Macroeconomic Variables and ICM in Malaysia

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Macroeconomic variables and

the Malaysian equity marketA view through rolling subsamplesMansor H. Ibrahim and Hassanuddeen Aziz

Kulliyyah of Economics and Management Sciences, International IslamicUniversity Malaysia, Kuala Lumpur, Malaysia

Keywords Stock prices, Macroeconomics, Variable costs, Malaysia

Abstract Analyzes dynamic linkages between stock prices and four macroeconomic variables for the case of Malaysia using standard and well-accepted methods of cointegration and vector autoregression. Empirical results suggest the presence of a long-run relationship between these

variables and the stock prices and substantial short-run interactions among them. In particular,documents positive short-run and long-run relationships between the stock prices and twomacroeconomic variables. The exchange rate, however, is negatively associated with the stock

prices. For the money supply, documents immediate positive liquidity effects and negative long-runeffects of money supply expansion on the stock prices. Also notes the predictive role of the stock

prices for the macroeconomic variables. However, there seems to be irregularity in the data whenobservations from the recent crisis are included. Finally, documents the disappearance of theimmediate positive liquidity effects of the money supply shocks and unstable interactions betweenthe stock prices and the exchange rate over time.

IntroductionFinance literature contains considerable number of studies that examine stock

price behavior. Perhaps, one important subject that has received increasingattention from economists, financial investors and policy makers is on dynamiclinkages between macroeconomic variables and stock returns. Based on thestock valuation model, macroeconomic forces may have systematic influenceson stock prices via their influences on expected discounted future cash flows.Alternatively, the relations between them may be motivated using thearbitrage pricing theory (APT) model developed by Ross (1976). Moreover, thestandard aggregate demand and aggregate supply (AD/AS) framework alsoallows for the roles of equity markets especially in the specification of moneydemand (Friedman, 1988) and in monetary transmission mechanisms (Mishkin,1998). These models provide a basis for the long-run relationship and short-run

dynamic interactions among macroeconomic variables and stock prices. Themain emphases have generally been on asset pricing, return predictability,stock market efficiency and equity price channel of monetary transmissionmechanisms.

The Emerald Research Register for this journal is available at The current issue and full text archive of this journal is available at

http://www.emeraldinsight.com/researchregister http://www.emeraldinsight.com/0144-3585.htm

The authors would like to thank two anonymous referees for helpful comments on the paper. Allremaining omissions and errors, however, are the authors’ responsibility. The authors wouldalso like to acknowledge the financial support from the Research Center, International IslamicUniversity under the short-term research grant.

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Journal of Economic Studies

Vol. 30 No. 1, 2003

pp. 6-27

q MCB UP Limited

0144-3585

DOI 10.1108/01443580310455241


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While a predominant number of empirical studies focus on industrializedeconomies, some recent studies have extended the analysis to the cases of developing economies. An illustrative list of studies for developed economiesincludes Fama (1981, 1990); Chen et al. (1986); Hamao (1988); Asprem (1989);Chen (1991); Thornton (1993); Kaneko and Lee (1995); Cheung and Ng (1998);Darrat and Dickens (1999). These studies identify such factors as industrialproduction, risk premiums, slope of the yield curve, inflation, interest rate,money supply and so forth as being important in explaining stock returns. Thefew notable studies for developing economies include Mookerjee and Yu (1997)and Maysami and Koh (2000) for Singapore, Kwon et al. (1997) and Kwon andShin (1999) for South Korea, and Habibullah and Baharumshah (1996) andIbrahim (1999) for Malaysia. Using bivariate cointegration and causality tests,Mookerjee and Yu (1997) note significant interactions between M 2 moneysupply and foreign exchange reserves and stock prices for the case of

Singapore. However, Maysami and Koh (2000) document significantcontribution of interest rate and exchange rate in the long-run relationshipbetween Singapore’s stock prices and various macroeconomic variables.Evaluating the Korean equity market, Kwon et al. (1997) provide evidence forthe exchange rate, dividend yield, oil price and money supply as beingsignificant macroeconomic factors. In a similar vein, Kwon and Shin (1999)establish a long-run relationship between stock prices and four macroeconomicvariables – industrial production index, exchange rate, trade balance andmoney supply – for Korea.

