexpressed as the ratio of the conditional covariance between theforecast error in the asset’s return, the forecast’s error of themarket return and the conditional variance of the forecast error ofthe market return.

The following analysis relies heavily on Bodurtha and Mark(1991). Let Ri,t be the nominal return on asset i (i = 1, 2, . . ., n) andRm,t the nominal return on the market portfolio m. The excess (real)return of asset i and market portfolio m over the risk-free assetreturn is presented by ri,t and rm,t, respectively. The conditionalCAPM in excess returns may be given as

ðri;tjIt�1Þ ¼ biIt�1Eðrm;tjIt�1Þ (1)

where

biIt�1¼ covðRi;t;Rm;tjIt�1Þ

varðRm;tjIt�1Þ¼ covðri;t ; rm;tjIt�1Þ

varðrm;tjIt�1Þ(2)

and E(jIt�1) is the mathematical expectation conditional on theinformation set available to the economic agents in the previousperiod (t � 1), It�1. Expectations are rational based on Muth’s (1961)definition of rational expectation where the mathematical expectedvalues are interpreted as the agent’s subjective expectations.According to Bodurtha and Mark (1991) asset i’s risk premiumvaries over time due to three time-varying factors: the market’sconditional variance, the conditional covariance between the asset’sreturn and the market’s return, and/or the market’s risk premium. Ifthe covariance between asset i and the market portfolio m is notconstant then the equilibrium returns Ri,t will not be constant.7

4 See Markowitz (1952), Sharpe (1964) and Lintner (1965) for details of the

CAPM.5 According to Klemkosky and Martin (1975) betas will be time-varying if excess

returns are characterized by conditional heteroscedasticity.6 Hansen and Richard (1987) have shown that omission of conditioning

information, as is done in tests of constant beta versions of the CAPM, can lead

to erroneous conclusions regarding the conditional mean variance efficiency of a

portfolio.7 In this paper we apply the domestic CAPM and not the international CAPM. The

international CAPM takes into account both the systematic risk and the exchange

rate risk. The only way to use the domestic CAPM in the international context is by

making two assumptions, (i) PPP holds at any point in time and (ii) consumption

baskets are the same for all investors.

3. The data, the bivariate MA-GARCH model and testing theeffects of the crisis

3.1. Data

In this paper, we employ daily industrial stock indices fromIndonesia, Singapore, South Korea, and Taiwan. The crisisaffected Indonesia and South Korea more heavily than Singaporeand Taiwan (Neiss, 2009).8 The range of the data is from 1January 1992 to 30 December 2002.9 For the empirical analysisthe total period is further broken into three smaller sub-periods:pre-crisis period (January 1992 to June 1997), crisis period (July1997 to June 1998), and post-crisis period (July 1998 toDecember 2002). The total period January 1992 to December2002 is also applied in the empirical tests. Four industrialsectors10 (chemical, finance, retail and industry) are included foreach country.11 The chemical sector includes companies inspecialty chemicals (e.g. adhesives, coatings, additives), pro-ducts derived from life sciences (e.g. pharmaceuticals), agricul-tural chemicals (e.g. pesticides, fertilizers) and consumer careproducts (e.g. detergent, bleach, cosmetics). The industry sectorindex is composed of aerospace industries, building supplies,industrial-building products, business equipment, chemicals,machinery (both light and industrial), metals fabrication, paperand packaging, and photo equipment. The definition of thefinancial sector used here includes banks, insurance, invest-ments, real-estate investment, real estate, and savings andloans. Finally, the retail sector includes apparel, departmentstores, food stores, and miscellaneous shops.

