Return and Volatility Spillovers Effects: Study of Asian ...

Journal of Systems Science and Information Apr., 2018, Vol. 6, No. 2, pp. 97–119 DOI: 10.21078/JSSI-2018-097-23

Return and Volatility Spillovers Effects: Study of Asian Emerging Stock Markets Bhowmik RONI∗ Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100190, China E-mail: [email protected]; [email protected]

Ghulam ABBAS School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, China E-mail: [email protected]

Shouyang WANG Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China University of Chinese Academy of Sciences, Beijing 100190, China E-mail: [email protected] Abstract This paper examines the extent of contagion and interdependence across the six Asian emerging countries stock markets (e.g., Bangladesh, China, India, Malaysia, the Philippine, and South Korea) and then try to quantify the extent of the Asian emerging market fluctuations which are described by intra-regional contagion effect. These markets experienced both fast growth and key upheaval during the sample period, and thus, provide potentially rich information on the nature of border market interactions. Using the daily stock market index data from January 2002 to December 2016 (breaking the 15 years data set into three sub periods; pre-crisis, crisis, and post crisis periods); particularly make attention to the global financial crisis of 2007∼2008. The return and volatility spillovers are modeled through the GARCH (generalized autoregressive conditional heteroscedasticity), pairwise Granger causality tests, and the forecast error variance decomposition in a generalized VAR (vector auto regression) models. This paper shows that volatility and return spillovers behave very differently over time, during the pre-crisis, crisis, and post crisis periods. Importantly, Asian emerging stock markets interaction is less before the global financial crisis period. The return and volatility spillover indices touch their respective historical peaks during the global financial crisis 2007∼2008, however Bangladeshi market faces this condition in 2009∼2010. Keywords spillover; stock returns; volatility; global financial crisis; GARCH; Granger causality; variance decomposition

1

Introduction

In modern economic system, financial markets make big rule particularly stock markets. Global financial markets become more connected than ever before. Nevertheless, crises are the Received May 24, 2017, accepted December 7, 2017 ∗ The Corresponding author

98

RONI B, ABBAS G, WANG S Y.

vital part of global financial markets, which has two common base, search for higher return and associated risk, for these crises. Although the developed economies are the main reason for these financial crises, the emerging and other developing countries are also affected by these crises. In August 2007, US subprime crisis was responsible for the global financial crisis which shows the worldwide fall of major banks and stock markets. Global financial crisis 2007∼2008 is considered as the worst financial crisis since the Great Depression of the 1930s. In financial crises, volatility in stock markets has enlarged rapidly and most of the time stock returns moves into negative territory. The true thing is that each of these crises is not only remained in originated country, furthermore, these crises are rapidly spreading to their border countries and financial integrated countries. Fluctuations in volatility of originated country’s stock market might be affected by the stock market volatility of their territory countries. Some of the studies[1−3] mention this issue is called as volatility spillover effects. Stock market investment decisions are affected by market volatility spillover effects, which has significant values for market investors and policy makers. Therefore economists and researcher are very interested in this issue because financial crisis play a big rule to break down for the global economic system. The global financial crisis 2007∼2008 is not the first financial crisis in the economic world but this crisis affects more in the financial markets, especially in the emerging countries markets. The impact of US subprime crisis is found in Chinese stock market which is considered as one of the biggest emerging economies in the world that justifies the highly volatile stock index return between 2007 and 2008, stock market is declined at 71%. Another Asian stock market is mostly affected in Indian stock markets and these markets are declined at 60% in the index. Among the many serious global financial crises, global financial crisis in 2007∼2008 is extremely affected by the world economy because of the correlation and interactions in global financial markets. As the intensity of this crisis has a negative effect on the volatility pattern of stock price return which varies from country to country based on their financial system and stock markets. In this chapter, the integration of six Asian emerging stock markets (East, South, and Southeast Asian countries) are investigated and paying particular attention to the global financial crisis period. This chapter is motivated by that respect of earlier literature[4−9] of financial market integration of investigation. All of these literatures mainly focus on the developed stock markets which are highly correlated with each other and the volatility of the U.S. stock market is transmitted to other developed markets more rapidly. Lagarde[10] mentioned that, in recent world, 60 percent of global GDP comes from emerging and developing economies but the half of this is found in the last decade. Since the 2008 financial crisis, 80 percent of global growth is contributed by the emerging and developing economies. Undoubtedly, last two decade’s role of emerging markets become more important, participants are not only depended on developed countries markets. Now a days, economists and researchers[11−16] are paying more attention to the emerging stock markets. They mention that the direction of the return spillover is developed stock markets, such as the US, the UK, Germany, French, and Japan, to emerging stock markets. In this chapter, major East, South, and Southeast Asian countries stock markets are inves-

Return and Volatility Spillovers Effects: Study of Asian Emerging Stock Markets

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tigated. Over the past few decades, East, South, and Southeast Asian markets have established into a center hub in the global financial markets. Furthermore, these markets are attaining a progress rate developing financial markets; these Asian markets are started to making significant role rapidly in the global stock markets. Hence, it is motivating to investigate how integrated Asian emerging markets are affected during the global financial crisis 2007∼2008. It can be elaborated for further study that whether the economic integration of the region can help or not in dampening the negative transmission effects of those shocks into the region. Glick and Rose[17] found out that the shocks spread all the way through intra and intraterritory trade agreement. Inter territory trade agreements, namely Association of South-East Asian Nations (ASEAN) South Asian Association for Regional Cooperation (SAARC), Trade in Goods Agreement (ATIGA), South Asian Free Trade Area (SAFTA), Asia–Pacific Trade Agreement (APTA), and ASEAN–China Free Trade Area (ACFTA) have potentially led a vital role in shaping the size of the growths. However, some Asian emerging countries still have low degree of trade and financial integration, and the magnitude of intra-Asia trade remains significantly huge despite specially East Asia territory. Bhowmik et al.[18] investigate the most closely related to this chapter, who examine the correlation dynamics for six Asian emerging countries during the financial crisis respect to the developed (U.S., the UK, Japan, and Singapore) stock market. This chapter contributes to both the global financial crisis 2007∼2008 literature and studies on the Asian emerging markets by investigating the effects of the global financial crisis, particularly on the integration of the Asian emerging stock markets during the recent global financial crisis, which has not been previously examines roughly. However, none has focused return and volatility spillovers effects together on the East, South, and Southeast Asian emerging stock markets. This study is aimed to analyze the causality linkage among the Asian emerging stock markets. The main contribution of this chapter is found in the following three aspects: 1) The data cover very recent years, 2002 to 2016, which have not been covered in previous studies of emerging Asian markets. 2) By breaking the 15 years data set into three five years sub-periods that includes the global financial crisis. The results are obtained for 2002∼2016 (full), 2002∼2006 (pre-crisis period), 2007∼2011 (crisis period), and 2012∼2016 (post crisis period), respectively. The comparison of different periods is useful to find out when markets are becoming more volatility effects influenced by regional markets. 3) This study considered most popular econometric models, which are more powerful. These are: GARCH (1, 1) model, pairwise granger causality tests, and VAR model and the variance decomposition analysis to 60 periods long rolling windows of Asian emerging stock markets returns and volatility measures separately. The rest of the chapter is structured as follows: (I) the background and study inspiration; (II) presents a brief literature review; (III) presents the details on the data set and outlines the basic analysis; (IV) briefly presents the methodology for the data analysis; (V) presents the analysis and empirical findings; (VI) draws the summary of these findings.