In the Malaysian context, the studies by Habibullah and Baharumshah(1996) and Ibrahim (1999) are notable. Habibullah and Baharumshah (1996)

employ cointegration analyses to evaluate the informational efficiency of theMalaysian stock market index and sectoral indices using monthly data from

January 1978 to September 1992. Considering real output and money supply( M 1 and M 2) in the cointegrating relation, they find no evidence forcointegration between them. Accordingly, they conclude that the Malaysianstock market is informationally efficient in the long run. However, using awider range of macroeconomic variables and longer data sample, Ibrahim(1999) concludes to the contrary. Specifically, the bivariate analysis suggestscointegration between the Malaysian stock prices and three macroeconomicvariables – the price level, credit aggregates and official reserves. Moreover,

the results from bivariate error correction models suggest that the stock pricesreact to deviations from the cointegrating relationship. The multivariatecointegration and causality analyses further suggest the significant role of theexchange rates in influencing short-run movements of the market prices.

The purpose of the present paper is to contribute further to the literature onstock market – macroeconomic variable linkages for developing economiesand, specifically, for the case of Malaysia. The specific issues that we attemptto address are stock return predictability or efficiency, monetary transmission

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mechanism via stock price channel and temporal stability of their interactions.To this end, the analysis contains the following features. First, we analyze theissue in a multivariate setting. The variables included are stock prices, industrialproduction as a measure of real activity, money supply, consumer price indexand bilateral exchange rate vis-à -vis the US dollar. Habibullah and Baharumshah(1996) consider only real output and money supply in their analysis of theinformational efficiency of the Malaysian equity market. Moreover, Ibrahim(1999) has a focus mainly on bivariate interactions. Our analysis, thus, mayfurther enrich our understanding of the Malaysian stock market behavior and itsinteractions with various relevant macroeconomic variables.

Second, we employ standard procedures of cointegration and vectorautoregressions (VARs). In the analysis, we go beyond existing analyses on theissue that end at reporting cointegration results and/or causality tests fromfinal models by evaluating variance decompositions and impulse responsefunctions. The procedures capture both direct and indirect effects of innovations in variables of interest on other variables. The variancedecompositions indicate the percentage of a variable’s forecast error varianceattributable to innovations in all variables considered. From these, we mayevaluate the percentage of equity return’s forecast error variance attributable tomacroeconomic shocks and vice versa. In addition, the impulse-responsefunctions capture the direction of response of a variable to a one standarddeviation shock in another variable. Accordingly, the dynamics that existamong these variables may be fully addressed.

Finally, in addition to the whole sample that spans about 22 years, we alsoapply rolling regression technique normally used in the money-income linkliterature (see, for instance, Thoma, 1994; Swanson, 1998) to evaluate thechanging interactions among the variables. Given the rapid development of themarket, it is generally contended that it becomes more efficient in digesting allrelevant macroeconomic information. Additionally, various works have notedthe increasing integration of national stock markets, especially after the stockmarket crash of October 1987 (Lee and Kim, 1993; Arshanapalli and Doukas,1993; Meric and Meric, 1997). Accordingly, the ways that the market respondsto external factors such as the exchange rate as well as to domestic variablesmay have changed over time. We attempt to address this issue by estimatingthe interactions over rolling subsamples.

The rest of the paper is structured as follows. We first describe the data and

evaluate their stochastic properties. Then, in the next two sections, we presentestimation results on the interactions among the variables for the wholesamples and rolling sub-samples. Finally, we provide concluding remarks andsome discussion on the findings.

Data and their temporal propertiesThe analysis considers the interactions between the Malaysian equity marketand four macroeconomic variables including real output, price level, money

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supply and exchange rate. The data are monthly for the period from January1977 to August 1998. We begin our sample in January 1977 due to theavailability of the stock price index from 1977 onwards. Then, the sample endsin August 1998 due to capital controls and fixed exchange rate imposed by thegovernment on 2 September 1998. Note that our sample includes severalobservations during the 1997/1998 Asian crisis[1], which started in July 1997.We believe that whether this crisis affects the results would be reflected in therolling regressions. The data on macroeconomic variables are obtained fromthe IFS-CD ROM while the stock price data are from Lian (1993); Investors

Digest (various issues).To measure stock prices, we use end-of-the-month values of the Kuala Lumpur

Composite Index (KLCI). The index, which is normally used to reflect theMalaysian equity market performance, is based on a sample of 100 componentstocks and is value-weighted. Real output is measured by real industrial

production index (IP). We employ the consumer price index (CPI), an oft-quotedindex for computing inflation, as a measure of the aggregate price level.Meanwhile, the money supply is represented by M 2 monetary aggregate ( M 2).With the shift in emphasis of the Bank Negara (i.e. Malaysia’s Central Bank)towards broader monetary aggregates during the mid-1980s, a broader monetaryaggregate seems to play a more important role in the conduct of monetary policy.Moreover, the broader monetary aggregate such as M 2 that we employ satisfiesboth wealth and substitution effects of monetary holdings and, accordingly,makes it more appropriate for the present analysis[2]. Finally, we use the bilateralRinggit exchange rate vis-à -vis the US dollar as a measure of the exchange rates(EXC). While we attempt to maximize the time span of the sample, the use of the

bilateral Ringgit-US dollar rate is justified based on its importance to Malaysianinternational transactions and economy. Elsewhere, compared to other measuresof exchange rates such as effective exchange rates, Ibrahim (2000) providesevidence for the relevant role of the Ringgit-US dollar rate.