The market index is represented by the general stock marketindex of each country: Indonesia, Jakarta SE composite index;South Korea, Korea SE composite index; Singapore, Straits Timesindustrial index; Taiwan, Taiwan SE weighted index. The returnon the risk-free asset applied is the 3-month deposit returnfor each country.12 Stock returns are created by taking thefirst difference of the log of the stock index, and the excessstock return is calculated as the nominal industrial stock returnminus the return on the risk-free asset. We also include aglobal factor in the empirical tests, to test the effect of theinternational volatility. The global factor is represented by thereturn in the MSCI world index. The massive capital flow, orsubsequent cease, across the borders had a major impact on theboom, bust of the Asian stock markets and the real economicdeterioration in the region. By including the global factor in ourtests we aim to capture the potential effects of the financialmarkets outside the Far East region on the industrial time-varying betas under study. All data are obtained from Data-stream. The basic statistics for all excess returns (including themarket return) are found to be skewed and have excess kurtosisand thus are found to be non-normal by means of the J–B tests.These results are not presented to save space but are availableon request.

3.2. Bivariate GARCH (BEKK) model

The following bivariate GARCH(p,q) model may be used torepresent the returns from asset i and the market portfolio (m).

8 We select countries with the different level of the crisis effect are for

comparison purposes.9 We kept the data to 11 years so that pre- and post-crisis periods would be

approximately the same length.10 We chose industries based on the availability of the data. We need the same

length data for the same industries from all four countries.11 An industrial sector index is termed sector-based. These indices represent

companies from one industrial sector of the economy.12 Once again availability of the data determined the risk-free asset return.

13 The conditional volatilities of the sectors, local market and global factor are

obtained from the BEKK model.14 The regression is estimated by using least square but then a consistent estimate

of the covariance matrix allowing for heteroscedasticity is computed.

T. Choudhry et al. / Japan and the World Economy 22 (2010) 228–234230

This presentation is termed by Engle and Kroner (1995) theBEKK model; the conditional covariance matrix is parametrizedas

yt ¼ mþ et � uet�1 (3)

et

Vt�1�Nð0;HtÞ (4)

vechðHtÞ ¼ C0C þXK

K¼1

Xq

i¼1

A0Kiet�ie0t�iAki þXK

K¼1

Xp

i¼1

B0K jHt� jBk j (5)

where yt = (rit, rm,t) is a (2 � 1) vector containing returns fromasset i and the market portfolio (m), m is a 2 � 1 vector ofconstant, Aki, i = 1, . . ., q, k = 1,. . . K, and Bkj j = 1, . . ., p, k = 1, . . ., K

are all N � N matrices. This formulation has the advantage overthe general specification of the multivariate GARCH in thatconditional variance (Ht) is guaranteed to be positive for all t

(Bollerslev et al., 1994). The BEKK model is sufficiently generalto include all positive definite diagonal representations, andnearly all positive definite vector representations. The movingaverage (MA) term uet�1 is included to capture the effect of non-synchronous trading. According to Susmel and Engle (1994),non-synchronous trading induces negative serial correlation,and the MA term allows for autocorrelation induced bydiscontinuous trading in the asset. The following presents theBEKK bivariate GARCH(1,1), with K = 1.

Ht ¼ C0C þ A0et�1e0t�1Aþ B0Ht�1B

Ht ¼ C0C þA11 A12

A21 A22

� �0 e21;t�1 e1;t�1e2:t�1

e2;t�1e1;t�1 e22;t�1

" #A11 A12

A21 A22

� �

þB11 B12

B21 B22

� �0Ht�1

B11 B12

B21 B22

� �(5a)

where C is a 2 � 2 lower triangular matrix with interceptparameters, A and B are 2 � 2 square matrices of parameters.The BEKK parameterization requires estimation of only 11parameters in the conditional variance–covariance structure,and guarantees Ht positive definite. Importantly, the BEKK modelimplies that only the magnitude of past returns innovations isimportant in determining current conditional variances andcovariances. The parameters in A reveal the extent to which theconditional variances of the two variables are correlated with pastsquared errors. The off-diagonal elements represent how the pastsquared error of one variable affects the conditional variance ofanother variable. The parameters in B depict the extent to whichthe current levels of conditional variances are correlated with pastconditional variances. The off-diagonal elements indicate theextent to which the conditional variance of one variable iscorrelated with the lagged conditional variance of anothervariable. High values of off-diagonal elements support a correla-tion between volatility of two variables.

