Optimum benefit of international diversifi- cation

Bambang Hermanto*

Department of Management, Universitas Indonesia

Fajar Indra**

Panin Sekuritas, alumni of Faculty of Economics Universitas Indonesia

This research studies the international co-movement among Asia Pacific emerging markets stock price indices during the late 2000s recession by using the monthly observations start from 1 st Octo- ber 2001 until 1 st April 2011. The co-integration analysis and parsimonious Vector Error Correction

Model employed in this research reveal a long-term relationship and interdependencies among seven Asia Pacific emerging market stock price indices. This research finds that the unique co-integation

exists on the equations. Specifically, two indices from China and Taiwan having meteor shower poten- tial while the rest indices from Thailand, Malaysia, and Indonesia are known to have heat waves ef- fects or country specific factors on the equation. Finally, all the results are linked to the international diversification strategies.

Keywords: Co-movement, co-integration, emerging market, heat waves, meteor shower, Asia Pacific, interdependencies, Vector Error Correction Model, international diversification

Introduction

1980–2000. In 2008, the prices of many com- modities, notably oil and food, rose so high as

International openness to trade and capital to cause economic damage, threatening stag- flows have a potential to increase the vulner-

flation and a reversal of globalization (Rubin, ability of a country to international shocks. The

2008). Some large financial institutions were recent international shock happened during the

collapsed and some national governments late 2000s when most securities suffered large

granted the bailout of the banks. The global re- losses erupted by the US subprime mortgage

cession also resulted in the downturns in stock collapse in August 2007. It has resulted in a

markets around the world. On the international sharp drop in international trade, rising unem-

stock market, Standard & Poor in December ployment and slumping commodity prices and

2009 reported that most countries in the S&P investments amount around the world. It also

Indices experienced a serious negative impact included a global explosion in prices, focused

on stock markets in 2008 as the effect of the especially in commodities and housing, mark-

global financial crisis took hold. ing an end to the commodities recession of

*Gedung Departemen Manajemen FEUI, Kampus Baru UI, Depok 16424, Indonesia, E-mail: bambang.hermanto@ui.ac. id **Gedung Bursa Efek Indonesia Tower II, Suite 1705, Jl. Jend. Sudirman Kav. 52-53, Jakarta 12190, Indonesia, E-mail: fajar@pans.co.id

Meanwhile, previous investigators found any interdependencies between some equity markets on crisis period since a high level of common short-run and long-run patterns of the market behavior tends to occur during the crisis. Forbes and Rigobon (2002) found any interde- pendencies during the 1997 Asian crisis, 1994 Mexican devaluation, and 1987 US market crash. They prefer to call it interdependencies since in their finding, co-movement does not increase significantly after the shocks. Baig and Goldfajn (1999) found a co-movement among financial markets of Thailand, Malaysia, Indo- nesia, Korea, and the Philippines. They found that correlations in currency increased signifi- cantly during the crisis period. Huyghebaert and Wang (2009) revealed that the relationships among East Asian stock markets were time var- ying. However, they revealed that there were integration and interdependencies among seven major East Asian stock exchanges before, dur- ing, and after the 1997–1998 Asian financial crises. Then, what about the latest 2000s reces- sion? Figure 1 depicts a structural break on all equity indices especially after the mid of 2008. The indices seemed to move together, declin- ing on the second semester of 2008, and then bouncing back one year after.

The typical co-movements and interdepend- encies on some crises suggest an unfavorable condition for creating some international port- folio diversifications. On the other side, pre-

vious investigators often stated that emerging markets are the best alternative solution for in- vesting in global economic crisis. Dattels and Miyajima (2009) stated that emerging markets are too worthy to miss because they have been relatively resilient to global market turmoil since they are able to avoid the kind of turbu- lence experienced in mature credit and other risk markets, especially in Asia. Emerging mar- ket returns typically have a low correlation with developed market returns (Errunza, 1983). The diversification potential of the emerging mar- kets is further supported by Harvey’s (1995) findings that the correlation between developed and emerging markets is less than 0.10. That implies that emerging countries’ equities may serve as attractive diversification vehicles for investors in developed countries.

This research seeks the internal relation- ship among Asia Pacific emerging market re- lationship during the late 2000s recession. The internal relationships may be revealed as co- movement or interdependencies. The investiga- tion is advanced by detecting the characteristics behind the transmission of the shocks, if any, based on Engle et al. (1990) hypothesis, “heat waves” and “meteor showers”. Finally, the im- plication of the interdependencies detected on this research is linked to the diversification pol- icy, especially for any equity investors around the globe.