2

Literature Review

It is not carrying long history of work on contagion, works start on after U.S. stock market crash 1987. Several researchers have studied stock market integration after 1987 crash few of

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them; Engle and Ng[19] , Chowdhury[20] , Theodossiou and Lee[21] , Wei, et al.[22] , Leachman and Francis[23] , and Aggarwal, et al.[24] . Choudhury[25] used a GARCH-M model to examine the market volatility and the persistence of shocks to the volatility before and after the crisis. This chapter follows simple GARCH type models but extends the work before crisis, crisis time, and after the crisis time. In additionally, Bekaert and Harvey[26] focused emerging market volatility, and found that volatility depends on market position. Two factors represent the markets where world factors influence volatility, if it is fully integrated markets and local factors influence volatility, if it is segmented markets. It shows that there are correlation between global capital markets and emerging markets that might be varied in different approaches. Though the correlation of both the markets is increasing[27−30] , nevertheless, emerging markets volatility is not carried out in global markets. After the East Asian and Russian financial markets crises that financial contagion and spillovers have become a most important part of financial research. Subsequently, a large body of this literature[31−35] has been work in the volatility spillover after the East Asian and Russian crises of 1997∼1998. Liu, et al.[36] examined the structure of international transmissions in daily returns for six national stock markets. Their results indicate that the US market plays a dominant role in influencing the Pacific-Basin markets; Japan and Singapore together have a significant persistent impact on the other Asian markets, while the markets in Taiwan and Thailand are not efficient processing international news. Ng[6] investigated the effects of volatility that spread out from Japan and the U.S. to the Pacific-Basin territory and finds that the two factors influence the Pacific-Basin territory market volatility but U.S. market news influence more the territory market volatility. Chen, et al[37] mentioned that the volatility return in advanced markets is affected more by the news comes from U.S. where bad news affects more. It is found from the analysis that U.S. bad news affect the open markets particularly. Nevertheless, Lim, et al.[38] argued that the earlier literature investigate that most of these Asian emerging markets refer that investors are not only react on local market news; however, similarly follow the news of other developed stock markets. Similarly, Hammoudeh and Li[39] found out that Gulf Arab stock markets are so sensitive that firstly global news affects these markets than local news. Chiang, et al.[40] investigated that the results are similar to this investigation, they examine the nine Asian stock markets over the period from 1990 to 2003 and they indicate that the financial crisis time is an increase in correlation, which they refer to as a contagion effect, and a continued high correlation in the few month of the crisis, referred to as too much herding in stock markets. Emerging stock markets, returns volatility has extreme place, on the other hand, developed markets are more stable, and that is why investors are more interested in emerging markets remarked by Abugri[41] . This chapter receives a large number of related literature and studies on global financial crises 2007∼2008. These include, for example, Asian emerging markets literature contributed by Fidrmuc and Korhonen[42], Engle, et al.[43] , Morales and Andreosso-O’Callaghan[44], Wang[45] , Kim, et al.[46] and Li and Giles[47] ; and other emerging markets literature contributed by Beirne, et al.[16] , Samarakoon[48], Terazi and Senel[49] , Tudor[50] , Min and Hwang[51] , Neaime[52] , Bekaert, et al.[53] , Bekiros[54], Dungey and Gajurel[55] , Sugimoto, et al.[56] , Alotaibi


101

and Mishra[57] , Maghyereh, et al.[58] , Pragidis, et al.[59] , and Mensi, et al.[60] . The literature indicates that numerous stock markets are expressively affected by the global financial crisis of 2007–2008. Yilmaz[61] found out that the U.S. sub-prime crisis 2008 which assist to get the position on volatility of stock returns in East Asian countries. Syllignakis and Kouretas[62] investigated the correlation between Central and Eastern European emerging markets which are affected by global financial crisis but dramatically this crisis could not affect the German stock market. Samarakoon[48] found out that frontier stock markets have shown interdependence and contagion to U.S. market shocks. Sakthivel, et al.[63] studied the recent global financial crisis which is pessimistically impacted the mean returns and pessimistic information causes larger volatility impact than optimistic news in the Indian stock markets. Existing studies focus on primarily dynamic linkages between developed markets, but some others focus on linkages between emerging markets. They considered linkages within the Asian region, and also between Asia and other emerging economies, and find out the evidence for changed causality patterns on a regional level, and also on a global level as the USA crisis are started. Following the volatility spillover literature, the common econometric methodologies are the Generalized AutoRegressive Conditional Heteroskedasticity (GARCH), GARCH-M, VAR, and Stochastic Volatility (SV) models; few of them use correlation of variance and causalities tests. Further, Lee[64] used the two-stage procedure based on GARCH-M model to evaluate the return and volatility spillover effects from developed markets to Asia than Latin America markets using daily stock returns and find out the larger spillover effects from developed markets. This chapter intends to examine the return and volatility spillover between the six Asian emerging markets by correlation in conditional variance from the GARCH model, pair-wise granger causality test, and VAR model where the daily index returns series of 2002 to 2016 has been observed. Specifically, it is focused on how and to what extent stock return and volatility in Asian emerging stock markets which are influenced by regional stock markets.

