# ASEAN 5 stock markets, currency risk and volatility spillover.

INTRODUCTION

Generally, volatility spillover occurs when changes in price volatility in one market create a lagged impact in other markets. When applied to currencies and stock markets, volatility spillover occurs when changes in foreign currency markets affect stock markets, over and above local effects. As several European and Asian countries consider the benefits of joining the Eurozone and ASEAN, respectively, the impact of volatility transmissions and spillovers raises key financial and policy questions that need to be further studied. From a business perspective, the prevalence of volatility spillovers can guide multinational corporations in managing their currency risk and exposure in these countries, a key element in their international diversification efforts. (Kanas, 2000).

This research investigates the interdependence of stock returns and exchange rate changes in the ASEAN5 countries. The countries included are the Philippines, Singapore, Malaysia, Thailand and Indonesia for the period January 4, 2000 to December 31, 2010. This study will also examine if there are volatility spillovers from stock returns to exchange rate changes present in each country and the ASEAN5.

THEORETICAL AND CONCEPTUAL FRAMEWORK

The Nature of Volatility Transmission and Volatility Spillover

Two approaches provide the possible link between exchange rates to the other economic and financial sectors. The first, so-called "flow model" looks at the impact of exchange rates on the balance of trade, such as those studied by Mundell in 1963 and by Dornbusch and Fisher in 1980. The flow model posits that changes in exchange rates affect international competitiveness and trade balances, thereby influencing real income and output. Stock prices, generally interpreted as the present values of future cash flows of firms, react to exchange rate changes and form the link among future income, interest rate innovations, and current investment and consumption decisions. (Yang and Doong, 2004)

The other model, "stock-oriented" models of exchange rates such as those studied by Branson (1983) and Frankel (1983) models view exchange rates as equating the supply and demand for assets such as stocks and bonds. This approach gives the capital account an important role in determining exchange rate dynamics. Since the values of financial assets are determined by the present values of their future cash flows, expectations of relative currency values play a considerable role in their price movements, especially for internationally held financial assets. Therefore, stock price innovations may affect, or be affected by, exchange rate dynamics. (Ibid, 1984)

An illustration of the second approach can be seen in Figure 1, where transmission and spillover is seen as an input-process-output model:

Because there has been no dominant approach to explain the impact of volatility spillover, numerous studies have populated the literature in recent years. The residual effect of the Global Financial Crisis still being felt in many countries as well as those "integrated" economies such as the Eurozone and ASEAN provide the motivation for sustained interest in this field of study.

LITERATURE REVIEW

Kanas (1998 and 2000) was one of the first to have examined volatility spillovers in the foreign exchange and stock markets. Using EGARCH, he studied the interdependence of stock returns and exchange rate changes among six industrialized countries, namely the United States (US), the United Kingdom (UK), Japan, Germany, France and Canada. The study concluded that there is evidence of volatility spillovers from stock returns to exchange rates changes for all countries except Germany. However, volatility spillovers from exchange rate changes to stock returns are insignificant for all countries.

[FIGURE 1 OMITTED]

Mishra and Rahman (2010) examined the dynamics of stock market returns volatility of India and Japan using the Threshold Generalized Autoregressive Conditional Heteroskedasticity (TGARCH-M) model. They concluded that return volatility persists in both countries. Savva, Osborn, Gill (2009) used the asymmetric Dynamic Conditional Correlations(DCC) version of the VAR-multivariate Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH) model for daily stock market returns across four major world markets, namely New York, London, Frankfurt and Paris. The results showed that the correlation in the post-euro period was highest between Frankfurt and Paris. Also, the presence of spillover effects from foreign markets for both returns and volatilities exists. The results are consistent with Kanas (1998) where he used the EGARCH model to capture potential asymmetric effects of volatility across the three largest European stock markets, namely London, Frankfurt and Paris. The results showed that there were reciprocal spillovers between London and Paris, and between Paris and Frankfurt, and unidirectional spillovers from London to Frankfurt. Yang and Doong (2004) explored the nature of the mean and volatility transmission mechanism between stock and foreign exchange markets for the G-7 countries. The results point to significant volatility spillovers and an asymmetric effect from the stock market to the foreign exchange market for France, Italy, Japan and the US, suggesting integration between stock and foreign exchange markets in these countries. (O' Donnell and Morales, n.d.)

Three other studies examined if there are price and volatility spillovers from the US market to other countries. In the case of Hong Kong, Singapore, Taiwan and Malaysia except Korea, there was a decrease in price and volatility spillovers from the US market since the 1997 Asian financial crisis. The study used the EGARCH model for the prior- and post-crisis periods. Data is the daily stock prices from January 3, 1995 to April 24 2001. (Nam, Yuhn, and Kim, 2008). In the case of Europe and the US, Anaraki, (n.d.) investigated the link by using Granger causality. The causality runs from the US to European stock market and that the US fundamentals including the Federal Fund Rate (FFR), the Euro-dollar exchange rate, and the US stock market indices affect European stock market volatility. Using a multivariate generalized autoregressive conditional heteroskedasticity (GARCH-M) model, Chancharoenchai and Dibooglu (2006) examined volatility spillovers in six Southeast Asian stock markets pre and after the 1997 Asian crisis and its interactions with the U.S. market (using the New York Stock Exchange as the global market), Japan (using the Tokyo Stock Exchange as a regional market). The study concluded that there were some interdependence in volatility between emerging markets and developed markets before and after crisis. The study showed evidence of the Asian contagion which started in Thailand and affected other financial markets.

Most of the literature on the international interactions of stock returns, foreign exchange rate changes and volatility spillovers employ Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models (Bollerslev, 1986). These models determine if there is volatility clustering, fat tails, volatility spillover. Volatility clustering in asset returns means that "large price changes follow large price changes of either sign and small price changes follow small prices changes of either sign." (Mandelbrot , 1963). The GARCH (1,1) is often used since the model is parsimonious. Another GARCH model was developed by Nelson (1991), the Exponential GARCH to study the asymmetrical effects of shocks on stock return volatility, known as leverage effect. The results showed that negative shocks have larger effects on volatility than positive shocks. (Kanas 1998).