INDONESIAN CAPITAL MARKET REVIEW • VOL.V • NO.1

Figure 1. Global stock market indices during the 2008 financial crisis

Literature Review

Optimum benefit of international diversifi- cation

The international portfolio activity will reach the optimum benefit when they are able to get the minimum risk. When the correlation coef- ficient between stock markets in one country of another are very low, it will imply that income portfolio that are balance-weighted greatly ben- efit from international diversification (Bodie et al., 2007). That is it, the basic technique for constructing the efficient international portfolio is still the efficient frontier. Markowitz (1952) has shown the main framework of the optimum mean-variance diversification in efficient fron- tiers concept. Portfolio risk depends on the cor- relation between the returns of the assets in the portfolio.

The variance of the portfolio is a sum of the contributions of the component security vari- ances plus a term that involves the correlation coefficient between the returns on the compo- nent securities. Therefore, Markowitz stated that a well-diversified portfolio means the port- folio with minimum variance so that the disper- sion of the expected returns will be minimized. For instance, once a portfolio is perfectly cor- related, it has a standard deviation which is a weighted-average of the standard deviation for each asset based on the weight of each asset in the portfolio. Thus, in this scenario investors do not obtain the risk reduction benefit from the diversification.

Co-movement

Baur (2004) once stated that there is no clear and unambiguous definition of term co-move- ment and no unique measure associated with it. It is also difficult to analyze in a time-varying context if it is based on the correlation coef- ficient. However, Barberis et al. (2005) deter- mined the explicit definition of co-movement as defined as a pattern of positive correlation. In general statistical usage, positive correlation means a correlation in which large values of one variable are associated with large values of

the other and small with small. Cahyadi (2009) described co-movement as co-integration, co- trending, and co-breaking. This definition was supported by Dolado et al. (1999) who stated that co-integration signifies co-movements among trending variables which could be ex- ploited to test for the existence of equilibrium relationships within a fully dynamic specifica- tion framework. Thus, this research concludes that the basic principle behind the term “co- movement” involves the common movements among any simultaneous variables during at certain time.

Furthermore, some common movement pat- terns have several distinctions when it comes to the characteristics affecting the movement. En- gle et al. (1990) proposed two hypotheses this characteristics. The first one is called the “heat wave” effect or country specific effect, a me- teorological analogy of a co-movement which is consistent with a view that major sources of disturbances are changes in country-specific fundamentals. In other words, it transmits the information internally within the same market on the previous t. The alternative hypothesis is “meteor shower” which rains down on the earth as it turns. The meteor shower effect transmits the information, as captured when they trade in different regions and time zones.

Previous empirical research

There are so many similar empirical re- searches that have been conducted to seek the interdependencies and international diversifica- tion among some stock market indices in any economic climate in the world. Azizan and Ah- mad (2008) revisited at the relationship between the movements of capital markets in developed economies and their emerging market counter- parts in the Asia Pacific region using market indices of the US, British, Malaysian, Singa- porean, Chinese, Hong Kong, Indian, Japanese, and Australian markets for the periods 1997 to 2007. They found several empirical results. The first one is the fact that the Asian markets are very much influenced by the events in the United States rather than other developed mar- kets. The second one is the fact that of all the

Hermanto and Indra

INDONESIAN CAPITAL MARKET REVIEW • VOL.V • NO.1

markets being surveyed, the South East Asian Dekker et al. (2001) found that there are markets are the most sensitive towards events

strong relationships on Asia Pacific stock mar- in their own regions outside themselves. Their

kets. All Asia Pacific stock markets, excluding last finding is the fact that Mainland China in

Taiwan, have significant interdependencies with the long-run is not affected by events outside

United States stock markets. Some significant themselves.

relationships also exist on Australia and New Baig and Goldfajn (1999) tested for evi-

Zealand, also among Malaysia, Singapore, and dence of contagion between the financial mar-

Hong Kong. While stock markets in Japan, the kets of Thailand, Malaysia, Indonesia, Korea,

Philippines, and Taiwan tend to be segmented. and the Philippines. They found that correla-

Dunis and Shannon (2005) examined the rela- tions in currency and sovereign spreads in-

tionships among developing countries’ stock crease significantly during the crisis period,

markets in Asia Pacific region versus US stock whereas the equity market correlations offer

market. They revealed a co-integrating relation mixed evidence. They showed that after con-

among developing countries’ stock markets and trolling for own country news and other fun-