3

Data and Preliminary Analysis

The time series data are formed in the daily closing prices for the six Asian emerging stock markets of general indices for the period 02 January, 2002 to 30 December, 2016. Firstly, work with full sample periods which divide these sample periods into three sub-periods; post-crisis period, crisis period, and last one post-crisis period. This chapter analyses the daily data on Stock indices return of Bangladesh, China, India, Malaysia, the Philippine, and South Korea for the above mentioned period. This study is principally based upon secondary data that the series are obtained from the database maintained by East, South, and Southeast Asian emerging stock exchanges websites (e.g., Wind Data base and dsebd.org). The stock indices are used for the examination which is the most important benchmark index for each country. The observations of the six emerging stock market indices-DSEX index for the Bangladesh stock market, SSE composite index for the Chinese stock market, BSE 30 index for India stock market, FBMKLCI for the Malaysia stock market, PSEi index for the Philippine stock market, and KOSPI index for the South Korea stock market are selected. All indexes are denominated


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by countries’ currencies. The public holidays and special events missing data are major problem for daily data. To resolve this problem, missing data are calculated with the relevant index record of previous and next price to average the result of the missing index price. 3.1

Basic Statistical Analysis

As for the analysis purpose, the stock index of each market is converted into stock index return to avoid complications following the algorithm expressing the difference in the logarithm between the yield of closing price of today and of yesterday’s (Equation (1)), where yt = log pt − log pt−1 .

(1)

Table 1 shows the various countries and their respective indices include in the study and data are collected from the website of respective stock exchanges. Table 1 Sample countries and their indices Sources: Each countries stock exchange website Country

Region

Stock Ex- Location change

Founded Index Selected

Abbreviation Listed Used Companies

Bangladesh

South Asia

Dhaka Stock Exchange

Dhaka

1954

DSEX

DSEX

China

East Asia Shanghai Stock Exchange

Shanghai

1990

SSE Com- SSE Composite 1,240 posite

India

South Asia

Mumbai

1875

S&P BSE BSE30 SENSEX

6,354

Malaysia

Southeast Bursa Asia Malaysia Berhad

Kuala Lumpur

1964

FBM KLCI

900

Philippine

Southeast The Philip- Metro Manila 1927 Asia pine Stock Exchange

Bombay Stock Exchange

South Korea East Asia Korea Ex- Busan change

1956

FBMKLCI

562

PSE Com- PSEi posite

344

Korea KOSPI Composite

2,030

Figure 1, shows the shape of the Asian emerging stock market indices during the investigation period. It expresses that the dependence in the volatility of the market indices is almost flat from 2002 to 2004. In mid of 2004 to 2005, it is found a bit more volatile because of its nature of the stock market. In other words, periods of low volatility tends to be followed by periods of low volatility for a long time. Similar scenario goes for the periods of high volatility. From the end of 2006 to 2008, market indices tends to have unexpected boom expressing market volatility, after effected by the global financial crisis market follow downward trend, only


103

exception is found in Bangladeshi market. Bangladesh market faces big bubble of 2009 to 2010; stock price index exhibits a downward trend in 2011. Furthermore, on 12 June 2015 Chinese stock market turbulence began with the popping of the stock market bubble and ended in early February 2016. It is found from the graph that comparing to the other emerging markets, the Bangladeshi stock market is not as volatile as the mature stock markets and the Chinese market is one of the world biggest market capitalization stock markets and this market volatility is too much. 30,000

25,000

20,000

15,000

10,000

5,000

0 2002

2003

2004

2005

2006

2007

2008

2009

BD MY

Figure 1 The daily indices

2010

2011

CN PH

2012

2013

2014

2015

2016

IN KR

Sources: Author’s calculations

Figure 2, shows the daily markets indices returns during the sample period. Daily returns fluctuate around zero and are characterized by volatility clustering. All returns demonstrate higher volatility in 2007∼2008. During the year 2009 to 2010, Bangladesh index returns tend to have jump to unexpected huge index returns. After the market scam in 2009, the movement of markets indices returns tends to have normal as expected various phases of ups and downs in market returns. Nevertheless, in 2015 Chinese market face another big bubble in the markets. .25

.20

.15

.10

.05

.00

-.05

-.10

-.15 2002

2003

2004

2005

2006

2007

2008 BD MY

2009

2010 CN PH

2011

2012

2013

2014

2015

2016

IN KR

Figure 2 The daily indices return volatility clustering



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Table 2 presents a brief overview of the descriptive statistics for the returns of each equity series for the daily sample period. The results show that the daily means for all the six equity returns are positive; however, they are very small compared to the standard deviations. More specially, the smallest of the emerging stock markets Bangladesh has the highest unconditional average with daily stock market return of around 0.0006, Indian stock markets return are similar to the Bangladeshi markets. The returns for Bangladesh fluctuate between the minimum of −0.099 and a maximum of 0.226. Volatility is usually very high in emerging stock markets, but it is one of the highest in the Asian emerging markets. As measured by standard deviation, it is the first highest in China at 0.016 and the lowest in Malaysia at 0.007. The majority of these stock returns have pessimistic skewness and optimistic kurtosis, which means that the stock returns possibly will not be normally distributed. The skewness of Chinese, Malaysian, and the Philippine stock returns is negative but Malaysian market is dramatically more negative, which is consistent with most developed countries, while that of Bangladeshi, Indian, and South Korean stock returns is positive. All the equity returns have fat tails or are leptokurtic as seen in the excess kurtosis, indicating that GARCH models can be used for pricing these series. To have “fat tails” is the tendency of financial markets because of the extreme outcomes in the form of bubbles and crashes. Instead of falling, markets tend to rise at a lower speed. However, after the sudden changes in U.S. market news are included in the GARCH model, the standardized residuals present much lower kurtosis and skewness. Moreover, the Jarque-Bera test strongly rejects the normality of the stock returns series in this sample periods. Table 2 Descriptive statistics


Countries

BD

CN

IN

MY

PH

KR

Mean

0.0006

0.0003

0.0006

0.0003

0.0005

0.0004

Median

0.0004

0.0007

0.0008

0.0003

0.0004

0.0005

Max.