METHODOLOGY

We used daily closing stock prices denominated in local currency for the Philippines Stock Exchange Index (denoted in the runs as PSEi) in the case of the Philippines, the FTSE Straits Times Index (denoted as FSSTI Index) for Singapore, the FTSE Bursa Malaysia KLCI Index (denoted as FBMKLCI) for Malaysia, the Stock Exchange of Thailand (denoted as SET) for Thailand, and the Jakarta Composite Index (denoted as JCI Index) for Indonesia for the period from 4 January 2000 to 31 December 2010. The stock indices are not adjusted for dividends since they will not affect the results (Kanas, 2000). For exchange rate, the currencies used were: the Philippine Peso, Indonesian Rupiah, Singapore Dollar, Thai Baht and Malaysian Ringgit. The exchange rate series for each country and indices were all derived from Bloomberg.

Following Kanas (2000), we compute for the stock returns (denoted as ret in the results) and exchange rate changes (denoted by [S.sub.t] and [E.sub.t], respectively) are calculated as the difference between the natural logarithms of the closing values for two consecutive trading days, i.e. [S.sub.t] = ln([P.sup.S.sub.t]) - ln([sup.PS.sub.t-1]), and [E.sub.t] = ln([P.sup.E.sub.t]) - ln ([P.sup.E.sub.t-1]), where [P.sup.S.sub.t] and [P.sup.E.sub.t] are the stock price and the exchange rate at period t, respectively.

GARCH (1,1) specification was used since it is a parsimonious representation of conditional variance of time series data (Bollerslev, 1987), in this case, the stock returns and foreign exchange changes.

RESEARCH FINDINGS: MODEL RESULTS

Descriptive statistics for stock returns of the indices are reported in Table 1. The sample means of returns are positive and statistically different from zero. The variances range from 0.009 (Malaysia) to 0.1525 (Indonesia). The measures for skewness indicate that all countries except the Philippines (PSEi) are negatively skewed and excess kurtosis indicate that the distributions of returns for all markets are leptokurtic.

Descriptive statistics for foreign exchange rate changes are reported in Table 2. The sample means of returns are positive except for Malaysia and Singapore and statistically different from zero. The variances range from 0.0000208 (Thailand) to 0.007 (Indonesia) The measures for skewness indicate that all countries except Thailand are negatively skewed and excess kurtosis indicate that the distributions of the foreign exchange rate changes for all markets are leptokurtic.

Table 3 shows the correlation coefficient for each variable. The PSEi stock return is negatively correlated to the Indonesian and Singaporean stock returns, while positively correlated to Malaysian and Thai stock returns. The Malaysian, Indonesian, Thai, and Singaporean stock returns are positively correlated to each other. The foreign exchange rate change, retpeso (Philippines) is negatively correlated to all countries' stock returns including the retpsei (Philippines). The Indonesian rupiah and Thai baht foreign exchange rate changes are also negatively correlated with their respective stock returns on the indices. Singapore dollar and Malaysia ringgit foreign exchange rate changes are positively correlated with their respective stock returns on the indices. An increase in the foreign exchange in one country is viewed as favorable in the other. This is typical of these two countries as both are active trading and financial partners.

Table 4 shows the results of GARCH (1,1) for retpsei and retpeso. The p-value is significant. Also, volatility spillover exists between the Philippine stock market to the Philippine foreign exchange rate based on the alpha and beta results.

Table 5 shows the results of GARCH (1,1) for retjcindex and retidr. The p-value is not significant. Also, volatility spillover exists between the Indonesian stock market to the Indonesian foreign exchange rate based on the alpha and beta results.

Table 6 shows the results of GARCH (1,1) for retset and retthb. The p-value is not significant. Also, volatility spillover exists between the Thai stock market to the Thai foreign exchange rate based on the alpha and beta results.

Table 7 shows the results of GARCH (1,1) for retfblmklc and retmyr. The p-value is significant. Also, volatility spillover exists between the Malaysian stock market and the Malaysian foreign exchange rate based on the alpha and beta results.

Table 8 shows the results of GARCH (1,1) for retFSSTI Index and retsgd. The p-value is not significant. Also, volatility spillover exists between the Singaporean stock market and the Singaporean foreign exchange rate based on the alpha and beta results.

Tables 9, 10, and 11 show that volatility spillovers from exchange rate changes to stock returns are significant for Singapore, Indonesia and the Philippines. However, software runs for Malaysia and Thailand resulted into error despite several attempts. This implies that the convergence criterion was not present.

Tables 12, 13, and 14 show that the stock returns for the Philippine, Indonesian and Singaporean markets have persistent volatility and that there are spillovers to the other stock markets and foreign exchange rates. This means that the ASEAN5 stock market and foreign exchange markets are integrated and that any news on these markets will affect the other countries' markets.

The study included a GARCH (1,1) specification for both the stock returns on indices and foreign exchange changes to cover a subperiod starting June 1, 2007. On June 2007, Bear Sterns were forced to sell assets after their hedge funds with large holdings of subprime mortgages suffered large losses(Guillen, n.d). We will use this date as a subperiod to determine if volatility persists during the start of a global financial crisis and if there are evidences of volatility spillovers until the end of December 2010.

Tables 15, 16, 17, 18, 19 show that the p-value of the retmyr is significant in all stock returns on indices except for Malaysia. The retmyr has a positive relationship with both the Singaporean and Thai stock returns on indices. The retmyr has a negative relationship with both the Philippine and Indonesian stock returns on indices.

Table 16 shows that the p-value of retsgd is significant and has a positive relationship with retjci. Table 17 and 18 show that the p-value of retpeso is significant and has a negative relationship with retset and retfbmklci_inde, respectively. Table 19 shows that the p-value of retthb is significant and has a negative relationship with retfssti.

Tables 15, 16, 17,18, and 19 show that volatility is persistent and that there are spillovers to the other stock markets and foreign exchange rates. The alpha and beta values are higher during this subperiod of the financial crisis which indicates that the market takes time to absorb the impact of information and volatility persists.

Tables 20, 21, 22, 23, and 24 show that volatility is persistent in the foreign exchange changes in all countries. Table 20 shows that the p-values of retthb and retfbmklci_inde are significant and are both negatively related with retpeso. Table 21 shows that retmyr, retfssti have significant p-values and are both positively related with retidr. Table 22 shows that retsgd, retpeso, retset have significant p-values. Both retpeso and retset have negative relationship with retthb while retsgd is positively related. Based on Table 22,it is only the Thai stock returns (retset) and Thai foreign exchange changes that showed evidence of volatility spillover in the same country and is negatively related. Table 23 shows that retsgd, retidr have significant p-values and positively related with retmyr. Table 24 shows retjci, retthb, retmyr have significant p-values and are all positively related with retsgd. Based on Tables 20, 21, 22, 23,and 24, we find evidence of volatility spillover from foreign exchange markets to stock markets other than its own country.