US stock markets. However, in the short run, damentals, there is evidence of cross-border

the correlations among those stock markets get contagion in the currency and equity markets.

declining compared with previous period. Forbes and Rigobon (2002) showed that there is

a high level of market comovement in all peri-

Research Method

ods during the 1997 Asian crisis, 1994 Mexican devaluation, and 1987 US market crash, which

Data summary

is called interdependence. Huyghebaert and Wang (2009) examined the integration and cau-

This research uses seven countries as prox- sality of interdependencies among seven ma-

ies which are listed as Asia Pacific emerging jor East Asian stock exchanges before, during,

markets based on the S&P Global BMI (Broad and after the 1997–1998 Asian financial crisis st Market Index) as of December 31 2010 with

by using daily stock market data from July 1 st significant market capitalizations of listed com- 1992 to June 30 th 2003 in local currency as well

panies. It uses monthly adjusted close stock as US dollar terms. They revealed that the rela-

price indices returns of the proxies which tionships among East Asian stock markets were

are constructed by the market capitalization-

1 time varying. While stock market interactions st weighted method , from October 1 2001 as were limited before the Asian financial crisis,

the re-opening of US stock market after the

they found that Hong Kong and Singapore re- st September 11 2001 attacks, to April 1 2011 sponded significantly to shocks in most other

th

obtained from Yahoo Finance. The choosing of East Asian markets, including Shanghai and

the opening research period are also referred Shenzhen, during this crisis. After the crisis,

on Alan Greenspan’s decisive reaction to Sep- shocks in Hong Kong and Singapore largely af- th tember 11 attacks and the various corporate

fected other East Asian stock markets, except scandals which undermined the economy that for those in China. On their final conclusion,

led Federal Reserve initiated a series of inter- they stated that considering the role of the US

est cuts that brought down the Federal Funds showed that it strongly influences stock returns 2 Rate to 1% in 2004 . The proxies are listed in

in East Asia – except for China – in all periods,

Table _ 1.

while the reverse did not hold true.

1 Market capitalization-weighted method is computed by the following formula: 2 On October 15 th 2008, Anthony Faiola, Ellen Nakashima, and Jill Drew wrote a lengthy article in The Washington Post

titled, “What Went Wrong”, claiming that Alan Greenspan’s controversy as Chairman of the Federal Reserve in lowering of Federal funds rate at only 1% for more than a year triggering the subprime mortgage crisis 2007.

Hermanto and Indra

Table 1. List of major indices on Asia Pacific emerging markets

Ticker Country Jakarta Composite Index

KLSE Malaysia Shanghai Stock Exchange Composite

JKSE

Indonesia

Kuala Lumpur Composite Index

PSEI Philippines Bombay Stock Exchange Sensex

Philippines Stock Exchange Index

TWII Taiwan Stock Exchange of Thailand Index

BSESN

India

Taiwan Stock Exchange Index

SETI

Thailand

Source: S&P Global BMI (Broad Market Index) as of December 31st 2010

Table 2. Descriptive statistic of Asia Pacific emerging market stock price indices

SSEC TWII Mean

2241.9 6610.3 Std. Dev.

115 115 Source: Author's own calculation

Table 3. Pairwise correlation matrix of Asia Pacific emerging market stock price indices

Correlation BSESN

KLSE JKSE BSESN

0.971260 1.000000 Source: Author's own calculation

Table 2 shows the descriptive statistic lationship between PSEI and KLSE in 0.9803. among the proxies, compared with some devel-

The lowest correlation coefficient implies to the oped countries’ major indices relevant statis-

relationship between SSEC and SETI in 0.051. tics. There are two important aspects informed

The strong positive correlation coeficients in- by the table above. The first aspect is volatility.

dicates that most of variables are strongly co- BSESN was the most volatile index among the

moving on this decade. In other words, it can group members and KLSE was the least volatile

be said that the variables are strongly interde- one during 2001 until 2010. The high monthly

pendent on each other. From this phenomenon, volatility, as a quadratic function of standard

at glance investors do not obtain the risk reduc- deviation, in BSESN, TWII, SSEC, JKSE, and

tion benefit from the diversification. However, PSEI implies that Asia Pacific emerging market

correlation matrix does not show the dynamic is fairly volatile. The second aspect is the data

and simultaneous relationship among the vari- distribution. The excess kurtosis in Asia Pacific

ables since it can not ensure the integration and emerging markets is significantly greater from

interdependencies among the variables (Cahy- zero. It indicates a fat-tailed distribution. The

adi, 2009).