The time-varying beta (b) for asset i is calculated as

bi;t ¼H12;t

H22;t

(6)

where H12;t is the estimated conditional covariance between thespecific asset returns and market portfolio returns, and H22;t is theestimated conditional variance of the market portfolio returnsfrom the bivariate BEKK model. Given that conditional covarianceand conditional variance are time-dependent, the stock beta willalso be time-dependent. We apply the time-varying beta defined inEq. (6).

3.3. Testing the effects of the Asian financial crisis

The following OLS regression is applied to investigate the effectof the crisis and the period after the crisis on the time-varying beta:

bit ¼ a0 þ a1CVit�1 þ a2MVt�1 þ a3GVit�1 þ et (7)

where bit is the individual industrial time-varying beta as definedin Eq. (6), CVit is the conditional volatility of the individualindustrial sector, MVt is the market conditional volatility, GVt is theglobal factor conditional volatility, and et is the random error termwith the standard assumptions.13 The parameters a1, a2, and a3,measure the effects of the conditional volatility of the individualindustrial sectors, the local market, and the global factor on thebeta of the four industrial sectors of each country, respectively. Ifthe sign on these parameters is positive (and significant) then a risein the volatility of the industry, local market, and/or the globalfactor should increase the beta of the firm. If investors perceive arise in the volatility of the industrial sector and/or the local stockmarket as an increase in the risk of equity investment, then theconditional volatility of the individual industrial market (CV) and/or the conditional volatility of the local market (MV) should imposea direct effect on the beta of the industry. Similarly, an increase inthe global factor volatility seen as an increase in risk (GV) shouldhave a direct relationship with the beta. Application of the threesub-periods of pre-crisis, crisis and post-crisis makes it possible toinvestigate and compare the changes in the effects of the threevolatilities on the beta.

4. Empirical results

The bivariate BEKK GARCH results are quite standard. Giventhis, and the lack of space, they are not presented in this paper butare available on request. In summary, the ARCH coefficients are allpositive and significant implying volatility clustering in both theindustrial return and the market return. All ARCH coefficients areless than unity in size. Some evidence of a significant MA (u)coefficient is found for both the industrial sectors and the market.The Ljung-Box Q statistics on the standardized (normalized)residuals (et/H

1=2t ) and standardized squared residuals (et/H2

t ) areapplied to specify adequacy of the first two conditional moments.All series are found to be free of serial correlation (at the 5% level).The absence of serial correlation in the standardized squaredresiduals implies the lack of need to encompass a higher orderARCH process (Giannopoulos, 1995). The normality test also fails toindicate non-normal standardized and/or standardized squaredresiduals. These results are available on request.

Figs. 1 and 2 present the time-varying betas of the fourindustrial sectors of Korea and Indonesia, respectively. Thesefigures clearly show that during and after the 1997–98 crisis thebeta of these industries was higher and more intense than duringthe pre-crisis period. Taiwan and Singapore figures are notpresented to save space but are available on request. They alsopresented similar conclusions but on a smaller scale (as expected).

Tables 1–4 present estimations of Eq. (7) for all industrialsectors of each country for the four periods. Eq. (7) is estimatedsixty four times by means of OLS regression. By means of theCochran–Orcutt method, all regressions are corrected for serialcorrelation. The results are also adjusted for heteroscedasticity.14

Table 1 shows the results from Indonesia. The constant term issignificant in all tests. For Indonesia, there is little evidence ofconditional volatilities affecting the beta. In a few of the tests the

[(Fig._1)TD$FIG]

Fig. 1. Korean time-varying betas.[(Fig._2)TD$FIG]

Fig. 2. Indonesian time-varying betas.