As a pre-requisite for subsequent analyses, we first evaluate the integrationproperties of the variables under consideration. Table I reports the ADF and PPunit root tests for all the series, where the tests are implemented without andwith a time trend. With the exception of IP, the ADF and PP tests with andwithout the time trend for the variables in levels indicate that they are non-stationary. The PP test with the time trend, however, suggests the possibility

that IP is stationary. When first differenced, we find evidence that the variablesare stationary. Namely, the PP tests suggest stationarity in all variablesconsidered. The ADF tests further substantiate the stationarity of KLCI, IP,and M 2 when expressed in first differences. They, however, indicate thepresence of two unit roots in CPI and EXC. Since the results tend to suggestnon-stationarity in levels of the variables but stationarity in their firstdifferences, we proceed by contending that the variables belong to the I (1)process.


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Estimation results: whole sampleCointegrationSince the five variables are noted to be I (1), there exists the possibility that theyshare a long-run equilibrium relationship. To test this, we apply multivariate

cointegration tests of Johansen (1988); Johansen and Juselius (1990) and reportthe trace statistics and maximal eigenvalue statistics from these tests atvarious lag lengths, namely, 3, 6, 9 and 12 in Table II[3]. As may be noted from

the table, in most cases, the results suggest the presence of cointegration

among the variables. However, when we set the lag length to 6, we find noevidence of cointegration. These contradicting results for different lagspecifications are not unexpected as the JJ tests are noted to be sensitive to lag

length selection (Hall, 1991). However, in selecting the lag length, the minimum

ADF PPVariables No trend Trend No trend Trend

LevelsKLCI 22.1246 22.3359 22.1211 22.4363IP 20.2165 22.4023 20.6557 25.0827*

M 2 20.8819 22.1968 20.7200 21.4419CPI 20.7824 22.4388 21.5089 21.6485EXC 0.6455 20.8774 0.9843 20.5743

First differencesKLCI 24.1392* 24.2467* 214.560* 214.608*IP 23.2605** 23.1487*** 228.410* 228.355*

M 2 22.7967*** 22.7986 215.505* 215.478*CPI 22.2907 22.2113 213.576* 213.641*EXC 21.7098 22.0731 214.999* 215.227*

Note: *, ** and *** denote significance at 1 percent, 5 percent and 10 percent respectively

Table I.Integration tests

Null hypothesis Trace Max. eigenvalue Trace Max. eigenvalue

Lags ¼ 3 Lags ¼ 6r ¼ 0 83.845* 35.310* 64.781 26.502r # 1 48.535* 29.149* 38.279 20.132r # 2 19.386 9.888 18.147 10.234r # 3 9.498 8.545 7.913 7.763

r # 4 0.953 0.953 0.150 0.150

Lags ¼ 9 Lags ¼ 12r ¼ 0 74.378* 35.947* 81.861* 31.296**r # 1 40.431 19.316 50.564* 23.759r # 2 21.115 12.902 26.805 17.637r # 3 8.213 7.893 9.168 8.420r # 4 0.320 0.320 0.748 0.748

Note: ** and *** denote significance at 5 percent and 10 percent levels respectively

Table II.Johansen-Juseliuscointegration tests

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requirement is that the error terms for all equations in the system must beserially uncorrelated. Accordingly, we use Ljung-Box-Pierce Q statistics to testthe null hypothesis of serially uncorrelated errors up to lag orders of 24. Theresults (not reported here) indicate the absence of autocorrelation when the laglength is set to 12. Accordingly, our further analysis will be based on the laglength of 12 for the vector error correction model (VECM) or, equivalently, of 13for VAR representation.