absolute skewness statistics are a bit greater than zero, revealing that the distributions are

Co-integration analysis

somewhat asymmetric. The existance of the co-movement on Asia

This research describes co-integration as a Pacific emerging markets indices can be rough-

set of variables with a stationary linear combi- ly withdrawn by analyzing the pairwise correla-

nation of them (Engle and Granger, 1987). The tion matrix on Table 3 above since most of the

co-integration test shall be conducted to seek if indices are strongly correlated to each other. The

there is any long-term relationship or equilib- highest correlation coefficient implies to the re-

rium among the Asia Pacific emerging markets’

INDONESIAN CAPITAL MARKET REVIEW • VOL.V • NO.1

stock price indices. The test of co-integration is

2. If the rank of Π is zero, then it will imply conducted by the Johansen’s test as the using

that there are no linear combinations of the Z t of multivariate system, begins with defining a

that are I(0), and consequently Π is an (n x n) vector Z t of n potentially endogenous variables

matrix of zeros. In this case the appropriate following Johansen (1988) and Harris (2003).

model is a VAR in first differences involving Z t is assumed as an unrestricted VAR system in

no long-run elements.

k-lags:

3. The third instance is when Π has reduced rank, then there will exist up to (n – 1) co-

Z t =A 1 Z t-1 +A 2 Z t-2 ... ... ... +A k Z t-k +ΦD t + μ+ε t

integration relationships: β’Z t-k ~ I(0). In this instance r ≤ (n - 1) co-integration vector ex-

where Z ists in is n x 1 and A

β, together with (n - r) non-stationary

1 is n x n matrix of pa-

rameters, μ is a constant, D t is a dummy vari- vectors. Only the co-integration vectors in β enter (equation 3), otherwise ΠZ would not

able that is orthogonal with μ constant, and er-

be I(0), which implies that the last (n – r) col- ε t is assumed to be independent. This way

t-k

ror

estimates dynamic relationships among jointly umns of α are insignificantly small. In this case the appropriate model is a VECM.

endogenous variables without imposing strong

a priori restrictions (Harris, 2003). The equa- To test the null hypothesis that there are at most r co-integration vectors can be used what

tion (1) can be reformulated into Vector Error has become known as trace statistic:

Correction Model (VECM) by subtracting Z t-1 of the both equation sides:

log(1- ) 3) ΔZ t = Γ 1 ΔZ t-1 +...+Γ k-1 Δ Zt-k+1 +ΠZ t-k +ΦD t + μ+u t 2)

λ trace = -2log(Q) = -T

r = 0,1,2, ... , (-2), (-1)

where Γi = - (I - A1 -…- Ai), (i = 1, …, k-1), where Q is the ratio between restricted maxi- and Π = - (I - A1 - … - Ak).

mized likelihood between unrestricted maxi- mized likelihood. Besides that, another test of

Equation (2) contains some information on the significant of the largest λ is the so-called both short term and long term error correction

maximal Eigenvalue or λ max statistic, formulat- models to ΔZ t, via the estimates of Γ and Π re-

ed as follows:

spectively. As will be seen, Π = αβ’, where α represents the speed of adjustment to disequi- λ

max = -T log(1- ) 4) librium and β is a matrix of long-run coeffi-

cients such that the term β’Z t-k embedded in

r = 0,1,2, ... , (n-2), (n-1)

equation (2) represents up to (n - 1) co-inte- gration relationships in the multivariate model

The maximum-Eigenvalue tests that there which ensure that the Z t converge to their long-

are r co-integration vectors againts the alterna- run steady-state solutions.

tive that (r + 1) exist.

Assuming Z t is a vector non-stationary I(1) variables, then the term (2) which involve ΔZ t

Vector Autoregression (VAR)

are I(0) while ΠZ t-k must also be stationary for u t ~ I(0) to be white noise. There are three instanc-

In a Vector Autoregression (VAR) model the es when this requirement ΠZ t-k ~ I(0) is met:

current value of each variable is a linear func-

1. If Π has full rank or there are r = n linearly tion of the past values of all variables plus ran- independent columns, then all variables will

dom disturbances. All the variables in a VAR

be stationary on level. It implies that there is are treated symmetrically by including for each no problem of spurious regression and the

variable an equation explaining its evolution appropriate modeling strategy is to estimate

based on its own lags and the lags of all the the standard VAR in levels.

other variables in the model. Suppose that each

Hermanto and Indra

equation contains k lag values of Y and X. In where can be reformulated into Vector Error this case, one can estimate each of the follow-

Correction Model (VECM) by subtracting Z t-1 ing equations by OLS:

of the both equation sides:

where the u is the stochastic error term, called 8) impulse or innovation or shock or white noise

where: Π = -(I-Φ 1 -Φ 2 ) = -Φ(1) and disturbance term.