Our findings can be summarized as follows:

1). There is presence of volatility clustering in the stock markets and foreign exchange markets as evidenced by the (significant) high alpha (1) value This means volatility in the previous period will have an impact on the volatility of the current period returns.

2). The high beta (1) value in all models mean that volatility is quick to react to movements in the stock market and foreign exchange market and volatility tend to be spikier.

3). There is also evidence of spillovers from stock returns to exchange rate changes for all countries. This implies that there is some form of interaction between the stock and foreign exchange markets within the ASEAN5 countries.

4). Volatility spillovers from exchange rate changes to stock returns are also significant for all countries.

5). There is more volatility during the sub-period June 1, 2007-December 31, 2010 based on the higher alpha and beta results for all stock market and foreign exchange markets as compared for the whole period January 4, 2000 to December 31, 2010.

6). Since the ASEAN5 countries have moved towards increasing interdependence with each other, any news affecting either the stock market or foreign exchange market of one country would have a volatility spillover effect in that country and spread in the region.

Of particular interest in the findings is the significance of the results for the Philippine peso and the PSEi. This implies the presence of numerous foreign investors in the Philippine stock market and any instance where volatility exists, usually adverse, allows the opportunity for these foreign investors to pull out almost immediately, affecting foreign currency levels. This also affirms the impact of so-called "hot money" as one of the main drivers of prices in the Philippine stock market. This is not as strong or even present in the other ASEAN5 countries.

CONCLUSION AND AREAS FOR FURTHER STUDY

This study provides evidence of volatility spillover within and among the ASEAN5 countries. This study also affirms the applicability of GARCH to determine the levels of transmission and spillover among the countries. The study had two periods, from January 4, 2000-December 31, 2010 and a sub-period June 1, 2007 to December 31, 2010 to capture the volatility during the global financial crisis. However, it would be interesting to find out if there are periods where spillovers as not significant. It would also be interesting if the spillovers were present before and after the Global Financial Crisis of 2008, or even during the Asian Financial Crisis of 1997. Dividing the study into sub periods would reveal more details that are otherwise not captured in this study. The EGARCH model can also be used to capture leverage of asymmetric effects for future studies.

REFERENCES

Anaraki, N. (n. d.). The European Stock Market Impulse to the US Financial Crisis. Retrieved March 22, 2011 from http://www.pdfdownload.org/pdf2html/pdf2html.php?url= http%3A%2F%2Fwww.aabri.com%2FOC09man uscripts%2FOC09012.pdf&images=yes

Awayan, N., Guerra, F., Quipones, M., and Tang Woo, S. (2010). A Study on the Volatility Transmission in the Exchange Rates of the Currencies of the Philippines, South Korea, and Indonesia and the Impacts (sic) of the Global and Asian Financial Crises for the Period 1990-2009. Unpublished Manuscript, De La Salle University

Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics 31, 307-327

Branson, W. H. (1983). Macroeconomic Determinants of Real Exchange Risk. Managing Foreign Exchange Risk. R. J. Herring ed., Cambridge: Cambridge University Press.

Dornbusch, R. and S. Fischer, (1980). Exchange Rates and the Current Account. American Economic Review, 70(5), 960-971.

Frankel, J. A., (1983). Monetary and Portfolio-Balance Models of Exchange Rate Determination. Economic Interdependence and Flexible Exchange Rates. J. S. Bhandari and B. H. Putnam eds., Cambridge: MIT Press.

Guillen, M. (n.d). The The Global Economic & Financial Crisis: A Timeline. Retrieved on April 27, 2011 from http://lauder.wharton.upenn.edu/pdf/Chronology%20Economic%20%20Financial% 20Crisis.pdf

Kanas, A. (1998). Volatility Spillovers Across Equity Markets: European Evidence. Applied Financial Economics. Taylor and Francis Journals, 8(3), 245-56.

Kanas, A. (2000). Volatility Spillovers between Stock Returns and Exchange Rate Changes: International Evidence. Journal of Business Finance and Accounting,. 27, 447-467.

Chancharoenchai, K.. and Dibooglu, S. (2006). Volatility Spillovers and Contagion During the Asian Crisis: Evidence from Six Southeast Asian Stock Markets. Emerging Markets Finance and Trade, March-April 42(2), 4-17.

Mandelbrot, B. (1963). The variation of certain speculative prices. Journal of Business, 36,.394-419.

Mishra, B. and Rahman, M. (2010). Dynamics of Stock Market Return Volatility: Evidence from the Daily Data of India and Japan. The International Business & Economics Research Journal, May, 9(5), 79-84.

Mundell, R., (1963). Capital Mobility and Stabilization Policy under Fixed and Flexible Exchange Rate. Canadian Journal of Economics and Political Science, 29, 475-467.

Nam, J. H., Yuhn, K., and Kim, S. B. (2008). What happened to Pacific-Basin emerging markets after the 1997 financial crisis? Applied Financial Economics. 18,639-658.

Nelson, D. B. (1991). Conditional Heteroskedasticity in Asset Returns: A New Approach. Econometrica, 59, 347370.

O' Donnell, M. and Morales. L. (n.d.) Volatility Spillovers Between Stock Returns and Foreign Exchange Rates: Evidence from Four Eastern European Countries. Retrieved on March 11, 2011 from http://www.fma.org/Prague/Papers/EECVOLATILITY.MoralesnOnnDonnell.pdf

Savva, C.S., Osborn, D.R., and Gill, L. (2009). Spillovers and Correlations between US and Major European Stock Markets: The Role of the Euro. Applied Financial Economics, 19, 1595-1604.

Yang, S. and Doong, S. (2004). Price and Volatility Spillovers between Stock Prices and Exchange Rates: Empirical Evidence from the G-7 Countries. International Journal of Business and Economics, 3(2), 139-153.

Leila C. Kabigting, University of Guam

Rene B. Hapitan, De La Salle University

Generally, volatility spillover occurs when changes in price volatility in one market create a lagged impact in other markets. When applied to currencies and stock markets, volatility spillover occurs when changes in foreign currency markets affect stock markets, over and above local effects. As several European and Asian countries consider the benefits of joining the Eurozone and ASEAN, respectively, the impact of volatility transmissions and spillovers raises key financial and policy questions that need to be further studied. From a business perspective, the prevalence of volatility spillovers can guide multinational corporations in managing their currency risk and exposure in these countries, a key element in their international diversification efforts. (Kanas, 2000).