0.226

0.095

0.173

0.044

0.098

0.161

Min.

−0.099

−0.088

−0.111

−0.095

−0.123

−0.106

Std.Dev.

0.014

0.016

0.014

0.007

0.012

0.014

Skewness

1.27

−0.24

0.14

−0.73

−0.44

0.03

Kurtosis

22.75

7.45

13.79

13.90

10.12

13.48

JarqueBera

13595

9263

8971

9671

8368

7868

Prob. Of JB

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

Observations

3905

3905

3905

3905

3905

3905

Note: The symbols BD, CN, IN, MY, PH, and KR denote the countries, Bangladesh, China, India, Malaysia, Philippine, and South Korea, are respectively.

3.2

Unit Root Test

In time series data analysis, it is significant concern to find out if the data series is stationary or non-stationary. The presence of a unit root in the Asian stock markets returns is tested using the Augmented Dickey-Fuller (1979); Phillips-Perron (1988); and Kwiatkowski, Phillips, Schmidt, and Shin (1992) unit root test. The Augmented Dickey-Fuller and Phillips-Perron


105

tests the null hypothesis with the presence of a unit root, but not reject that hypothesis. An alternative unit root test is the Kwiatkowski, Phillips, Schmidt, and Shin test which is specifically designed to test the null hypothesis of stationarity against the alternative of a unit root. Table 3 Unit root test Countries

Bangladesh

China

India

Malaysia

Philippine

Sources: Author’s calculations. Sample 2002∼2016 Levels

Period

First Differences

ADF

PP

KPSS

ADF

PP

KPSS

Full

−1.18

−1.24

5.63***

−27.64*** −59.98***

0.09

Pre-Crisis

−0.79

−0.78

3.28***

−32.93*** −32.93***

0.18

Crisis

−1.27

−1.26

3.41***

−27.25*** −34.44***

0.17

Post-Crisis

−3.46***

−3.67***

0.66**

−14.19*** −35.70***

0.13

Full

−1.78

−1.76

2.05***

−28.17*** −60.63***

0.07

Pre-Crisis

2.94

2.78

0.64**

−34.37*** −34.52***

1.03****

Crisis

−1.15

−1.21

1.39***

−35.97*** −35.99***

0.22

Post-Crisis

−1.56

−1.37

2.27***

−15.97*** −32.76***

0.08

Full

−0.97

−0.92

6.99***

−58.05*** −57.94***

0.04

Pre-Crisis

1.67

1.56

3.85***

−26.37*** −33.15***

0.47**

Crisis

−1.77

−1.71

1.18***

−33.17*** −33.11***

0.10

Post-Crisis

−1.71

−1.72

3.65***

−34.08*** −34.04***

0.15

Full

−1.26

−1.23

7.05***

56.17***

−56.28***

0.13

Pre-Crisis

0.18

0.07

3.77***

−32.88*** −33.09***

0.14

Crisis

−0.96

−0.99

1.52***

−32.27*** −32.26***

0.16

Post-Crisis

−2.39

−2.22

1.03***

−32.44*** −32.29***

0.22

Full

−0.51

−0.53

7.14***

−34.20*** −57.87***

0.08

Pre-Crisis

1.12

1.26

4.04***

−32.02*** −32.02***

0.34*

Crisis

−0.74

−0.65

1.81***

−31.89*** −31.73***

0.21

Post-Crisis

−2.51

−2.49

3.19***

−20.84*** −34.32***

0.25

Full

−1.56

−1.53

6.52***

−61.27*** −61.33***

0.06

−0.05

0.03

3.56***

−35.57*** −35.61***

0.19

Crisis

−1.81

−1.78

1.27***

−35.18*** −35.19***

0.08

Post-Crisis

−4.52***

−4.53***

0.85***

−35.85*** −36.01***

0.03

South Korea Pre-Crisis

*Denote statistical significance at the 10% level. **Denote statistical significance at the 5% level. ***Denote statistical significance at the 1% level.

In Table 3 unit root tests for the full period and three sub periods (pre-crisis, crisis, and post crisis periods) are reported. The unit root tests for daily indices price data between index level and first differences are close to zero at all significance level. The ADF and PP tests indicate that the null hypothesis of the existence of a unit root in the levels of each of the six


106

index prices series cannot be rejected. The only exception is found in the post crisis period in Bangladeshi and South Korean markets where the null hypothesis is rejected both at the 1%-level in ADF and PP tests. The KPSS test statistics suggest rejecting the null hypothesis of stationarity for all market indices and data frequencies for the full period and the three sub periods.

4 4.1

Methodology GARCH Model

GARCH model developed by Bollerslev[65], this model have become well-known tools for dealing with time series heteroskedasticity and are more broadly used to model the conditional volatility of economic data analysis. The most commonly used GARCH model is GARCH (1, 1) that meets 2 σt2 = α0 + α1 α2t−1 + β1 σt−1 . (2) Given conditional variance equation has three components: the constant term, using the mean equation, the lag squared residuals to measure the volatility obtained and the last forecast variance (GARCH items). GARCH model assumes that there are two equations mean and variance equation. The equation follows α0 > 0,

αi > 0,

βi > 0,

α1 + β1 < 1.

(3)

So, the next period forecast of variance is a blend of last period forecast, and last period’s squared return. 4.2

Granger Causality Tests

Granger causality tests are first proposed in 1969, this tests are guided to determine whether the existing and lagged values of one variable affect another. Granger causality demonstration theorem is that if two variables, say Xt and Yt are co-integrated and each is individually i, then either Xt must Granger-cause Yt or Yt must Granger-cause Xt . We adopt the bivariate VAR model to estimate granger causality relationship. The following bivariate regression is used where X Granger causes Y and Y granger causes X: Yt = α0 + α1 Yi−1 + · · · + αn Yi−k + β1 Xi−1 + · · · + βn Xi−k + εt ,

(4)

Xt = α0 + α1 Xi−1 + · · · + αm Xi−1 + β1 Xi−k + · · · + βm Xi−k + μt .