Γ = -(Φ 1 +Φ 2 ) = -(I-Φ 1 ) Unrestricted VAR

From the decomposition above, the VECM model can be reformulated as detail estimation:

This type of VAR illustrates the value of VAR as a linear function of its value on the past. The value on the past of other variables is the serially uncorrelated error term. This type of VAR can be divided into VAR on level and VAR on first difference. The unrestricted VAR on level is used for stationary data or if Π has full rank or there are r = n linearly independ-

9) ent columns. Whereas, the unrestricted VAR on

first difference shall be used if the rank of Π is As described previously, the rank Π equals zero or if there are no linear combinations of

to αβ’, where α represents the speed of adjust- the Z t that are I(0)

ment to disequilibrium and β is a matrix of long-run coefficients. If the rank Π is k, then

Vector Error Correction Model α can be decomposed as k x 2 matrix while β is decomposed as 2 x k matrix on the bivariate

The model becomes a Vector Error Cor- VECM. Therefore, the error correction term of rection Model (VECM) which can be seen as

the equation (9) is determined follow:

a restricted VAR. This type of VAR restricts the long-term relationship of the endogenous variables so that there are convergent co-inte-

10) grations but still tolerance the short-term dy-

namics. This type of VAR is used for data that

Granger Causality test

has reduced rank on its Π or there exist up to (n – 1) co-integration relationships. When the

The causality testing can figure out whether variables are co-integrated, the error correction

an endogenous variables can be treated as an ex- term has to be included in the VAR. The co-in-

ogenous one. The causality relationship can be tegration term is also known as an “error” since

tested by Granger Causality test with assump- the deviation of the long-term equilibrium is

tion that the information relevant to the predic- corrected gradually through the partial series of

tion of the respective variables. The Granger short term adjustments. Recalling the bivariate

Causality test is a statistical hypothesis test for version of VAR equations (5) and (6), Z t value

determining whether one time series is useful in on VAR(2) can be decomposed as follows:

forecasting another (Granger, 1969). However, bivariate Granger Causality is not sufficient to imply true causality in multivariate system. A

similar test involving more variables can be ap-

INDONESIAN CAPITAL MARKET REVIEW • VOL.V • NO.1

plied with Vector Autoregression (VAR). On an

be overcome by creating the orthogonal error examination process of VAR model, it needs to

using Cholesky decomposition method. The conduct simultaneously so that there will be a

mathematical approach of the IRF is conducted combined significances on the equation (Ham-

by manipulating the VAR equations on (5) and ilton, 1994; Patterson, 2000). All VAR estima-

(6) into the matrices below: tions shall be tested on Wald Chi-squares dis- tribution (χ 2 -Wald). The statistical results of χ 2 -Wald shall show the joint significance of the

11) endogenous variables on VAR estimation.

Is this test always valid for every measure- From the equation above, it is obvious that ment? Toda and Phillips (1993) stated that the

the value of Y and X depend on their respective empirical use of Granger Causality tests in

lag and residual values. By focusing the deriva- levels VAR is not to be encouraged in general

tion into the influence of residual shock chang- when there are stochastic trends and the possi-

ing ε 1t and ε 2t on the value of Y and X, the equa- bility of co-integration 3 . That is, causality tests

tion (11) can be denoted as follows: are valid asymptotically as χ 2 -Wald criteria only

when there is sufficient co-integration with re- spect to the variables whose causal effects are being tested. Since the estimates of such ma-

12) trices in levels VAR suffers from simultaneous equations bias there is no valid statistical basis

Those φ11, φ21, φ12, and φ22 are the im- for determining whether the required sufficient

pulse response function coefficient. condition applies.