At lag length equals 12, the trace statistics indicate the presence of twocointegrating vectors. Meanwhile, the maximal eigenvalue statistics indicates aunique cointegrating vector. In the case of more than one cointegrating vector,the vector that corresponds to the maximum eigenvalue is the most useful(Johansen and Juselius, 1990). Moreover, for the long-run relationship to bemeaningful for our analysis, the stock price index needs to contributesignificantly to the long-run relationship. The LR test confirms the importance

of the stock price in the cointegrating relationship. Assuming one cointegratingvector, the test statistics is 184.6, which is significant at 1 percent level. Thecointegrating vector representing the long-run relationship is (normalized onthe price level):

KLCI ¼ 0:2476IP þ 4:5197CPI 0:3957 M 2 1:5787EXC 9:0716: ð1Þ

These estimated long-run coefficients may be interpreted as elasticity measuressince the variables are expressed in natural logarithms.

The long-run relationship between stock prices and industrial production ispositive, similar to results obtained for the USA (Fama, 1990), Japan(Mukherjee and Naka, 1995), South Korea (Kwon and Shin, 1999), andSingapore (Maysami and Koh, 2000). The result is expected since, representingreal economic activity, the real industrial production directly influences firms’expected future cash flows. The result we obtained also indicates a positiverelationship between CPI and KLCI in the long run, which is consistent with thework of Khil and Lee (2000). Examining whether the common stocks can be agood hedge against inflation for ten Pacific-rim countries, Khil and Lee (2000)find a negative relationship between stock returns and inflation for allcountries except Malaysia. That is, Malaysia is the only country in the samplethat exhibits a positive association.

Interestingly, we find a negative long-run association between stock prices

and M 2 money supply. While Mukherjee and Naka (1995) and Maysami andKoh (2000) document positive long-run relation for respectively Japan andSingapore, Kwon and Shin (1999) note a negative relation. Moreover,examining five industrialized countries (Canada, Germany, Italy, Japan and theUSA), Cheung and Ng (1998) find ambiguous relation between the twovariables. Namely, while the money supply-stock price relation is positive forCanada, it is negative for Japan and the USA. For Italy and Germany, therelation is ambiguous since there are more than one cointegrating vector and


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their relation depends on which cointegrating vector is used. Theoretically, therelation between the two variables can be positive or negative. Theexpansionary effect of money supply on real economic activity suggests apositive relation (Mukherjee and Naka, 1995). Changes in money supply mayalso be positively related to stock prices through portfolio substitution (Dhakalet al., 1993; Cheung and Ng, 1998). However, if the increase in money supplygenerates inflation as well as contributes to inflation uncertainty, then it mightexert a negative influence on the stock prices. A related point is that the(unexpected) increase in money supply may generate risk premium, causingequity prices to fall (Cornell, 1983). Lastly, Bulmash and Trivoli (1991) arguethat the continued increase in money supply may exert a negative effect on thestock prices due to increasing inflationary pressures and subsequent policyorientation to contain the pressure. Our negative long-run coefficient seems toindicate the dominance of these negative channels.

However, a surprising aspect that arises from the forgoing discussion is thatmoney supply is negatively related to stock prices while consumer prices arepositively related to stock prices. While it is generally noted that monetaryexpansion is inflationary for the case of Malaysia and therefore a positivemoney supply-stock price relation should be expected, it needs to be noted thatthe relation between money supply and stock prices is not only accounted byinflationary pressures from monetary expansion. Inflation uncertainty andexpectations of future contractions may also accompany changes in moneysupply and generate risk premium for holding shares. Moreover, since thecoefficient captures the long-run relation, Bulmash and Trivoli’s (1991) viewson differential short- and long-run effects of monetary expansion may apply.

In the context of the Malaysian economy, the Malaysian Central Bank hasbeen highly active in achieving multiple objectives of stable price level, stableexchange rate, sustainable output growth and low unemployment.Accordingly, since at times certain objectives take priority, the Central Bankhas to shift policy stance. For example, the contractionary effects of oil priceshocks prompted easy monetary policy in 1973, which was then reversed totight monetary policy in 1974 to contain inflationary pressures. Similarly, theCentral Bank shifted to stabilizing the exchange rate in 1986 and allowed theinterest rate to increase despite its expansionary stance in 1985 to cope with therecession. This active participation may have intended effects in the short run

but generates risk premiums and uncertainty in the long term, prompting anegative relation between money supply and stock prices.Finally, we find a negative association between stock returns and the

Ringgit exchange rate, which is in line with the results reported for SouthKorea (Kwon and Shin, 1999) and Singapore (Maysami and Koh, 2000). Acommon feature of these economies is that they are highly dependent oninternational trade, i.e. on exports and imports of capital and intermediategoods. While currency depreciation encourages exports, it increases costs of

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production through increasing domestic prices of imported capital andintermediate goods. The latter effect of currency depreciation on real outputand accordingly expected cash flows of the firms seems more dominant.Moreover, the result is also consistent with recent observations during theAsian crisis that both stock prices and exchange rates substantially decreasedin value.