15 To check for multicollinearity the variance inflation factor (VIF) for multi-

collinearity is applied. The VIF is equal to 1/(1 � R2). A value of 5 or more indicates

potential multicollinearity. For tests involving Indonesia the highest VIF is 1.41.


coefficients are negative but less than unity in absolute value. Thus,the magnitude of the effect is small. The industrial sector seems tobe the most influenced and the retail sector is the least affected.Conditional volatility of the industries is the most influential. Themarket and global conditional volatilities had little effect. In thehandful of significant cases, the size of the coefficient (in absolutevalue) is less than unity. The Indonesian results are somewhat of asurprise given the magnitude of the crisis effect. The R2 range from

0.185 to 0.292 and the Durbin–Watson statistics are quitesatisfactory.15 The lack of any significant effect from the crisiscould be because Indonesia during the crisis and earlier periodswas also experiencing major political crisis, with heightened

Table 1Indonesia.

Time Constant CV MV GV Rho R2 DW

Chemical

1992–97 0.8821*** (190.78) 0.2865* (1.9237) 0.0186 (0.0839) 0.3574 (0.8030) 0.4672*** (19.9322) 0.222 1.98

1997–98 0.9984*** (13.4350) �0.5181 (�1.0490) 0.6907 (0.9408) �4.4058 (�1.1516) 0.5444*** (10.2740) 0.293 1.89

1998–2002 0.9435*** (62.957) 0.7777*** (4.0068) �0.8681*** (�2.7174) 0.9698 (1.4671) 0.512*** (20.487) 0.277 1.95

1992–2002 0.9185*** (95.143) 0.2035 (1.5543) �0.1820 (�0.8900) 0.3254 (0.6251) 0.535*** (33.927) 0.280 1.93

Finance

1992–97 1.0620*** (145.290) �0.069 (�0.300) 0.5076 (1.4700) 1.1193* (1.8380) 0.531*** (23.661) 0.284 1.95

1997–98 1.1455*** (12.945) �0.3091 (�0.5383) �0.0068 (�0.0069) �5.0500 (�0.9668) 0.483*** (8.812) 0.229 2.01

1998–2002 1.0094*** (58.186) �0.5480* (�1.8618) 0.5765 (1.3000) �0.4526 (�0.5086) 0.4426*** (16.409) 0.192 1.94

1992–2002 1.0480*** (91.548) �0.4284** (�2.4216) 0.3076 (1.0814) �0.5311 (�0.7528) 0.4571*** (10.5641) 0.230 1.98

Retail

1992–97 1.0507*** (165.848) 0.0150 (0.1040) 0.1493 (0.5522) 0.1966 (0.3569) 0.512*** (22.465) 0.261 1.97

1997–98 1.1962*** (12.008) �0.1199 (�0.2480) �0.5368 (�0.6200) �2.5800 (�0.5119) 0.5489*** (10.4500) 0.292 1.99

1998–2002 1.1240*** (72.295) �0.2664 (�1.2078) 0.2706 (0.7390) 1.7067*** (2.2376) 0.4640*** (17.967) 0.215 1.89

1992–2002 1.0937*** (93.827) �0.1901 (�1.3791) �0.1382 (�0.5755) 0.9259 (1.4357) 0.5251*** (32.9990) 0.277 1.95

Industrial

1992–97 0.9727*** (233.769) �0.3484*** (�2.2778) 0.3052 (1.4016) 0.2076 (0.5070) 0.4570*** (19.355) 0.207 1.96

1997–98 1.0885*** (17.3389) 0.0561 (0.1029) �1.4622* (�1.8541) �3.9961 (�0.9270) 0.4115*** (7.1858) 0.185 2.07

1998–2002 1.0418*** (87.3351) �0.4802*** (�2.1450) 1.4697*** (4.6810) �0.9017 (�1.4434) 0.4322*** (16.289) 0.188 2.00

1992–2002 1.0117*** (125.200) �0.2414 (�1.6036) 0.0054 (0.0252) �1.0226* (�1.8877) 0.4385*** (26.0171) 0.194 2.05

CV = conditional variance of the industrial sector. MV = conditional variance of the market. GV = conditional variance of the global market.* Significant at the 10%.** Significant at the 5%.*** Significant at the 1%.

Table 2Korea.