Result and Discussion

Impulse Response Function

Co-integration analysis

In structural analysis, certain assumptions about the causal structure of the data under in-

This research has found that all indices are vestigation are imposed, and the resulting caus-

stationary in first difference I(1). Thus, if there al impacts of unexpected shocks or innovations

is no co-integration found on the system, the to the specific variables on the variables in the

first difference VAR will be able to be con- model are summarized. These causal impacts

ducted. This research conducts the optimum are usually summarized with impulse response

lag based on the least result of Final Prediction functions and forecast error variance decompo-

Error, which is lag 1. The optimum lag length sitions.

declaration must be done before estimating the This research emphasizes on Impulse Re-

models since the simultaneous equation process sponse Function (IRF) as a tool on VAR anal-

such as VAR and co-integration test is very sen- ysis to see the response and the future effects

sitive to the lag length (Enders 2004). Based on to the shocks or changes in other variables in

the optimum lag declared, the Johansen’s co- the VAR system. An impulse response function

integration test is conducted. traces out the response of a variable of inter-

To determine the appropriate assumption of est to an exogenous shock. The shocks and in-

the Johansen’s co-integration test, five sets of novations of each variable are correlated each

assumptions that stated that the co-integration other so that those variables have the same

test should be conducted with intercept and component but are unable to be specifically at-

trend assumption on CE with linear determin- tributed to a certain variable. This problem can

istic trend in data. Dummy variable is set for

3 They developed a limit theory for Wald tests of Granger Causality in levels Vector Autoregressions (VAR) and Johansen- type error correction models (ECM), allowing for the presence of stochastic trends and co-integration. For further expla-

nation, see Toda and Phillips (1993). 8

Hermanto and Indra

Table 4. Johansen’s co-integration rank test on trace statistic method

No. of CE(s)

Prob. None *

Eigenvalue

statistic

Critical value

0.0006 At most 1

0.1597 At most 2

0.3955 At most 3

0.7001 At most 4

0.8965 At most 5

0.8779 At most 6

0.9509 At most 7

0.9170 Trace test indicates 1 co-integrating eqn(s) at the 0.05 level Source: Author's own calculation

Table 5. Johansen’s co-integration rank test on maximum Eigenvalue method

Hypothesized

0.05 Prob. no. of CE(s)

critical value

0.0002 At most 1

0.3400 At most 2

0.3965 At most 3

0.5540 At most 4

0.9364 At most 5

0.7488 At most 6

0.9212 At most 7

0.9170 Max-Eigenvalue test indicates 1 co-integrating eqn(s) at the 0.05 level Source: Author's own calculation

Table 6. Adjustment coefficients on unique co-integration equation (standard error in parentheses)

D(TWII) D(SSEC)

D(D) D(BSESN) -0.27961

D(SETI)

D(PSEI)

D(KLSE)

D(JKSE)

-1.55E-05 -0.2 (-0.08306)

(-1.90E-05) (-0.1837) Source: Author's own calculation

identifying the crisis period and non-crisis (1x8) matrix while matrix of long-run coeffi- period. Crisis period (July 2007 – present) is

cients β is decomposed as (8x1) matrix. Table 6 represented by 1 dummy value and non-crisis

reveals the adjustment coefficients of the vari- period is represented by 0 dummy value. The

ables. TWII and SSEC, with highest adjustment Johansen Co-integration test shows that there is

coefficients, at glimpse, tend to follow vari-

1 co-integration rank on both trace statistics and ables with smaller adjustment coefficients or in maximum Eigenvalue test type. Co-integration

other words, it is called interdependency. The analysis variable ordering is JKSE-SSEC-

comprehensive explanation of the movement BSESN-KLSE-SETI-PSEI-TWII-DUMMY. Ta-

interdependency is described after analyzing ble 4 and 5 comprehend the co-integration test-

the results in Vector Error Correction Models. ing results from both methods above. Both maximum Eigenvalue and trace statis-

Vector Error Correction Model

tic indicate one co-integration equation in 5% levels. That means there is only one long-term

Since there is one linear combinations of equilibrium possibility among the markets or

the variables proof by the results of Johansen Π has reduced rank. Furthermore, it is called

co-integration test on previous subsection, the

a unique co-integration. In this instance one appropriate model to estimate is a Vector Er- co-integration vector exists in β, together with

ror Correction Models (Harris, 1995). The op- seven non-stationary vectors. The speed of ad-

timum lag is 1, declared by the VAR lag order justment to disequilibrium α is decomposed as

selection criteria for first difference data on

INDONESIAN CAPITAL MARKET REVIEW • VOL.V • NO.1

Table 7. Vector Error Correction Model estimates

Error Correction: D(JKSE)

D(TWII) D(D) CointEq1

D(SSEC)

D(BSESN)

D(KLSE)

D(SETI)

D(PSEI)

-0.4188 -2.3E-05 [-0.4992]

[-0.8036] D(JKSE(-1))

[ 1.3571] [ 0.7177] D(SSEC(-1))

-0.09229 1.20E-05 [-0.7668]