(5)

The calculated F-statistics is used to accept or reject the hypothesis. The first regression Equation (4) is used to test the hypothesis “one country stock price index (e.g., Bangladesh stock market), say X does not granger cause the another country stock prices index (e.g., Chinese stock market) say Y ” and the second regression Equation (5) is used to test the hypothesis “one country stock prices index (e.g., Chinese stock market) does not granger cause the another country stock prices index (e.g., Bangladesh stock market)”. 4.3

VAR Model

Developed by Sims[66] , the VAR model is suitable for capturing the volatility transmission across the markets. Thus, the conventional VAR model is used to conduct dynamic analysis


107

of the adjustment of the volatilities to shocks deriving from various markets through variance decomposition analyses. The mathematical representation of mth order VAR is Vt = C +

m

Bs Vt−s + t ,

(6)

s=1

where Vt denotes a (6 × 1) column vector of equity market conditional volatility for the 6 stock markets under consideration, C denotes a (6 × 1) vector of constants, Bs are (6 × 6) matrix of autoregressive coefficients, m is the lag length and t is a vector of innovations that maybe contemporaneously is correlated but that are serially uncorrelated with their own lagged values and uncorrelated with all of the right hand side variables. The moving average takes the following form: Vt = C +

k

As t−s ,

(7)

s=0

where Vt denotes a linear combination of present and past of one step ahead forecast error or innovations. It describes the coefficient As that can be interpreted as the response of one stock market returns to one standard error shock of any of the market under study s periods ago. As in Equation (6), the t ’s are also maybe contemporaneously is correlated but these are serially uncorrelated with their own lagged values and uncorrelated with all the past Vs . Cholesky decomposition is proposed by Sims[66] . This study uses the conventional Cholesky decomposition estimation criterion to decompose the error components. To conduct the shortrun dynamic analysis, the GARCH-M estimation is used to be in the Vt vector.

5 5.1

Results and Analysis Correlations in Conditional Variances

In Table 4, the sample correlation in variance of the returns for full, pre-crisis, crisis, and post crisis periods are focused. Squared returns are used to represent the time-varying variances of the returns. Panel A presents the full periods reports, the correlations between most of the markets are positive which tend to indicate that there is a common factor to drive the markets in the same direction. The only exception is the Bangladesh market that stands alone with the lowest correlation with all emerging Asian countries markets. The inter-Asia stock market correlations are stronger except the Bangladesh. The emerging market namely, India, Malaysia, Philippine, and South Korea seem to be highly correlated in the territory markets. The reason of high co-relation is that there are trade agreements of the bilateral and multilateral as well as financial links between these countries. Panel B presents the pre-crisis period results that the correlation in the variances of the stock returns are very low in the pre-crisis periods. The highest markets correlations are found between Malaysia and India at 18%. Panel C presents the most important crisis periods, the results are noticeable and huge different from the pre-crisis periods. All countries correlations increase statistically significantly only exception is found in Bangladesh markets. Most noteworthy results that correlation in-


108

creases inter Asian emerging markets in the global crisis periods. Malaysian market shows the highest correlation of 36% with the Philippine market. Table 4 Correlations in the variances of the stock returns (%) Countries

BD

CN

IN

MY

Source: Author’s calculations PH

KR

A. Full Periods (2002∼2016) BD

100

CN

−0.20

100

IN

−0.79

6.09*

MY

−0.38

10.56*

9.06*

100

PH

−0.22

9.75*

20.18*

16.12*

100

KR

−0.96

7.48*

14.20*

16.50*

16.71*

100

100

B. Pre Crisis Periods (2002∼2006) BD

100

CN

4.89

100

IN

−3.04

0.98

100

MY

3.65

10.38*

2.02

100

PH

0.96

5.44*

4.49

2.19

100

KR

−0.16

−0.48

10.13*

13.29*

7.25*

100

C. Crisis Periods (2007∼2011) BD

100

CN

−1.42

100

IN

−0.95

5.80*

100

MY

−0.47

16.46*

11.80*

100

PH

−0.34

8.57*

10.97*

24.42*

100

KR

−1.24

14.01*

11.24*

23.26*

23.85*

100

D. Post Crisis Periods (2012∼2016) BD

100

CN

1.66

100

IN

0.01

10.81*

100

MY

−1.79

2.00

13.81*

100

PH

−1.79

15.37*

27.19*

19.04*

100

KR

−4.23

9.53*

25.74*

11.84*

15.72*

100

Note: The symbols BD, CN, IN, MY, PH, and KR denote the countries, Bangladesh, China, India, Malaysia, Philippine, and South Korea, respectively. *Denote statistical significance at the 5% level.

Panel D presents the post crisis periods results, one striking observation is that the Panel D results appear to be very similar to the results of Panel B. Nevertheless, the highest correlation in variances with Indian market is that of the Philippine market at 22%, followed by Philippine


109

and Malaysia market at 21%. However, results show that Asian emerging markets correlation increases more. Overall, the results indicate that the Asian emerging stock markets become more closely correlated during the post-crisis period. The Asian emerging markets conditional volatility is presented in Figure 3. During the crises the volatility increases, experiencing the highest levels in six Asian emerging markets during 2007 to 2009 in the global financial crisis period. Only exception is Bangladeshi stock market, this market pick the highest levels during periods from 2009 to 2011, while the conditional variances are lower before 2009. In particularly, the Chinese stock market face another crisis

India .06

.05

.04

.03

.02

.01

.00 2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

Conditional standard dev iation

2012

2013

2014

2015

2016


110 Malay sia .036 .032 .028 .024 .020 .016 .012 .008 .004 .000 2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

2012

2013

2014

2015

2016


Philippine .06

.05

.04

.03

.02

.01

.00 2002

2003

2004

2005

2006

2007

2008

2009

2010

2011


South Korea .06

.05

.04

.03

.02

.01

.00 2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016


Figure 3 The conditional volatility


after the global financial crisis 2015, this crisis start by the stock market turbulence began with the popping of the Chinese stock market bubble June 2015 to February 2016 and Chinese market experiencing the historical highest levels during this period. 5.2

Pair-Wise Granger Causality Tests

In order to check short run causal causality, we estimate pairwise granger causality test and results are presents in Table 5. The results of granger causality test show that there are


Table 5 Pairwise Granger causality summary

111

Source Author’s calculations

F-Statistic and Causality Direction (CD)

Null Hypothesis Pre-crisis F-St.