This research investigates the interdependence of stock returns and exchange rate changes in the ASEAN5 countries. The countries included are the Philippines, Singapore, Malaysia, Thailand and Indonesia for the period January 4, 2000 to December 31, 2010. This study will also examine if there are volatility spillovers from stock returns to exchange rate changes present in each country and the ASEAN5.

THEORETICAL AND CONCEPTUAL FRAMEWORK

The Nature of Volatility Transmission and Volatility Spillover

Two approaches provide the possible link between exchange rates to the other economic and financial sectors. The first, so-called "flow model" looks at the impact of exchange rates on the balance of trade, such as those studied by Mundell in 1963 and by Dornbusch and Fisher in 1980. The flow model posits that changes in exchange rates affect international competitiveness and trade balances, thereby influencing real income and output. Stock prices, generally interpreted as the present values of future cash flows of firms, react to exchange rate changes and form the link among future income, interest rate innovations, and current investment and consumption decisions. (Yang and Doong, 2004)

The other model, "stock-oriented" models of exchange rates such as those studied by Branson (1983) and Frankel (1983) models view exchange rates as equating the supply and demand for assets such as stocks and bonds. This approach gives the capital account an important role in determining exchange rate dynamics. Since the values of financial assets are determined by the present values of their future cash flows, expectations of relative currency values play a considerable role in their price movements, especially for internationally held financial assets. Therefore, stock price innovations may affect, or be affected by, exchange rate dynamics. (Ibid, 1984)

An illustration of the second approach can be seen in Figure 1, where transmission and spillover is seen as an input-process-output model:

Because there has been no dominant approach to explain the impact of volatility spillover, numerous studies have populated the literature in recent years. The residual effect of the Global Financial Crisis still being felt in many countries as well as those "integrated" economies such as the Eurozone and ASEAN provide the motivation for sustained interest in this field of study.

LITERATURE REVIEW

Kanas (1998 and 2000) was one of the first to have examined volatility spillovers in the foreign exchange and stock markets. Using EGARCH, he studied the interdependence of stock returns and exchange rate changes among six industrialized countries, namely the United States (US), the United Kingdom (UK), Japan, Germany, France and Canada. The study concluded that there is evidence of volatility spillovers from stock returns to exchange rates changes for all countries except Germany. However, volatility spillovers from exchange rate changes to stock returns are insignificant for all countries.

[FIGURE 1 OMITTED]

Mishra and Rahman (2010) examined the dynamics of stock market returns volatility of India and Japan using the Threshold Generalized Autoregressive Conditional Heteroskedasticity (TGARCH-M) model. They concluded that return volatility persists in both countries. Savva, Osborn, Gill (2009) used the asymmetric Dynamic Conditional Correlations(DCC) version of the VAR-multivariate Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH) model for daily stock market returns across four major world markets, namely New York, London, Frankfurt and Paris. The results showed that the correlation in the post-euro period was highest between Frankfurt and Paris. Also, the presence of spillover effects from foreign markets for both returns and volatilities exists. The results are consistent with Kanas (1998) where he used the EGARCH model to capture potential asymmetric effects of volatility across the three largest European stock markets, namely London, Frankfurt and Paris. The results showed that there were reciprocal spillovers between London and Paris, and between Paris and Frankfurt, and unidirectional spillovers from London to Frankfurt. Yang and Doong (2004) explored the nature of the mean and volatility transmission mechanism between stock and foreign exchange markets for the G-7 countries. The results point to significant volatility spillovers and an asymmetric effect from the stock market to the foreign exchange market for France, Italy, Japan and the US, suggesting integration between stock and foreign exchange markets in these countries. (O' Donnell and Morales, n.d.)

Three other studies examined if there are price and volatility spillovers from the US market to other countries. In the case of Hong Kong, Singapore, Taiwan and Malaysia except Korea, there was a decrease in price and volatility spillovers from the US market since the 1997 Asian financial crisis. The study used the EGARCH model for the prior- and post-crisis periods. Data is the daily stock prices from January 3, 1995 to April 24 2001. (Nam, Yuhn, and Kim, 2008). In the case of Europe and the US, Anaraki, (n.d.) investigated the link by using Granger causality. The causality runs from the US to European stock market and that the US fundamentals including the Federal Fund Rate (FFR), the Euro-dollar exchange rate, and the US stock market indices affect European stock market volatility. Using a multivariate generalized autoregressive conditional heteroskedasticity (GARCH-M) model, Chancharoenchai and Dibooglu (2006) examined volatility spillovers in six Southeast Asian stock markets pre and after the 1997 Asian crisis and its interactions with the U.S. market (using the New York Stock Exchange as the global market), Japan (using the Tokyo Stock Exchange as a regional market). The study concluded that there were some interdependence in volatility between emerging markets and developed markets before and after crisis. The study showed evidence of the Asian contagion which started in Thailand and affected other financial markets.

Most of the literature on the international interactions of stock returns, foreign exchange rate changes and volatility spillovers employ Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models (Bollerslev, 1986). These models determine if there is volatility clustering, fat tails, volatility spillover. Volatility clustering in asset returns means that "large price changes follow large price changes of either sign and small price changes follow small prices changes of either sign." (Mandelbrot , 1963). The GARCH (1,1) is often used since the model is parsimonious. Another GARCH model was developed by Nelson (1991), the Exponential GARCH to study the asymmetrical effects of shocks on stock return volatility, known as leverage effect. The results showed that negative shocks have larger effects on volatility than positive shocks. (Kanas 1998).

METHODOLOGY

We used daily closing stock prices denominated in local currency for the Philippines Stock Exchange Index (denoted in the runs as PSEi) in the case of the Philippines, the FTSE Straits Times Index (denoted as FSSTI Index) for Singapore, the FTSE Bursa Malaysia KLCI Index (denoted as FBMKLCI) for Malaysia, the Stock Exchange of Thailand (denoted as SET) for Thailand, and the Jakarta Composite Index (denoted as JCI Index) for Indonesia for the period from 4 January 2000 to 31 December 2010. The stock indices are not adjusted for dividends since they will not affect the results (Kanas, 2000). For exchange rate, the currencies used were: the Philippine Peso, Indonesian Rupiah, Singapore Dollar, Thai Baht and Malaysian Ringgit. The exchange rate series for each country and indices were all derived from Bloomberg.