Chemical

1992–97 1.0082*** (346.8191) 0.6253*** (4.1309) �0.5580*** (�3.1417) �0.3052 (�0.8561) 0.3400*** (13.6564) 0.121 2.02

1997–98 1.1407*** (33.8682) 1.2321** (2.3266) �1.9416** (�2.4232) 0.8634 (0.4763) 0.5249*** (9.7651) 0.277 2.15

1998–2002 1.0221*** (160.160) �0.5017*** (�2.6186) 0.1984 (0.8588) �0.2037 (�0.5500) 0.3826*** (14.121) 0.146 2.02

1992–2002 1.0260*** (225.8040) 0.1288 (1.0215) �0.3534** (�2.2096) �0.1432 (�0.4984) 0.4677*** (28.294) 0.233 2.09

Finance

1992–97 1.0626*** (335.1655) 0.2330 (1.4012) �0.2073 (�0.9900) �0.0679 (�0.1621) 0.2924*** (11.532) 0.084 1.98

1997–98 1.2254*** (33.2089) 2.6307*** (5.0555) �3.5743*** (�4.3960) 1.8364 (0.9529) 0.5391*** (10.1773) 0.339 2.25

1998–2002 1.1041*** (161.332) 0.7475*** (3.4014) �0.6108** (�1.9799) 0.3267 (0.6932) 0.2818*** (10.0301) 0.089 2.03

1992–2002 1.0947*** (219.833) 0.9819*** (7.0725) �1.1141*** (�5.7429) 0.2762 (0.8051) 0.4266*** (25.2405) 0.204 2.11

Retail

1992–97 0.8886*** (211.684) 0.5523*** (3.1521) �1.0506*** (�4.9634) 0.1080 (0.2060) 0.3276*** (13.0867) 0.117 1.98

1997–98 1.0761*** (26.4130) 2.1450*** (4.1630) �3.2006*** (�3.8714) 0.8442 (0.4088) 0.5487*** (10.4227) 0.309 2.14

1998–2002 0.8978*** (93.7108) �0.2249 (�0.9785) 0.2399 (0.7367) 0.1906 (0.2915) 0.2869*** (10.2116) 0.079 2.03

1992–2002 0.9091*** (151.136) 0.2450* (1.7225) �0.5384* (�2.7040) 0.3080 (0.6994) 0.3950*** (22.9890) 0.163 2.08

Industrial

1992–97 1.0943*** (426.223) 0.3367** (2.1990) �0.3360* (�1.8075) �0.3286 (�0.9803) 0.3015*** (11.9337) 0.091 1.97

1997–98 1.2269*** (39.0835) 2.5084*** (4.9394) �3.2687*** (�4.1716) �0.2863 (�0.17166) 0.5296*** (9.9224) 0.317 2.18

1998–2002 1.0968*** (264.697) 0.6726*** (3.5739) �0.8588*** (�4.0943) 0.0716 (0.2969) 0.3809*** (14.0490) 0.152 2.06

1992–2002 1.1078*** (281.6773) 1.1129*** (8.9250) �1.2988*** (�8.3218) �0.1152 (�0.4940) 0.4963*** (30.5728) 0.281 2.13


16 The largest VIF is 1.51 and thus no indication of multicollinearity in these tests.


uncertainty and growing public unrest. As reported in Neiss (2009),there was also a severe drought causing a serious rice shortage, andthere was a fall in oil prices. Tests applied in this paper do not takeinto consideration the effects of factors other than the financialcrisis. Further, the crisis has not affected Indonesia in ahomogeneous fashion across the country (Neiss, 2009). The crisisaffected more heavily those industries producing many non-tradable goods, where domestic demand is critical, imported rawmaterials are important, and credit or external financing isrequired.