[-0.5317] [ 0.2984] D(BSES(-1))

-0.28373 -2.7E-05 [-1.6663]

[-1.4033] D(KLSE(-1))

[ 0.2271] [-0.3974] D(SETI(-1))

1.513761 -6.2E-05 [ 0.2823]

[ 1.1252] [-0.1985] D(PSEI(-1))

[ 1.6956] [ 0.7191] D(TWII(-1))

[ 1.3945] [ 0.5596] D(D (-1))

-414.296 -0.03997 [-0.7618]

[-0.9507] [-0.3943] C -20.3597

[-0.4974] [ 0.9591] R -squared

0.270639 Source: Author's own calculation

Table 8. Parsimonious VECM with DSSEC as dependent variable

Prob. DJKSE

Variable

Coefficient

Std. error

t -Statistic

0.0103 R -squared

C -1024.478

57.05365 Source: Author's own calculation

F-statistic

previous subsection. The equation is conducted The VECM equations suggest that there ex- with intercept and trend assumption on CE with

ists an interdependence pattern in response to linear deterministic trend in data.

SSEC. The significant influences come from The comprehensive VECM estimates for

the response of JKSE, BSESN, and PSEI so that seven Asia Pacific emerging market indices

the parsimonious equation is built using those are listed in table 7. The numbers within the

variables as independent ones. The estimation parentheses describes the t-statistics value.

is conducted on first difference data. Table 8 The F tests given in that table are to test the

shows the results of parsimonious VECM with hypothesis that collectively the various lagged

SSEC as dependent variable. coefficients are zero. The high F-stat value on

The “meteor shower” effect apparently ex- SSEC and TWII equations reveals the “meteor

ists on the internal relationship effects on shower” potential on those variables. However,

SSEC. With R-squared 0.675 and F-stat coef- the statistically least significant lag variables

ficient 57.056, the parsimonious VECM sug- are sequentially eliminated so that parsimoni-

gests that SSEC is significantly influenced by ous specification is obtained following Ndako

JKSE on the previous lag, BSESN on the previ- (2008). The parsimonious VECM is used to ex-

ous lag, and PSEI on the previous lag. It can amine the existence of significant interdepend-

be said that the main factors that influenced the encies among variables.

SSEC movement pattern are BSESN, JKSE, and

Hermanto and Indra

Figure 2. The causality relationship on Asia Pacific emerging market stock price indices Table 9. Parsimonious VECM with DTWII as dependent variable

Prob. DBSESN

Variable

Coefficient

Std. Error

t -Statistic

0.0000 R -squared

F-statistic 341.1123 Source: Author's own calculation

PSEI. However, to say that it consists of meteor criteria only when there is sufficient co-integra- shower effects, the exogeneity test should be

tion with respect to the variables whose causal conducted since that effect examines the cau-

effects are being tested. Since the data have one sality relationship among variables.

co-integration relationship, the Granger Cau- The VECM equations also suggest that an

sality test can be used to detect the specific cau- interdependence pattern exists in response to

sality relationship among variables. The greater TWII. Only that the significant influences come 2 χ -Wald value suggests the endogenous vari-

only from the response of BSESN. The parsimo- able significantly cause the exogenous variable. nious VECM again prove that the relationship

The empirical of the Pairwise Granger Cau- between TWII and BSESN significantly exists.

sality suggests that BSESN Granger causes At last, the VECM equation suggests that JKSE 2 SSEC (χ = 14.85839), PSEI Granger causes

and 2 PSEI are not significantly influenced by SSEC (χ = 12.01752), and BSESN Granger other proxies. The tendency of the heat waves 2 causes TWII (χ = 11.96404). Eight bivariate effect seems existing on these situations though

causality relationships that are found on those the situations on JKSE and PSEI are inconclu-

variables strengthen the co-integration pattern sive. The causality relationship to examine the

among the variables.

direction of causality is tested by Multivariate From those types of Granger Causality test, Granger Causality test based on the output of

the results can be summarized into the causal- parsimonious VECM test.

ity relationship Figure 2. It depicts that in the system, the major sources of disturbances are

Granger Causality test

changes in country-specific fundamentals, espe- cially in PSEI, BSESN, SETI, KLSE, and JKSE.