It needs to be reiterated that the above estimated coefficients relate only tothe long run relationship. That is, the estimated coefficients can be viewed asdescribing some trend relationship linking the variables concerned. They,however, do not tell us about short-run dynamics among the variables, thenature of which may be different from the ones described. Accordingly, weproceed to evaluate variance decompositions and impulse-response functionsbased on the VAR specification to capture the dynamic interactions among thevariables.

Variance decompositions and impulse-response functionsIn this section, we specify a dynamic model using VAR framework andgenerate variance decompositions and impulse response functions to examineshort-run dynamic interactions among the variables. Generally, there are twodifferent ways of specifying a VAR when the time series under study arecointegrated – an unrestricted VAR in levels or a VECM. Which specificationis more appropriate remains debatable. While the VECM convenientlycombines the long-run behavior of the variables and their short-run relationsand thus can better reflect the relationship among the variables, there is noguarantee that imposing restriction of cointegration can be a reliable basis for

making structural inferences (Faust and Leeper, 1997). Moreover, currentfinding is still unclear on whether the VECM outperforms the unrestricted VARat all forecasting horizons. Naka and Tufte (1997) found that the two methodshave comparable performance at short horizons. The support for the use of theunrestricted VAR can also be found in Clements and Hendry (1995), Engle andYoo (1987) and Hoffman and Rasche (1996). Accordingly, with lowcomputational burden required by the VAR in levels, we implement theVAR using the variables in levels.

Compactly, the VAR model can be expressed as follows:

Að LÞ zt ¼ ut ð2Þ

where A( L ) is a matrix of polynomials in the lag operators and z is a vectorconsisting of the five variables considered. Orthogonalized innovations in eachof the variables and the dynamic responses to such innovations are identifiedfrom the Cholesky decomposition of the variance-covariance matrix. For ouranalysis, the variables making up z are entered in the following order: z ¼ (IP,CPI, M2, EXC, KLCI). This ordering is consistent with various works that orderthe variables representing the goods market (IP and CPI) first and the variables


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from the asset markets (EXC and KLCI) last (Koray and McMillin, 1999; Parkand Ratti, 2000). This means that the good markets are affected with lags bythe variables higher in the ordering. However, they contemporaneouslyinfluence the monetary variable ( M 2) and asset price variables. Similarly, fromthe ordering, monetary innovations affect asset prices contemporaneously andare affected by asset price innovations with lags. We set the lag length of theabove VAR to 13, since we verified that the equations in the VECM are seriallyuncorrelated at 12 lags.

Table III provides variance decompositions while Figure 1 plots impulseresponse functions generated from the VAR. From Table III, while much of the variations in KLCI can be attributed to its own variations, we may notean important role of macroeconomic variables in forecasting variance of stock prices. These results seem to be in line with those found for Korea(Kwon and Shin, 1999). Using Granger causality tests, Kwon and Shin

(1999) found the significance of such variables as industrial production,money supply, exchange rate and trade balance in explaining changes instock prices. Similarly, Nasseh and Strauss (2000) note substantial fractionof stock price variance explained by real economic activity for six OECDcountries. However, while Nasseh and Strauss (2000) documented minorrole for nominal factors, our results seem to indicate dominant roles of monetary variables such as money supply, price level and exchange rate inexplaining variations in stock prices. In particular, after a period of 24

Innovations inVDs Periods KLCI IP M 2 CPI EXC

KLCI 1 92.62 0.66 2.77 3.78 0.176 76.16 0.20 8.26 7.73 7.64

12 58.85 3.89 22.33 8.23 6.6924 48.48 6.64 26.57 9.47 8.85

IP 1 0.00 100.00 0.00 0.00 0.006 2.30 82.63 7.72 2.91 4.44

12 5.24 66.62 7.48 3.74 16.9124 8.26 64.41 9.16 3.19 14.97

M 2 1 0.00 0.03 99.97 0.00 0.006 0.32 1.25 95.70 1.51 1.21

12 1.15 7.45 67.52 5.87 18.0024 6.25 14.63 33.69 6.69 38.74

CPI 1 0.00 3.41 0.00 96.59 0.006 7.90 1.76 10.87 73.75 5.73

12 6.47 2.06 13.60 61.36 16.5124 13.13 2.28 21.55 45.67 17.37

EXC 1 0.00 0.22 0.53 0.003 99.256 1.22 0.69 1.49 0.16 96.43

12 4.58 2.65 8.23 0.34 84.2124 9.53 3.24 12.88 1.57 72.79

Table III.Variancedecompositions

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Figure 1.Impulse-response

functions


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Figure 1.