Following Kanas (2000), we compute for the stock returns (denoted as ret in the results) and exchange rate changes (denoted by [S.sub.t] and [E.sub.t], respectively) are calculated as the difference between the natural logarithms of the closing values for two consecutive trading days, i.e. [S.sub.t] = ln([P.sup.S.sub.t]) - ln([sup.PS.sub.t-1]), and [E.sub.t] = ln([P.sup.E.sub.t]) - ln ([P.sup.E.sub.t-1]), where [P.sup.S.sub.t] and [P.sup.E.sub.t] are the stock price and the exchange rate at period t, respectively.

GARCH (1,1) specification was used since it is a parsimonious representation of conditional variance of time series data (Bollerslev, 1987), in this case, the stock returns and foreign exchange changes.

RESEARCH FINDINGS: MODEL RESULTS

Descriptive statistics for stock returns of the indices are reported in Table 1. The sample means of returns are positive and statistically different from zero. The variances range from 0.009 (Malaysia) to 0.1525 (Indonesia). The measures for skewness indicate that all countries except the Philippines (PSEi) are negatively skewed and excess kurtosis indicate that the distributions of returns for all markets are leptokurtic.

Descriptive statistics for foreign exchange rate changes are reported in Table 2. The sample means of returns are positive except for Malaysia and Singapore and statistically different from zero. The variances range from 0.0000208 (Thailand) to 0.007 (Indonesia) The measures for skewness indicate that all countries except Thailand are negatively skewed and excess kurtosis indicate that the distributions of the foreign exchange rate changes for all markets are leptokurtic.

Table 3 shows the correlation coefficient for each variable. The PSEi stock return is negatively correlated to the Indonesian and Singaporean stock returns, while positively correlated to Malaysian and Thai stock returns. The Malaysian, Indonesian, Thai, and Singaporean stock returns are positively correlated to each other. The foreign exchange rate change, retpeso (Philippines) is negatively correlated to all countries' stock returns including the retpsei (Philippines). The Indonesian rupiah and Thai baht foreign exchange rate changes are also negatively correlated with their respective stock returns on the indices. Singapore dollar and Malaysia ringgit foreign exchange rate changes are positively correlated with their respective stock returns on the indices. An increase in the foreign exchange in one country is viewed as favorable in the other. This is typical of these two countries as both are active trading and financial partners.

Table 4 shows the results of GARCH (1,1) for retpsei and retpeso. The p-value is significant. Also, volatility spillover exists between the Philippine stock market to the Philippine foreign exchange rate based on the alpha and beta results.

Table 5 shows the results of GARCH (1,1) for retjcindex and retidr. The p-value is not significant. Also, volatility spillover exists between the Indonesian stock market to the Indonesian foreign exchange rate based on the alpha and beta results.

Table 6 shows the results of GARCH (1,1) for retset and retthb. The p-value is not significant. Also, volatility spillover exists between the Thai stock market to the Thai foreign exchange rate based on the alpha and beta results.

Table 7 shows the results of GARCH (1,1) for retfblmklc and retmyr. The p-value is significant. Also, volatility spillover exists between the Malaysian stock market and the Malaysian foreign exchange rate based on the alpha and beta results.

Table 8 shows the results of GARCH (1,1) for retFSSTI Index and retsgd. The p-value is not significant. Also, volatility spillover exists between the Singaporean stock market and the Singaporean foreign exchange rate based on the alpha and beta results.

Tables 9, 10, and 11 show that volatility spillovers from exchange rate changes to stock returns are significant for Singapore, Indonesia and the Philippines. However, software runs for Malaysia and Thailand resulted into error despite several attempts. This implies that the convergence criterion was not present.

Tables 12, 13, and 14 show that the stock returns for the Philippine, Indonesian and Singaporean markets have persistent volatility and that there are spillovers to the other stock markets and foreign exchange rates. This means that the ASEAN5 stock market and foreign exchange markets are integrated and that any news on these markets will affect the other countries' markets.

The study included a GARCH (1,1) specification for both the stock returns on indices and foreign exchange changes to cover a subperiod starting June 1, 2007. On June 2007, Bear Sterns were forced to sell assets after their hedge funds with large holdings of subprime mortgages suffered large losses(Guillen, n.d). We will use this date as a subperiod to determine if volatility persists during the start of a global financial crisis and if there are evidences of volatility spillovers until the end of December 2010.

Tables 15, 16, 17, 18, 19 show that the p-value of the retmyr is significant in all stock returns on indices except for Malaysia. The retmyr has a positive relationship with both the Singaporean and Thai stock returns on indices. The retmyr has a negative relationship with both the Philippine and Indonesian stock returns on indices.

Table 16 shows that the p-value of retsgd is significant and has a positive relationship with retjci. Table 17 and 18 show that the p-value of retpeso is significant and has a negative relationship with retset and retfbmklci_inde, respectively. Table 19 shows that the p-value of retthb is significant and has a negative relationship with retfssti.

Tables 15, 16, 17,18, and 19 show that volatility is persistent and that there are spillovers to the other stock markets and foreign exchange rates. The alpha and beta values are higher during this subperiod of the financial crisis which indicates that the market takes time to absorb the impact of information and volatility persists.

Tables 20, 21, 22, 23, and 24 show that volatility is persistent in the foreign exchange changes in all countries. Table 20 shows that the p-values of retthb and retfbmklci_inde are significant and are both negatively related with retpeso. Table 21 shows that retmyr, retfssti have significant p-values and are both positively related with retidr. Table 22 shows that retsgd, retpeso, retset have significant p-values. Both retpeso and retset have negative relationship with retthb while retsgd is positively related. Based on Table 22,it is only the Thai stock returns (retset) and Thai foreign exchange changes that showed evidence of volatility spillover in the same country and is negatively related. Table 23 shows that retsgd, retidr have significant p-values and positively related with retmyr. Table 24 shows retjci, retthb, retmyr have significant p-values and are all positively related with retsgd. Based on Tables 20, 21, 22, 23,and 24, we find evidence of volatility spillover from foreign exchange markets to stock markets other than its own country.