Toda and Phillips (1993) stated that the cau- In other words, it transmits the information in- sality tests are valid asymptotically as χ 2 -Wald ternally within the same market and tend to suf-

Impulse Response Functions

On IRF model, the response of the shock in variables to the new information is measured by 1 standard deviation (SD) innovations. The impulse response similarities will be found on SSEC and TWII responses. In response to SSEC, the negative responses on early periods will be found on the responses of all variables in the second period. Except the response of KLSE, those variables will bounce back on the next period significantly. Meanwhile, KLSE will significantly respond on the second period, and then get less significant response on the third period. Meanwhile in response to TWII, both TWII and SSEC will respond to TWII’s shocks negatively until period 5, and then reach the long term equilibrium on the next period. It is consistent with the responses of those indices to all indices but SETI. KLSE will emerge the same response like its responses to PSEI and SETI, which will give a monotonic response since it will just positively respond until period

5 and then reach the steady state condition or long-term equilibrium. The shock of JKSE will be responded posi- tively by all variables on the first four periods. The positive response to JKSE simply means that the shock of JKSE will cause the rise on the responding variables, vice versa. The nega- tive response to JKSE will be continued to the fifth period, except the response of KLSE. The responses tend to be less significant to converge to the long-term equilibrium. Meanwhile in re- sponse to PSEI; SSEC, TWII, and SETI will make a positive response in period 2 and turn down in period 3. Only that SETI will still have

significant positive responses until period 8. The responses to BSESN shock also tend to be less significant to reach the long-term equilib- rium. The significant negative responses to the shock of BSESN on period two will obviously happen on all variables. At last, the responses of all variables to KLSE on early period are ho- mogenous. They will positively respond until period 3.

Particularly, the empirical result of the Im- pulse Response Function test simulates that the country specific factor is the main reason that the signal will be transmitted through the shock of the variable in the future. Most significant response will happen on the early period, es- pecially in period two, since the responses tend to be less significant to reach the long-term equilibrium in the indefinite period. The similar functions looks like happen on certain indices, like in China and Taiwan as countries of East- ern Asia emerging market. Similarity also hap- pens in response to Indonesia and China in one SD innovations. All indices will positively re- spond to both JKSE and KLSE on early period, and then will converge into steady state shock.

Conclusion

The observed high correlations across Asia Pacific emerging markets brings into question the wisdom of large diversification benefits from international investing on the region. A long term relationship among seven Asia Pacif- ic emerging market stock price indices during October 2001 until April 2011 suggests that in- vestors did not obtain the risk reduction benefit from the diversification since the indices do not offset the risk of other indices. That means all tested variables in the short term were integrated each other to reach their long term equilibrium. The co-integration is unique since there is no flexibility to achieve equilibrium in equation. This situation can be associated as a common movement among any simultaneous variables during the research period since there exists some influences on the series which imply that the markets are bound by some relationship in the long run or integrated, in other words. So, since the market integration across Asia Pacific

INDONESIAN CAPITAL MARKET REVIEW • VOL.V • NO.1

Several interdependencies on the multivari- ate systems are revealed by parsimonious Vec- tor Error Correction Model. The estimations of SSEC and TWII, which belong to East Asian hemisphere, are the most significant estimates among the others. They tend to follow certain variables from the previous time (t – 1). The multivariate Granger Causality tests strengthen the phenomenon that SSEC and TWII have me- teor shower potential. Meanwhile, the rest vari- ables such as KLSE, SETI, and JKSE tend to have heat waves effects since they are not sig- nificantly influenced by other variables in mul- tivariate system. The result is consistent with Engle et al. (1990) statement that the “meteor showers” and “heat waves” effects are not mu- tually exclusive and, hence, during any period of time both of them can co-exist, even though one may dominate. The Impulse Response

Function simulates that the country specific factor is the main reason that the signal will be transmitted through the shock of the variable in the future. Most significant response will hap- pen on the early period then the responses tend to be less significant to reach the long-term equilibrium in the indefinite period.

For the next research, the analysis and find- ing of volatility spillovers during the crisis is encouraged since the stock movement may vary over time. The volatility analysis provides more information of the variables’ interdepend- encies and information transmissions and is very useful in detecting the existence of time- varying variance and volatility clustering on the observed data. When return distribution data shows asymmetric pattern, and the associated variances are non constant, the resulting model can be used to predict (Febrian and Herwany, 2009). Since this research employs only 114 monthly data during the global financial crisis 2008, the detail estimations on the pre -crisis and on-crisis period are not possible to estimate. The monthly data is al so not encouraged to es- timate the variance process since the standard deviations of the data will be so high (Engle et al., 1990). That is the reason that this research does not touch the variance process estimation to determine the volatility spillovers.

Hermanto and Indra

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