The Korean results (Table 2) indicate a high level of volatilityinfluence, especially conditional volatilities of the industries andthe local market. Almost all sectors are affected during all periods

considered. During the crisis (1997–98), the conditional volatilityof the industry imposes a positive effect on the beta in all foursectors. Some of the significant coefficients are larger than unity.During this time the market volatility also imposes large sizeeffects but of an inverse nature. During the other periods, the effectof the industry volatility is mostly positive and smaller. The marketvolatility effect is negative and in absolute value smaller. Theglobal volatility imposes no effect at all during any period. Theconstant is again significant in all tests. The R2 ranges from 0.079 to0.339. The largest R2 is for the crisis period tests. Once again, theDurbin–Watson statistics are also quite satisfactory.16

Table 3Singapore.


Chemical

1992–97 1.0115*** (791.851) 0.0027 (0.0590) 0.0060 (0.1329) �0.0238 (�0.2256) 0.5326*** (23.7522) 0.285 1.76

1997–98 1.0274*** (147.9722) 0.5657*** (5.8500) �0.5397*** (�5.3441) �0.4789 (�1.1110) 0.4623*** (8.2049) 0.247 1.90

1998–2002 1.0004*** (877.079) 0.1666*** (5.6142) �0.1296*** (�4.0712) �0.0322 (�0.3916) 0.2497*** (8.8065) 0.094 2.02

1992–2002 1.0100*** (999.313) 0.2183*** (9.1068) �0.1999*** (�8.1696) �0.0700 (�0.9966) 0.4199*** (24.7336) 0.377 1.97

Finance

1992–97 1.0007*** (1644.618) �0.0220 (�0.4546) 0.0274 (0.5668) �0.0343 (�0.5857) 0.4733*** (20.282) 0.226 1.85

1997–98 1.0176*** (283.553) 0.0602 (0.6919) �0.0601 (�0.6775) 0.1175 (0.6737) 0.5691*** (11.0258) 0.311 2.17

1998–2002 1.0043*** (1317.379) �0.8941*** (�28.9218) 0.8989*** (27.4469) 0.2192*** (3.9196) 0.2359*** (8.0713) 0.372 1.97

1992–2002 1.0066*** (1928.485) �0.7147*** (�30.1995) 0.7191*** (29.9900) 0.2038*** (4.9012) 0.3464*** (19.5716) 0.584 2.00

Retail

1992–97 0.9554*** (1813.1712) �0.0416* (�1.7304) 0.0353 (1.4673) �0.0372 (�0.6409) 0.3995*** (16.4433) 0.166 1.92

1997–98 0.9539*** (368.261) �0.0715 (�0.8757) 0.0450 (0.5438) 0.1401 (0.6338) 0.2835*** (4.6885) 0.088 2.03

1998–2002 0.9571*** (881.353) �0.9360*** (�37.0288) 0.9754*** (33.2624) �0.1994** (�2.2322) 0.1458*** (4.8652) 0.510 1.95

1992–2002 0.9558*** (1675.113) �0.7810*** (�42.7171) 0.7750*** (41.0700) �0.0790 (�1.3368) 0.1639*** (8.7143) 0.512 1.97

Industrial

1992–97 0.9930*** (1488.227) 0.0237 (0.6513) �0.0191 (�0.5240) 0.0045 (0.0840) 0.5473*** (24.6782) 0.301 1.75

1997–98 1.0022*** (296.602) 0.0220 (0.2863) �0.0207 (�0.2629) 0.0277 (0.1916) 0.6157*** (12.4800) 0.370 2.19

1998–2002 0.9895*** (1800.634) �0.5641*** (�19.0471) 0.5786*** (19.0344) 0.0028 (0.0683) 0.2343*** (8.1447) 0.230 1.98

1992–2002 0.9923*** (2006.972) �0.3692*** (�16.8557) 0.3762*** (16.9829) 0.0053 (0.1632) 0.4494*** (26.8563) 0.658 2.00


Table 4Taiwan.