Our findings can be summarized as follows:

1). There is presence of volatility clustering in the stock markets and foreign exchange markets as evidenced by the (significant) high alpha (1) value This means volatility in the previous period will have an impact on the volatility of the current period returns.

2). The high beta (1) value in all models mean that volatility is quick to react to movements in the stock market and foreign exchange market and volatility tend to be spikier.

3). There is also evidence of spillovers from stock returns to exchange rate changes for all countries. This implies that there is some form of interaction between the stock and foreign exchange markets within the ASEAN5 countries.

4). Volatility spillovers from exchange rate changes to stock returns are also significant for all countries.

5). There is more volatility during the sub-period June 1, 2007-December 31, 2010 based on the higher alpha and beta results for all stock market and foreign exchange markets as compared for the whole period January 4, 2000 to December 31, 2010.

6). Since the ASEAN5 countries have moved towards increasing interdependence with each other, any news affecting either the stock market or foreign exchange market of one country would have a volatility spillover effect in that country and spread in the region.

Of particular interest in the findings is the significance of the results for the Philippine peso and the PSEi. This implies the presence of numerous foreign investors in the Philippine stock market and any instance where volatility exists, usually adverse, allows the opportunity for these foreign investors to pull out almost immediately, affecting foreign currency levels. This also affirms the impact of so-called "hot money" as one of the main drivers of prices in the Philippine stock market. This is not as strong or even present in the other ASEAN5 countries.

CONCLUSION AND AREAS FOR FURTHER STUDY

This study provides evidence of volatility spillover within and among the ASEAN5 countries. This study also affirms the applicability of GARCH to determine the levels of transmission and spillover among the countries. The study had two periods, from January 4, 2000-December 31, 2010 and a sub-period June 1, 2007 to December 31, 2010 to capture the volatility during the global financial crisis. However, it would be interesting to find out if there are periods where spillovers as not significant. It would also be interesting if the spillovers were present before and after the Global Financial Crisis of 2008, or even during the Asian Financial Crisis of 1997. Dividing the study into sub periods would reveal more details that are otherwise not captured in this study. The EGARCH model can also be used to capture leverage of asymmetric effects for future studies.

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Leila C. Kabigting, University of Guam