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months, innovations in M 2 account for more than a quarter of the variationin KLCI. Note also that IP shocks appear to have delayed effects on thestock prices, i.e. after 21 months.

Substantiating the evidence from the variance decompositions, the stockprices respond to innovations in the macroeconomic variables (Figure 1).Interestingly, a one standard deviation shock in the money supply results inpositive equity price response, which peaks around seven to eight months afterthe shock and then subsides gradually afterward. Coupled with the negativelong-run coefficient estimated earlier, the results seem consistent with theargument made by Bulmash and Trivoli (1991) that the money supply hasimmediate positive liquidity effects and possible long-run negative effects. Noteagain that, in line with variance decomposition results, there seems to bepositive lagged responses of the stock prices to the industrial productioninnovations. Gjerde and Sættem (1999) also document similar lagged responses

of the stock prices to real activity in a small open, but developed, market of Norway. Finally, the stock prices respond negatively to Ringgit depreciationshocks. This may stem from the fact that the Malaysian economy is highlydependent on the imported capitals. Moreover, the depreciation shocks maygenerate risk in the market that reverses the inflows of portfolio investments,as observed during the recent crisis.

Our results also seem consistent with the contention that stock prices containinformation on future variations in macroeconomic variables. From Figure 1, wemay note that money supply seems to be accommodative to the stock priceshocks, responding positively over the entire horizons. The stock priceinnovations also have a total positive influence on the price level, which seems

to be in line with the accommodative nature of the monetary policy. However,they result in negative responses from the exchange rates. Accordingly, thepattern of the stock prices – exchange rates interactions seems to bedestabilizing in the sense that once shocks in one variable take place, the othervariable responds such that the shock variable reacts and amplifies itself. Forinstance, depreciation shocks decrease the stock prices, which result in furtherdepreciation. For the case of the industrial production, its responses to the stockprices are first negative and then turn positive after five months.

Apart from these stock prices-macroeconomic interactions, the substantialrole of the exchange rate to the Malaysian economy deserves highlighting,which is consistent with Malaysia being open and highly dependent on

international trade, in accounting for variations in other macroeconomicvariables. We may note particularly that M 2 seems to respond more to EXCshocks than to IP and CPI shocks. From the impulse-response functions, wenote that money supply has a negative response to depreciation shocks. Thus,money supply seems to be contractionary to contain the decreasing value in theRinggit and expansionary in the face of currency appreciation, suggesting themonetary policy process stabilizes the exchange rate. According to Siregar(1999), the policy to stabilize the exchange rate to keep the rate competitive is


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normally inflationary. As we may note, we indeed document the inflationaryeffects of currency depreciation in our case. Currency depreciation is also foundto be contractionary. However, its effect lasts only for about 18 months, whereit peaks at 12-month horizons and reduces to zero afterward.

Rolling regression resultsIn this section, we implement rolling regressions of the VAR model to gainfurther insights on the dynamic linkages among the stock prices and themacroeconomic variables. The rolling regression technique is generally appliedin the money-income link literature to evaluate possible diminishing predictiverole of monetary aggregates and is recently used by Park and Ratti (2000) forthe stock market – macroeconomic variable linkages. We follow a similarempirical approach for our case. Over the past years, Malaysia has witnessedrapid developments in its financial scene characterized by liberalization of interest rates, financial innovations, removal of trade barriers and rapid growthof equity market. This has made the Malaysian equity market more integratedto the international financial markets[4]. Accordingly, it is interesting to seewhether there is an evolving pattern in dynamic interactions among stockprices and macroeconomic variables under Malaysian changing financialenvironments. Moreover, as our sample includes some observations from therecent 1997/1998 Asian crisis[5], the results that we obtained previously maybe the artifacts of the crisis. By using the rolling regression method, we maypossibly discern the influences of the recent crisis on the interactions.

In running the rolling regressions, we need to set the size of the rollingwindow. To the best of our knowledge, there is no statistical method to set theoptimal window size. In existing works, the choice of the window size seemsarbitrary. With the luxury of more than 42 years of data, Park and Ratti (2000)employ 20-year window size (i.e. 240 observations). In our case, we have onlyabout 22 years of observations. From the related literature that we havereviewed in the introduction, the shortest sample that is used is about eightyears (Maysami and Koh, 2000). Accordingly, we settle with 13-yearobservations (i.e. 156 observations) as our effective window size (after theadjustment of lags) to exclude first the period of rapid stock marketdevelopments of the 1990s (exclusive of the 1990 observations). In whatfollows, we focus only on the interactions between the Malaysian market onone end and the macroeconomic variables on the other end.