Time Constant CV FV GV Rho R2 DW

Chemical

1992–97 0.9888*** (437.618) 0.3188** (2.1354) �0.1577 (�1.0862) 0.1770 (0.5264) 0.2082*** (8.0345) 0.047 2.03

1997–98 0.9945*** (173.804) 0.0965 (0.2879) �0.4292 (�1.1630) 0.6675 (1.1339) 0.1439** (2.3076) 0.028 1.97

1998–2002 0.9906*** (334.207) 0.5329*** (3.7680) �0.3338* (�1.9369) �0.2304 (�1.0877) 0.2518*** (8.9024) 0.070 1.98

1992–2002 0.9902*** (571.2691) 0.4013*** (4.2884) �0.2819*** (�2.6880) �0.0488 (�0.2923) 0.2214*** (12.1457) 0.109 2.00

Finance

1992–97 1.0290*** (380.259) �0.0109 (�0.0738) 0.3349** (2.0164) 0.0734 (0.1983) 0.2687*** (10.512) 0.082 2.06

1997–98 1.0228*** (165.653) 2.1519*** (5.7566) �2.3773*** (�5.8630) 0.0072 (0.0124) 0.2179*** (3.5639) 0.151 2.04

1998–2002 1.0101*** (396.085) 0.6075*** (4.0504) �0.2956* (�1.7470) �0.2284 (�1.1922) 0.2152*** (7.5331) 0.067 2.02

1992–2002 1.0206*** (561.1229) 0.4375*** (4.26061) �0.1954* (�1.7000) �0.1315 (�0.7774) 0.2467*** (13.6027) 0.130 2.05

Retail

1992–97 0.8629*** (296.528) �0.0161 (�0.1089) 0.0869 (0.5481) �0.8271** (�2.0228) 0.2503*** (9.7223) 0.064 2.042

1997–98 0.8360*** (107.336) 0.2639 (0.6814) �0.3102 (�0.8774) 0.3246 (0.5133) 0.3177*** (5.3663) 0.091 2.04

1998–2002 0.8292*** (237.509) 0.4341*** (2.6755) �0.6390*** (�3.8932) 0.4580* (1.8535) 0.2588*** (9.1084) 0.083 2.04

1992–2002 0.8466*** (373.406) 0.2354** (2.2700) �0.2608** (�2.4246) 0.1900 (0.9535) 0.2892*** (16.1410) 0.113 2.05

Industrial

1992–97 0.9972*** (507.690) �0.4882*** (�3.6502) 0.6968*** (5.1400) �0.0410 (�0.1527) 0.2684*** (10.5073) 0.085 2.04

1997–98 1.0014*** (223.881) 0.8357** (2.5010) �1.0860*** (�3.2775) 0.5726 (1.5976) 0.3289*** (5.5209) 0.130 2.08

1998–2002 1.0101*** (566.693) 0.1720 (1.2059) �0.2313 (�1.4589) 0.0017 (0.0129) 0.2300*** (8.0672) 0.051 2.01

1992–2002 1.0029*** (769.561) �0.1680* (�1.7828) 0.2540*** (2.5903) 0.0088 (0.0749) 0.2622*** (14.5415) 0.172 2.04


17 The highest VIF is 2.9.18 The highest VIF is 1.2.


Singapore provides somewhat different results (Table 3).Results show that the chemical sector is the most affected andthe remaining three sectors less so. Conditional volatility of mostindustries provides a negative effect, with the exception of for thechemical sector. No significant effect is found during the crisisperiod except for the chemical sector. The size of the significantcoefficients is smaller than unity (in absolute value). Conditionalvolatility of the local market provides the opposite result. This timethe significant effects are positive except for, again, the chemicalsector. The size of the effects is smaller than unity. The global factorshows minimal significant effect. Notably the financial sectorsimposed a positive effect during the post-crisis and over the totalperiod. The size of the coefficients is less than unity. Like theprevious two countries, the constant is significant in all tests. The

range of R2 is from 0.088 to 0.658 with the total period showing thelargest ones for all sectors. The Durbin–Watson statistics aresatisfactory.17

The Taiwanese results in Table 4 shows that the financial sectorwas affected the most and the retail sector the least. The significanteffect of the conditional volatility of the industry is mostly positiveand less than unity in size. The significant market volatilitycoefficients are mostly negative and smaller than unity in absolutevalue. Once again, the global effect is very little and all constantterms are significant. The range of the R2 is from 0.028 to 0.172. TheDurbin–Watson is satisfactory.18