Rene B. Hapitan, De La Salle University

Table 1: Summary Statistics, Stock Returns using the observations 2000/01/04-2010/12/30 (missing values were skipped) Variable Mean Median Minimum Maximum retpsei 0.000246919 0.000107464 -0.130887 0.161776 retjciindex 0.000624071 0.00127921 -0.109540 0.0762312 retset 0.000270504 0.000404790 -0.160633 0.105770 retfbmklci 0.000221836 0.000462797 -0.0997851 0.0450273 retfSSTI Index 8.12490e-005 0.000449251 -0.0869598 0.0753053 Variable Std. Dev. C.V. Skewness Ex. kurtosis retpsei 0.0143023 57.9230 0.500516 15.6014 retjciindex 0.0152522 24.4399 -0.627927 5.67023 retset 0.0151458 55.9912 -0.756638 8.99236 retfbmklci 0.00940011 42.3741 -0.863193 9.14827 retfSSTI Index 0.0132466 163.037 -0.241634 4.36202 Table 2: Summary Statistics, FOREIGN EXCHANGE, using the observations 2000/01/04-2010/12/30 (missing values were skipped) Variable Mean Median Minimum Maximum retpeso 3.60732e-005 0.000000 -0.142778 0.0371944 retidr 8.16137e-005 0.000000 -0.0897804 0.0590335 retthb 3.63517e-005 2.62916e-005 2.46655e-005 8.21164e-005 retmyr -7.52771e-005 0.000000 -0.0231778 0.0175549 retsgd -8.88746e-005 -0.000115380 -0.0203807 0.0157647 Variable Std. Dev. C.V. Skewness Ex. kurtosis retpeso 0.00488392 135.389 -9.52797 280.322 retidr 0.00735742 90.1493 -0.395801 17.1222 retthb 2.08127e-005 0.572537 1.50691 0.293686 retmyr 0.00264459 35.1314 -0.345536 11.0646 retsgd 0.00311705 35.0724 -0.179246 3.74293 Table 3: Correlation coefficients, using the observations 2000/01/04-2010/12/30 (missing values were skipped) 5% critical value (two-tailed) = 0.0366 for n = 2868 retpsei retjciindex retset retfbmklci retfSSTI Index 1.0000 -0.0119 0.0099 0.0092 -0.0059 retpsei 1.0000 0.0270 0.0323 0.0036 retjciindex 1.0000 0.0184 0.0002 retset 1.0000 0.0538 retfbmklci 1.0000 retfSSTI Index retpeso retidr retthb Retmyr retsgd -0.0015 0.0127 -0.0105 0.0278 0.0134 retpsei -0.0468 -0.0093 0.0054 -0.0209 -0.0371 retjciindex -0.0031 0.0010 -0.0068 0.0012 -0.0121 retset -0.0254 -0.0012 -0.0062 0.0274 0.0227 retfbmklci -0.0261 -0.0168 0.0360 -0.0163 0.0441 retfSSTI Index 1.0000 0.0613 0.0002 0.0169 0.0055 retpeso 1.0000 0.0230 0.0192 0.1268 retidr 1.0000 0.0304 0.0315 retthb 1.0000 0.0452 retmyr 1.0000 retsgd TABLE 4: Model 1 ARCH, using observations 2000/01/05-2010/05/11 (T = 2700) Dependent variable: retpsei Standard errors based on Hessian Coefficient Std. Error const 0.000409585 0.00023327 retpeso -0.42622 0.0382203 alpha(0) 1.46418e-05 2.48929e-06 alpha(1) 0.199983 0.0211614 beta(1) 0.746274 0.023964 Mean dependent var 0.000235 Log-likelihood 7870.915 Schwarz criterion -15694.42 z p-value const 1.7558 0.07912 * retpeso -11.1517 <0.00001 *** alpha(0) 5.8819 <0.00001 *** alpha(1) 9.4504 <0.00001 *** beta(1) 31.1415 <0.00001 *** Mean dependent var S.D. dependent var 0.014317 Log-likelihood Akaike criterion -15729.83 Schwarz criterion Hannan-Quinn -15717.03 Unconditional error variance = 0.00027244 Table 5: Model 2: GARCH, using observations 2000/01/04-2010/03/26 (T = 2669) Dependent variable: retjciindex Standard errors based on Hessian Coefficient Std. Error const 0.00132724 0.000251939 retidr -0.049531 0.0331003 alpha(0) 1.4793e-05 2.71361e-06 alpha(1) 0.141121 0.017445 beta(1) 0.795718 0.0239791 Mean dependent var 0.000624 Log-likelihood 7634.732 Schwarz criterion -15222.13 z p-value const 5.2681 <0.00001 *** retidr -1.4964 0.13455 alpha(0) 5.4514 <0.00001 *** alpha(1) 8.0895 <0.00001 *** beta(1) 33.1839 <0.00001 *** Mean dependent var S.D. dependent var 0.015252 Log-likelihood Akaike criterion -15257.46 Schwarz criterion Hannan-Quinn -15244.68 Unconditional error variance = 0.000234213 Table 6: Model 3: GARCH, using observations 2000/01/04-2010/04/29 (T = 2693) Dependent variable: retset Standard errors based on Hessian Coefficient Std. Error const 0.000761275 0.000506557 retthb 4.06065 11.9368 alpha(0) 2.03909e-05 3.41554e-06 alpha(1) 0.120092 0.0165926 beta(1) 0.786669 0.0274207 Mean dependent var 0.000271 Log-likelihood 7685.133 Schwarz criterion -15322.87 z p-value const 1.5028 0.13288 retthb 0.3402 0.73372 alpha(0) 5.9700 <0.00001 *** alpha(1) 7.2377 <0.00001 *** beta(1) 28.6888 <0.00001 *** Mean dependent var S.D. dependent var 0.015146 Log-likelihood Akaike criterion -15358.27 Schwarz criterion Hannan-Quinn -15345.47 Unconditional error variance = 0.000218695 Table 7: Model 4: GARCH, using observations 2000/01/05-2010/05/24 (T = 2709) Dependent variable: retfbmklci Standard errors based on Hessian Coefficient Std. Error const 0.000577577 0.000134222 retmyr 0.0824977 0.0474972 alpha(0) 1.07521e-06 3.17802e-07 alpha(1) 0.123606 0.0158681 beta(1) 0.874124 0.0153776 Mean dependent var 0.000222 Log-likelihood 9185.873 Schwarz criterion -18324.32 z p-value const 4.3032 0.00002 *** retmyr 1.7369 0.08241 * alpha(0) 3.3833 0.00072 *** alpha(1) 7.7896 <0.00001 *** beta(1) 56.8439 <0.00001 *** Mean dependent var S.D. dependent var 0.009400 Log-likelihood Akaike criterion -18359.75 Schwarz criterion Hannan-Quinn -18346.94 Unconditional error variance = 0.000473662 Table 8: Model 5: GARCH, using observations 2000/01/05-2010/08/04 (T = 2761) Dependent variable: retFSSTI Index Standard errors based on Hessian Coefficient Std. Error const 0.000547155 0.000181741 retsgd 0.0263514 0.0624312 alpha(0) 1.4354e-06 4.16955e-07 alpha(1) 0.0998542 0.0107854 beta(1) 0.895657 0.0102046 Mean dependent var 0.000081 Log-likelihood 8451.841 Schwarz criterion -16856.14 z p-value const 3.0106 0.00261 *** retsgd 0.4221 0.67296 alpha(0) 3.4426 0.00058 *** alpha(1) 9.2582 <0.00001 *** beta(1) 87.7703 <0.00001 *** Mean dependent var S.D. dependent var 0.013247 Log-likelihood Akaike criterion -16891.68 Schwarz criterion Hannan-Quinn -16878.84 Unconditional error variance = 0.000319773 Table 9: Model 6: GARCH, using observations 2000/01/05-2010/05/11 (T = 2700) Dependent variable: retpeso Standard errors based on Hessian Coefficient Std. Error const 8.07153e-05 5.00607e-05 retpsei -0.00129963 0.00390811 alpha(0) 3.47397e-07 7.30592e-08 alpha(1) 0.222078 0.0180842 beta(1) 0.777922 0.0174963 Mean dependent var 0.000032 Log-likelihood 11572.62 Schwarz criterion -23097.83 z p-value const 1.6123 0.10689 retpsei -0.3325 0.73948 