CD

Crisis F-St.

CD

Post-crisis F-St.

CD

CN does not Granger Cause BD

1.0302

CN = BD

0.0026

CN = BD

1.0911

CN = BD

BD does not Granger Cause CN

1.6775

IN does not Granger Cause BD

5.5029**

BD does not Granger Cause IN

0.1607

MY does not Granger Cause BD

0.1827

BD does not Granger Cause MY

0.6748

PH does not Granger Cause BD

0.8128

BD does not Granger Cause PH

1.9346

KR does not Granger Cause BD

2.6934

BD does not Granger Cause KR

0.5174

IN does not Granger Cause CN

1.0351

CN does not Granger Cause IN

0.7161

MY does not Granger Cause CN

1.2658

CN does not Granger Cause MY

3.3059*

PH does not Granger Cause CN

2.3654

CN does not Granger Cause PH

3.6715*

KR does not Granger Cause CN

0.2572

CN does not Granger Cause KR

0.2293

MY does not Granger Cause IN

1.5617

IN does not Granger Cause MY

3.1962*

PH does not Granger Cause IN

0.5093

IN does not Granger Cause PH

4.8743**

KR does not Granger Cause IN

0.9511

IN does not Granger Cause KR

7.1807**

PH does not Granger Cause MY

1.1604

MY does not Granger Cause PH

3.5309*

KR does not Granger Cause MY

0.3116

MY does not Granger Cause KR

0.0133

KR does not Granger Cause PH

2.5079*

PH does not Granger Cause KR

1.4806

0.9475 IN → BD

0.8323

0.3986 IN = BD

0.8171 MY = BD

1.1031

1.0690

MY= BD

0.0135

PH = BD

6.8276**

KR = BD

0.8427

IN ↔ CN

0.2773

MY= CN

0.0951

CN →PH

2.2327*

KR = CN

0.8078

IN ↔ MY

2.3358

IN →PH

4.4777*

IN →KR

8.0719**

MY↔ PH

13.2396** 3.1420*

0.3678*

PH ↔ CN

2.7225

CN →KR

0.5929

IN →MY

1.9012*

IN ↔ PH

7.7406**

IN ↔ KR

0.6637

MY = PH

2.8900 KR↔ MY

1.8816 KR → PH

CN →MY

13.6812**

11.8463** KR = MY

0.5741

8.4977**

12.6654** MY →PH

CN →IN

5.7640**

9.3405** IN →KR

2.9565

0.1235**

7.6370** IN →PH

KR = BD

2.4580**

2.2175 IN →MY

3.8231

1.3905**

4.6604** KR = CN

PH = BD

3.4556*

1.4002 CN →PH

2.8917

0.7934

5.8516** CN → MY

MY = BD

0.1232

0.7928 IN = CN

1.4804 0.5079

1.7302 KR = BD

IN → BD

0.9550

0.0289 PH = BD

3.1388*

2.9202*

KR → MY

1.0386 KR ↔ PH

4.1750*

KR → PH

1.1743

Note: * and ** denote the significance level at 5% and 1%, respectively. The symbols BD, CN, IN, MY, PH, and KR denote the countries, Bangladesh, China, India, Malaysia, Philippine, and South Korea, respectively. Sample: 2002∼2006 (Pre-Crisis), 2007∼2011 (Crisis), and 2012∼2016 (Post-Crisis).

significant differences in the causality relationship across the three sample periods. For example, in case of pre-crisis, crisis, and post-crisis sample period there is no short run causality relationship between Chinese and Bangladeshi markets. Similarly, there is no sign of causal connection between Malaysian, Philippines, and South Korean stock markets with Bangladeshi stock markets during the pre-crisis, crisis, and post-crisis sample period. Exception only Indian


112

markets, the results indicate a bidirectional causality running from India to Bangladesh stock market during the pre-crisis and post-crisis period, while there is no causality in either way during the crisis period. In case of Chinese stock market there is either an unidirectional or bidirectional causality (e.g., China and India, China and Malaysia) exits among of the emerging Asian countries stock markets, particularly crisis and post-crisis periods there causality increase. Most causal connection exist Indian stock markets with among emerging markets during all periods. Malaysian, Philippine, and South Korean markets casual connection exist among emerging countries except Bangladeshi markets. Results indicate the global financial crisis 2007∼2008 is establishing the dynamic linkages of Asian emerging stock markets stronger. 5.3

Dynamic Analysis of the Conditional Volatility

Variance decomposition is used to identify the causal relationships among the Asian emerging markets. It demonstrations the extent to which a stock market is explained by the innovations or shocks in all the stock markets in the system. In Tables 6(a) and 6(b) presents the variance decompositions those are calculated using the Cholesky factorization based variance decompositions, where the markets are ordered as presented. Both tables reveal the order dependence of the variance decompositions. Panel A in Table 6(a) shows the percentage of the Bangladeshi markets return that is explained by shocks to return in other markets. It is clear that the Bangladeshi market is not affected by other countries. In Table 6(b) Panel A shows the percentage of the Bangladeshi markets return volatility that is explained by shocks to volatility in other markets. Indian markets shock starts to increase significantly the volatility in Bangladeshi markets from the 40 period on. Bhowmik, et al.[18] found out that volatility of the Bangladeshi market has been explained by the shocks of its own market and their results are almost parallel to this result. As Bangladesh stock market is merely quite a small, this market yet opens like other five markets. Panel B in Table 6(a) presents the case of Chinese markets return spillover. A maximum 3% of the variation in Chinese market can be explained by the Malaysia factor in about 60 days. From South Asian emerging territory markets, India has the significant impact on the Chinese market. Indian market shock starts to increase significantly the return in Chinese market from the 20 period on. In Table 6(b), Panel B shows the Asian territory markets shock starts to significantly increase the volatility in the Chinese market from the 20 period on. A maximum at 5% of the variation in emerging Chinese market can be explained by the Malaysia factor in about 60 days. Panel C in Table 6(a) shows the case of Indian markets, it is found that Indian market is major affected by Chinese and Malaysian markets in Asian emerging countries. Chinese market shock starts to increase significantly the return in Indian market from the 1 period on. Form Table 6(b) in Panel C results presents that India return volatility that is explained by shocks to volatility in other countries. Without Bangladeshi market other Asian emerging markets have lagged significant impacts on Indian market volatilities. Chinese market shock starts to increase significantly the volatility in Indian market from the 20 period on. Panel D in Table 6(a) shows the case of Malaysian market return spillover. The Indian, and Chinese markets have lagged significant impacts on Malaysian market return spillover. Indian market shock starts to increase significantly the return in Malaysian market from the 1 period