We then conduct further tests to check for errors in thevariables.19 The Hausman (1978) test and the Hansen (1982) testare employed to check for error-in-variables. The Hausman (1978)test is based on looking for a statistically significant differencebetween an efficient estimator under the null hypothesis of nomisspecification, and a consistent estimator under the alternativehypothesis that misspecification is present. The Hansen (1982) testinvestigates whether or not the assumptions can be held in asample test of the over-identifying restriction. Both the Hausmantest and the Hansen test fail to show any error in the variables.Thus, results indicate very few cases of specification problems.These results are available on request.

A comparison of results across the countries and industriesindicates that Korea is the most affected and Indonesia the least.20

The global factor is an important factor to some extent for Indonesiaand Singapore. During the crisis period (1997–98), all four Koreanindustries were affected by volatilities but in Indonesia andSingapore only one industry was affected. During the post-crisisperiod (1998–2002) all four countries were heavily affected.21

Excluding the Indonesian results, at least one of the volatilitiesinfluenced all industries of the remaining countries during allperiods under consideration. For Indonesia, all industries wereaffected by one of the volatilities during the post-crisis period only.Our results show that along with betas of the firms in the Far East, theindustrial sectors’ betas were also influenced during the Asian crisisof 1997–98. This result has implications for portfolio managementinvolving industrial sector shares during financial crises.

5. Conclusion and implications

This paper investigates whether the 1997 Asian financial crisisand the post-crisis period had any effect on the time-varying betaof the four selected industrial sectors (chemical, finance, industryand retail) of Indonesia, Singapore, South Korea, and Taiwan. Weapply daily data ranging from 1 January 1992 to 30 December2002, and create daily time-varying betas by means of the bivariateBEKK GARCH model. One main impact of the 1997–98 Asian crisiswas an increase in the volatility of financial markets and capitalflows around the world. Investors may perceive a rise in the stockmarket volatility as an increase in the risk of equity investment.

We employ standard linear regression to investigate the effects ofthe financial crisis on the time-varying beta. Change in the beta isinvestigated by using the conditional volatility of the industry, thelocal market and the global factor as explanatory variables in theregression. The conditional volatilities are estimated by means of theBEKK bivariate GARCH model. We then further divide the total periodintothree smaller sub-periods,pre-crisis (1992–97), crisis (1997–98)and post-crisis (1998–2002). We conduct estimations for all threeperiods to investigate and compare the potential changes in theeffects of volatility on the industrial beta from the pre- to the post-crisis period. Results show that during the crisis period (1997–98)there was a substantial increase in the positive effect of conditionalvolatility of the individual industry on the time-varying betas. Thereis also some evidence ofan increase in the effect during the post-crisisperiod. Local market volatility mostly seems to impose a negativeinfluence on the beta. Very little significant effect is found for theglobal factor. Of the four countries, Indonesia is the least affected.

19 If the explanatory variables are correlated with the error term and the least

square estimate is biased and inconsistent, then the explanatory variables are

measured with error.20 The Korean result is understandable given it was one of the most effected

countries but as explained earlier the Indonesian result is surprising.21 Given the small size of the crisis, effect on Singapore the weak result is

understandable.

Similar to the results presented in this paper, Choudhry (2005)and Maroney et al. (2004) also show increases in the beta of Asiancompanies and industrial sectors during the Asian crisis period.The increase in the market risk (beta) due to higher stock returnvolatility has implications for the decisions of investors in relationto portfolio risk management, as well as for the firms in financialoperations (Chen and So, 2002). If market risk increases theninvestors will demand higher returns, or, alternatively, investorswill form new investment portfolios to achieve their expectedutility of wealth.

The results presented also advocate further research in the area.Further research can be conducted using data from other Asian ornon-Asian markets, data from individual firms, investigation ofother financial crises, and perhaps also by means of a differentmethod of estimation, etc. The current financial crisis doesadvocate a similar empirical investigation.

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