Our first rolling sample spans from January 1977 to December 1990. Then, weadd one observation at a time and drop an initial observation such that thewindow size remains fixed. From these regressions, we obtain impulse-responsesfunctions for three-month, six-month, 12-month and 24-month horizons.Figures 2-5 plot the rolling responses of KLCI to innovations in the fourmacroeconomic variables – IP, M2, CPI and EXC. Meanwhile, Figures 6-9represent the responses of the four macroeconomic variables to innovations inKLCI.

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A noted feature from these plots is the irregularity in the responses among thevariables once observations from the recent financial crisis are included in the

samples. Indeed, for the case of the KLCI-EXC interactions, the responses of

KLCI to EXC innovations and vice versa are both erratic and substantial. Since

the inclusion of these observations seems to mask their dynamic interactions

for early years, we report only the results up to June 1997 for the two cases, i.e.

Figure 5 and Figure 9. Note that, excluding the observations from the recent

Figure 2.Impulse responses of the

KLCI to IP shocks –rolling regression


KLCI to M2 shocks –rolling regression


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crisis, the interactions between stock prices and macroeconomic variables seemgenerally to be in line with those from the whole sample. In other words, in thecase of long data set, the effects of including the crisis (which is a part of thewhole sample) may have not influenced the general conclusion of theestimation. However, due to irregularity that the crisis generates, it should beomitted from any empirical analysis that requires the understanding of thedata regularity especially for the case of small sample sizes.

Figure 4.Impulse responses of theKLCI to CPI shocks –

rolling regression

Figure 5.Impulse responses of theKLCI to EXC shocks – rolling regression

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In the case of KLCI-IP dynamic interactions, the results for all rolling sub-

samples concur well with the estimation using the whole sample. Namely, theysuggest lagged responses of KLCI to IP shocks and, similarly, lagged responsesof IP to KLCI shocks. Likewise, conforming to the results from the wholesample, KLCI-CPI dynamic responses to each others innovations, plotted inFigure 4 and Figure 8, exhibit a consistent pattern over all rolling samplesexcept the samples that include the recent crisis. In line with existing works forMalaysia and our earlier results, there seems to be a positive associationbetween the stock market movement and CPI.


IP to KCLI shocks –

rolling regression


M 2 to KCLI shocks –rolling regression


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One interesting pattern that emerges from these rolling regressions is that theresponses to money supply shocks by KLCI seem to shift over the rollingsamples, as indicated by Figure 3. As in the whole sample, we observe positiveresponses of the stock prices to money shocks at horizons from three to 12months. The responses seem to be higher at three- and six-month horizons.However, these positive responses seem to diminish as we move the windowtoward the recent observations. Again, including the samples from the recentcrisis, the responses become erratic with negative responses at the short

Figure 8.Impulse responses of theCPI to KCLI shocks –

rolling regression

Figure 9.Impulse responses of theEXC to KCLI shocks – rolling regression

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horizon and positive at longer horizons. As may be noted from thefigure, the 24-horizon responses of KLCI seem to shift substantially over therolling samples. First, KLCI exhibits positive responses and then negativeresponses for the samples that end around late 1992 and 1993. Interestingly, theMalaysian economy witnessed a drastic increase of the portfolio investmentinflows in 1993. Then, KLCI responses turn positive and reduce graduallyafterwards. For the samples that end around the crisis years, the 24-monthperiod responses turn erratic again. These results, we believe, can be taken asconstruing the positive liquidity effects of money supply in the short run butthe uncertain and negative effects in the intermediate or long run, as explainedearlier. Again, from these rolling sub-samples, we note the accommodativenature of money to stock price innovations (Figure 7).

Finally, there seems to be fluctuations in the responses to EXC innovationsby the stock market. In some samples, the responses are positive. Meanwhile, in

other samples, the responses are positive. Moreover, the direction of responsesfor a given horizon keeps changing over the rolling samples. Take six-monthhorizon as an example, the responses are first positive, then negative. Next,they turn negative and again positive. Only at 12-month horizon, the responsesseem to be consistently positive. The instability of the KLCI responses to theexchange rate shocks may suggest the danger of the shocks to the Malaysianequity market performance. From Figure 7, while EXC appreciates at three-month horizon, it depreciates at 12-month horizon. However, its responses tothe stock market shocks seem to be zero at six- and 24-month horizons. Thispattern of responses seems to suggest overshooting in the EXC.