113

on. A maximum at 12% of the variation in Malaysian market can be explained by the Indian factor. In Table 6(b) Panel D shows the Chinese, Indian, and the Philippine markets shock starts to significantly increase the volatility in the Malaysian market from the 20 period on. The Chinese markets impact explains about 8% variability in Malaysian markets return volatility. Panel E in Table 6(a) presents the case of the Philippine markets return spillover. The Malaysian and Indian from the Asian emerging markets has significant lagged impact on Philippines market return spillover from the 5 period on. In Table 6(b) Panel E shows the border country Malaysian stock market shock starts to significantly increase the volatility in the Philippine market from the very immediately. Chinese and Indian markets have lagged significant impacts on the Philippine market volatilities from the 20 period on. Panel F in Table 6(a) shows the case of South Korean market return spillover. It is found that South Korean market is major affected by Indian and Malaysian markets in Asian emerging countries. Indian and Malaysian markets shock starts to increase significantly the return in South Korea market from the 1 period on. A maximum at 16% of the variation in South Korean market can be explained by the Indian factor. In Table 6(b) Panel F shows the Indian and the Philippine markets shock starts to significantly increase the volatility in the South Korean market from the 10 period on. Consequently, the similarity of results is found between the modeling approaches which are really remarkable. In Table 6(a), the number of the lags in the VAR is 2. The (i, j)th value is the estimated contribution to the variance of the 8-week-ahead stock return forecast error of country i coming from innovations to the stock return of country j. The Choleski decomposition is in the following order: Bangladesh, China, India, Malaysia, Philippine, South Korea, and United States. Using other numbers of lags or orders of decomposition yields similar results. In Table 6(b), the number of the lags in the VAR is 2. The (i, j)th value is the estimated contribution to the variance of the 8-week-ahead stock return volatility forecast error of country i coming from innovations to the stock return volatility of country j. The Choleski decomposition is in the following order: Bangladesh, China, India, Malaysia, Philippine, South Korea, and United States. Using other numbers of lags or orders of decomposition yields similar results.

6

Conclusion

Examining the interrelationships in term of return and volatility spillover effects are among the selected six Asian emerging countries stock markets and pay special attention to the effects that is created by global financial crisis 2007∼2008. By applying simple GARCH (1, 1) model, pairwise granger causality test, and VAR model on the above analyses, the daily data on closing price index data are used over a period from January 2002 to December 2016, and decomposed the sample period into three sub-samples. The correlations in conditional variances are investigated and these are measured by the squared of the standardized residuals from the estimated GARCH model. This paper has identified the common factors that correlation in conditional variances among the emerging markets are positive, but the only exception is the Bangladesh market which stands alone with the lowest correlation. Most noteworthy results that correlation increases inter Asian emerging markets in the global crisis and post-crisis periods. It indicates that financial crisis 2007∼2008 make more cointegrated to Asian emerging stock markets.

114

Table 6(a) Asian emerging markets return spillovers


Source: Author’s calculations. Sample 2002∼2016

Lag Bangladesh China India Malaysia Philippine South Korea A. Percentage of conditional volatility of Bangladesh stock returns explained by returns of 1 100.00 0.00 0.00 0.00 0.00 0.00 5 99.70 0.08 0.11 0.08 0.03 0.00 10 98.71 0.08 1.10 0.07 0.04 0.00 20 98.49 0.28 1.12 0.07 0.04 0.00 40 97.00 0.72 2.17 0.07 0.03 0.01 60 96.74 1.04 2.12 0.06 0.02 0.02 B. Percentage of conditional volatility of China stock returns explained by returns of 1 0.00 100.00 0.00 0.00 0.00 0.00 5 0.00 98.39 0.53 1.00 0.05 0.02 10 0.00 98.09 0.54 1.30 0.05 0.02 20 0.00 97.20 1.03 1.69 0.05 0.03 40 0.00 97.00 1.13 1.79 0.04 0.04 60 0.00 96.15 1.31 2.41 0.04 0.09 C. Percentage of conditional volatility of India stock returns explained by returns of 1 0.05 3.64 96.31 0.00 0.00 0.00 5 0.13 3.66 95.21 0.52 0.13 0.35 10 0.12 3.67 95.05 0.63 0.18 0.35 20 0.10 3.70 94.70 0.72 0.43 0.35 40 0.08 3.80 94.31 0.83 0.53 0.45 60 0.11 3.84 94.65 0.52 0.33 0.55 D. Percentage of conditional volatility of Malaysia stock returns explained by returns of 1 0.05 4.43 9.19 86.33 0.00 0.00 5 0.07 4.44 11.68 83.60 0.13 0.07 10 0.07 4.44 11.68 83.60 0.13 0.07 20 0.07 4.44 11.68 83.60 0.13 0.07 40 0.07 4.44 11.68 83.60 0.13 0.07 60 0.07 4.44 11.68 83.60 0.13 0.07 E. Percentage of conditional volatility of the Philippine stock returns explained by returns of 1 0.02 2.32 4.99 9.45 83.22 0.00 5 0.05 2.68 9.87 9.42 77.75 0.22 10 0.05 2.68 9.87 9.42 77.75 0.22 20 0.05 2.68 9.87 9.42 77.75 0.22 40 0.05 2.68 9.87 9.42 77.75 0.22 60 0.05 2.68 9.87 9.42 77.75 0.22 F. Percentage of conditional volatility of South Korea stock returns explained by returns of 1 0.01 4.65 13.34 8.69 2.28 71.03 5 0.07 4.56 15.08 8.57 2.32 69.40 10 0.07 4.56 15.08 8.57 2.32 69.40 20 0.07 4.56 15.08 8.57 2.32 69.40 40 0.07 4.56 15.08 8.57 2.32 69.40 60 0.07 4.56 15.08 8.57 2.32 69.40