Discussion and conclusionsThis study examines causal relations and dynamic linkages between theMalaysian stock market and four macroeconomic variables, namely, theindustrial production, the money supply, the price level and the bilateralexchange rate vis-à -vis the US dollar. The analysis relies on standard and well-accepted techniques of cointegration and VARs to uncover the long-runrelationship and short-run interactions among the variables using the data thatspan for about 22 years. From the VAR, we compute variance decompositionsand simulate impulse response functions to trace the strength of the Granger-causal links among them and the responses of a variable to innovations in other

variables. We also investigate the possible changing patterns in theinteractions by simulating impulse-response functions based on rollingregressions with a fixed window size of 13 years.

Empirical results we obtained bear various implications on the issues of equity market efficiency, monetary transmission mechanism, and temporalstability of dynamic linkages between macroeconomic variables and stockprices. The presence of cointegration between stock prices and macroeconomicvariables indicate long-run predictability of the Malaysian equity prices. In


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other words, at least in the long run, movements in the Malaysian equitymarket are tied to its economic fundamentals. Moreover, the dynamicresponses of the stock prices to changes in macroeconomic variables especiallyits lagged responses to real economic activity spell inefficiency in theMalaysian equity market. With the exception of its diminishing responses tomoney supply, the inefficiency of the market seems to persist over time.Accordingly, investors may gain by exploiting information contained inmacroeconomic variables for investment decisions.

Given that shocks in stock prices anticipate future variations in output andprice level, the stock prices may be used as an indicator variable for the conductof stabilization policies. However, our results raise caution or, to some extent,question on the implementation of monetary policies for the stability of thefinancial market. Although the role of money supply in the dynamic behaviorof equity prices seems to decline over time, at times, its relation to equity pricesis uncertain and in the long run is negative. This means that shocks in moneysupply may feed into the economy inflation instability, expectations of contractions and risk elements and, accordingly, result in adverse or uncertainbehavior of the stock market. The dominant role of nominal variables ininfluencing stock price behavior makes this point more relevant sincedisturbances in money supply can create nominal disturbances. Moreover,while the Central Bank has stated multiple objectives for monetary policy, itseems that it has an overriding concern on stabilizing the exchange rate. If thisis the case, an additional channel for possible instability in the financial marketmay exist, which stems from instability in the exchange rate – stock marketdynamic interactions. Accordingly, to the extent that policymakers can controlthe stock of money supply, they have to be well cautious of possibledestabilizing effects of monetary shocks on the financial market. Given thatstock price movements are tied to other macroeconomic variables, monetarydisturbances can generate real disturbances.

Finally, from the rolling regressions, it is worth mentioning that the Asiancrisis seems to create irregularity in the interactions between stock prices andmacroeconomic variables. Excluding the crisis years, we note consistentpatterns of interactions among the stock prices and the macroeconomicvariables, which conform to the findings for the whole sample. Accordingly, wedoubt that the Asian crisis has changed the pattern of dynamic interactionsamong the variables. In other words, the crisis may have only created

temporary irregularity in the variables’ interactions. To state this conclusively,however, is premature since more data are needed for the purpose, a potentialavenue for future research.

Notes

1. The 1997/1998 Asian crisis began with devaluation of the Thai baht on 2 July 1997, whichsubsequently affected exchange rates, stock prices and economic activities of manycountries in the East Asian and Southeast Asian regions. In the context of the Malaysian

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economy, both currency value and the Kuala Lumpur Composite Index plunged drastically.As a result, in 1998, Malaysian GDP recorded a negative growth of more than 7 percent.

2. The broadest monetary aggregate for Malaysia is M 3. However, monthly data on M 3 areavailable only from 1986 onwards. Since the analysis requires sufficiently long span of data

sample for us to be able to evaluate changing relations among variables, we use M 2 for ourpurpose.

3. These tests are now well known and, accordingly, are not explained here. Interested readersmay refer to Johansen (1988), Johansen and Juselius (1990) or Enders (1995) for detail.

4. Ibrahim (2001) provides some background information on financial developments inMalaysia. In the context of the Malaysian equity market, various studies have noted itsincreasing integration to major international market particularly the US. Arshanapalli et al.(1995), in particular, note that the “cointegrating structure” that ties the Asian stock markets(including Malaysia) and the US has increased especially after the October 1987.Additionally, Groenewold and Ariff (1998) attribute the greater predictability of the AsiaPacific equity markets (including again Malaysia) to the greater integration of internationalcapital markets.

5. Henceforth, we refer to the Asian crisis that started with the devaluation of the Thai baht assimply the recent financial crisis.

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Macroeconomic Variables and ICM in Malaysia

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