Table 6(b) Asian emerging markets volatility spillovers

115

Source: Author’s calculations. Sample 2002∼2016

Lag Bangladesh China India Malaysia Philippine South Korea A. Percentage of conditional volatility of Bangladesh stock returns explained by conditional volatilities of returns of 1 100.00 0.00 0.00 0.00 0.00 0.00 5 99.97 0.01 0.01 0.00 0.02 0.00 10 99.39 0.03 0.53 0.03 0.01 0.00 20 98.74 0.12 1.06 0.06 0.02 0.00 40 98.09 0.24 1.57 0.07 0.02 0.00 60 98.01 0.30 1.60 0.08 0.01 0.00 B. Percentage of conditional volatility of China stock returns explained by conditional volatilities of returns of 1 0.00 100.00 0.00 0.00 0.00 0.00 5 0.02 99.49 0.37 0.09 0.04 0.00 10 0.02 98.05 0.55 1.32 0.05 0.01 20 0.02 96.19 0.79 2.90 0.07 0.03 40 0.02 95.00 1.08 3.72 0.14 0.04 60 0.02 93.40 2.22 4.13 0.19 0.04 C. Percentage of conditional volatility of India stock returns explained by conditional volatilities of returns of 1 0.00 0.09 99.91 0.00 0.00 0.00 5 0.00 0.44 98.09 0.35 0.81 0.30 10 0.00 0.79 95.39 1.68 1.49 0.66 20 0.01 1.57 89.36 4.60 2.91 1.55 40 0.01 3.13 81.47 7.30 4.96 3.13 60 0.01 4.29 78.07 7.90 5.84 3.90 D. Percentage of conditional volatility of Malaysia stock returns explained by conditional volatilities of returns of 1 0.00 1.37 0.68 97.96 0.00 0.00 5 0.00 1.73 1.32 96.56 0.30 0.08 10 0.02 2.44 1.64 94.46 0.95 0.50 20 0.03 3.89 2.23 89.65 2.46 1.73 40 0.04 6.14 3.22 82.81 4.34 3.46 60 0.04 7.47 3.76 79.71 4.98 4.04 E. Percentage of conditional volatility of the Philippine stock returns explained by conditional volatilities of returns of 1 0.00 1.71 0.09 4.36 93.84 0.00 5 0.00 3.92 4.44 8.05 81.71 1.88 10 0.01 4.49 5.44 8.46 79.18 2.42 20 0.01 5.23 6.48 8.74 76.39 3.15 40 0.02 6.18 7.38 8.94 73.56 3.91 60 0.02 6.75 7.68 9.01 72.36 4.18 F. Percentage of conditional volatility of South Korea stock returns explained by conditional volatilities of returns of 1 0.01 0.04 1.24 1.60 0.39 96.72 5 0.01 0.11 3.80 1.35 1.91 92.82 10 0.01 0.43 6.33 1.58 4.17 87.47 20 0.01 1.39 10.36 2.23 8.17 77.84 40 0.01 3.45 14.30 3.45 12.21 66.58 60 0.01 5.08 15.52 4.17 13.46 61.77

Furthermore, the results pairwise granger causality test ascertain that Bangladeshi stock

116


market does not granger cause Asian emerging markets and vice versa during the sample period, while there is exception in Indian markets a bidirectional causality from Indian to Bangladeshi markets during the pre-crisis and post-crisis periods. Among of the others emerging markets have significant casual effect running either bidirectional or unidirectional. Specially, the crisis and post-crisis periods are establishing the dynamic linkages of Asian emerging stock markets stronger. The Asian emerging markets are affected by their own past shocks. Particularly, these are more focused in Bangladeshi stock markets results as compared to the other Asian emerging markets. The negative shock of the regional stock markets affect the six Asian emerging stock markets, particularly in global financial crisis 2007∼2008 period, only exception is found in Bangladeshi stock market. Even though emerging markets are similarly very volatile, particularly it is driven by bad news (e.g. global financial crisis period) from the financial markets and their massive volatility is spread back to the big market capitalization stock markets. This investigation shows that volatility and return spillovers behave very differently over time, during the pre-crisis, crisis, and post crisis periods. The Asian emerging stock markets are highly correlated with each other in terms of the market volatility, especially during the global financial crisis period 2007∼2008. In this analysis, results are found that 2007∼2008, the Chinese, Indian, Malaysian stock markets are hardly affected by the global financial crisis in terms of volatility spillover. Prior to 2007∼2008 global financial crisis, the Chinese, Malaysian, and South Korean stock markets are slightly affected by the regional markets. In general, volatility spillovers among the Bangladeshi market that is more distinct than those among the other Asian emerging markets, which suggests that the links and correlations among Asian emerging countries stock markets have become increasingly in recent years. The Bangladeshi stock markets become more interdependent as captured by the increase in return spillovers in the pre-crisis periods 2002∼2006. This result shows that the Bangladeshi, Indian and Chinese stock markets are not deeply affected by the global financial crisis in terms of return spillover. The consequences of this study show that while the Asian emerging stock markets appear to be segmented before the crisis, the correlations increase significantly during the global financial crisis 2007∼2008 periods. Nevertheless, the global financial crises 2007∼2008 periods return spillovers in the Asian emerging territory which also touches the peak level in the history. For this paper studies, six Asian emerging countries stock markets are used. As our focus is on the Asian emerging stock market return and volatility spillover, the macroeconomic markets and other financial markets are not considered which are left for future investigation. Furthermore, researcher can use some other remarkable issues, return and volatility spillovers with high, medium, and low frequency data. Investors, like the foreign institutional investors (FIIs) and domestic institutional investors (DIIs), who are seeking for portfolio diversification, can associate these findings. Basically, FIIs prefer short term returns and they are interested in high degree volatility of stock market. As it is impossible to gain such high returns without causing volatility, FIIs are looking forward to these speculations.


117

Acknowledgment An earlier version of this paper was presented at the 2015 Service Systems Engineering Conference & 2015 IEEE Symposium on Analytics and Risk Conference. Thanks go to some participants who made comments and suggestions for us to improve the paper. Financial support provided by the Chinese Academy of Sciences and The World Academy of Sciences (CAS-TWAS) are gratefully acknowledged.

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