alpha(0) 4.7550 <0.00001 *** alpha(1) 12.2802 <0.00001 *** beta(1) 44.4620 <0.00001 *** Mean dependent var S.D. dependent var 0.004881 Log-likelihood Akaike criterion -23133.24 Schwarz criterion Hannan-Quinn -23120.43 Unconditional error variance = 6.16567e+007 Table 10: Model 7: GARCH, using observations 2000/01/04-2010/03/26 (T = 2669) Dependent variable: retidr Standard errors based on Hessian Coefficient Std. Error const 3.43288e-05 9.40919e-05 retjciindex -0.00515879 0.00555879 alpha(0) 3.44955e-06 4.37192e-07 alpha(1) 0.335201 0.0305775 beta(1) 0.664799 0.0261348 Mean dependent var 0.000089 Log-likelihood 9832.963 Schwarz criterion -19618.59 z p-value const 0.3648 0.71523 retjciindex -0.9280 0.35339 alpha(0) 7.8902 <0.00001 *** alpha(1) 10.9623 <0.00001 *** beta(1) 25.4373 <0.00001 *** Mean dependent var S.D. dependent var 0.007566 Log-likelihood Akaike criterion -19653.93 Schwarz criterion Hannan-Quinn -19641.14 Unconditional error variance = 960324 Table 11: Model 8: GARCH, using observations 2000/01/05-2010/08/04 (T = 2761) Dependent variable: retsgd Standard errors based on Hessian Coefficient Std. Error const -0.000105167 5.12557e-05 retfSSTI Index 0.00627846 0.00401432 alpha(0) 1.06892e-07 3.26818e-08 alpha(1) 0.0437789 0.0069778 beta(1) 0.945068 0.00892546 Mean dependent var -0.000073 Log-likelihood 12246.09 Schwarz criterion -24444.63 z p-value const -2.0518 0.04019 ** retfSSTI Index 1.5640 0.11781 alpha(0) 3.2707 0.00107 *** alpha(1) 6.2740 <0.00001 *** beta(1) 105.8844 <0.00001 *** Mean dependent var S.D. dependent var 0.003088 Log-likelihood Akaike criterion -24480.17 Schwarz criterion Hannan-Quinn -24467.34 Unconditional error variance = 9.58388e-006 Table 12: Model 9: GARCH, using observations 2000/01/05-2010/03/26 (T = 2668) Dependent variable: retPSEi Standard errors based on Hessian Coefficient Std. Error Const 0.051502 0.0315025 retfSSTI Index 0.151885 1.3426 retjciindex -0.759426 1.0524 Retset -0.0530194 1.16144 retfbmklci -1.07831 1.8491 Retpeso -30.5301 2.70618 Retidr 1.94724 2.1933 Retthb -549.092 735.818 Retmyr 9.85199 7.1398 Retsgd -2.01639 5.45151 alpha(0) 0.068975 0.011935 alpha(1) 0.202876 0.0214085 beta(1) 0.746174 0.0239392 Mean dependent var 0.017888 Log-likelihood -3541.332 Schwarz criterion 7193.112 z p-value Const 1.6349 0.10208 retfSSTI Index 0.1131 0.90993 retjciindex -0.7216 0.47053 Retset -0.0456 0.96359 retfbmklci -0.5832 0.55979 Retpeso -11.2816 <0.00001 *** Retidr 0.8878 0.37464 Retthb -0.7462 0.45553 Retmyr 1.3799 0.16763 Retsgd -0.3699 0.71147 alpha(0) 5.7792 <0.00001 *** alpha(1) 9.4764 <0.00001 *** beta(1) 31.1695 <0.00001 *** Mean dependent var S.D. dependent var 0.999071 Log-likelihood Akaike criterion 7110.664 Schwarz criterion Hannan-Quinn 7140.498 Unconditional error variance = 1.35378 Table 13: Model 10: GARCH, using observations 2000/01/05-2010/03/26 (T = 2668) Dependent variable: retjciindex Standard errors based on Hessian Coefficient Std. Error const 0.00110156 0.000494091 retfSSTI Index -0.0136058 0.0192688 retset 0.0138238 0.0183159 retfbmklci 0.0432274 0.0286173 retpeso -0.0389996 0.0556795 retidr -0.0404195 0.0337233 retthb 4.9839 11.6034 retmyr -0.158373 0.109879 retsgd -0.130468 0.0840113 retPSEi -0.0222121 0.018222 alpha(0) 1.50416e-05 2.75839e-06 alpha(1) 0.141404 0.0176693 beta(1) 0.793987 0.0243974 Mean dependent var 0.000636 Log-likelihood 7638.708 Schwarz criterion -15166.97 z p-value const 2.2295 0.02578 ** retfSSTI Index -0.7061 0.48012 retset 0.7547 0.45040 retfbmklci 1.5105 0.13091 retpeso -0.7004 0.48366 retidr -1.1986 0.23070 retthb 0.4295 0.66754 retmyr -1.4413 0.14949 retsgd -1.5530 0.12043 retPSEi -1.2190 0.22285 alpha(0) 5.4530 <0.00001 *** alpha(1) 8.0028 <0.00001 *** beta(1) 32.5440 <0.00001 *** Mean dependent var S.D. dependent var 0.015242 Log-likelihood Akaike criterion -15249.42 Schwarz criterion Hannan-Quinn -15219.58 Unconditional error variance = 0.000232811 Table 14: Model 11: GARCH, using observations 2000/01/05-2010/03/26 (T = 2668) Dependent variable: retset Standard errors based on Hessian Coefficient Std. Error const 0.00112611 0.000507964 retfSSTI Index 0.0126462 0.020667 retfbmklci 0.0462272 0.029768 retpeso 0.0686826 0.0636375 retidr -0.020453 0.0368582 retthb -5.33834 11.9532 retmyr 0.193123 0.118983 retsgd 1.95525e-05 0.0877279 retPSEi -0.00648415 0.0189839 retjciindex 0.0229849 0.0197718 alpha(0) 2.04548e-05 3.49307e-06 alpha(1) 0.128036 0.0191421 beta(1) 0.780403 0.0291884 Mean dependent var 0.000289 Log-likelihood 7615.112 Schwarz criterion -15119.78 z p-value const 2.2169 0.02663 ** retfSSTI Index 0.6119 0.54060 retfbmklci 1.5529 0.12044 retpeso 1.0793 0.28046 retidr -0.5549 0.57896 retthb -0.4466 0.65516 retmyr 1.6231 0.10456 retsgd 0.0002 0.99982 retPSEi -0.3416 0.73268 retjciindex 1.1625 0.24503 alpha(0) 5.8558 <0.00001 *** alpha(1) 6.6887 <0.00001 *** beta(1) 26.7367 <0.00001 *** Mean dependent var S.D. dependent var 0.015139 Log-likelihood Akaike criterion -15202.22 Schwarz criterion Hannan-Quinn -15172.39 Unconditional error variance = 0.0002234 Table 15: Model 12: GARCH, using observations 2007/06/01-2010/08/11 (T = 834) Dependent variable: retpsei Standard errors based on Hessian Coefficient Std. Error Const 0.000981244 0.000447434 Retjci -0.0271703 0.0246361 Retset -0.00408886 0.0313633 retfbmklc_inde 0.0731688 0.0638906 Retfssti -0.0104083 0.0293733 Retidr 0.0611328 0.0614346 Retmyr -0.215454 0.111388 Retsgd 0.0682212 0.123933 Retpeso 0.158323 0.107717 Retthb -0.0623793 0.143085 alpha(0) 9.10178e-06 3.96753e-06 alpha(1) 0.150747 0.0319415 beta(1) 0.819348 0.0388831 Mean dependent var 0.000247 Log-likelihood 2360.239 Schwarz criterion -4626.310 z p-value Const 2.1931 0.02830 ** Retjci -1.1029 0.27009 Retset -0.1304 0.89627 retfbmklc_inde 1.1452 0.25212 Retfssti -0.3543 0.72308 Retidr 0.9951 0.31969 Retmyr -1.9343 0.05308 * Retsgd 0.5505 0.58200 Retpeso 1.4698 0.14161 Retthb -0.4360 0.66287 alpha(0) 2.2941 0.02179 ** alpha(1) 4.7195 <0.00001 *** beta(1) 21.0721 <0.00001 *** Mean dependent var S.D. dependent var 0.016347 Log-likelihood Akaike criterion -4692.477 Schwarz criterion Hannan-Quinn -4667.109 Unconditional error variance = 0.000304357 Table 16: Model 13: GARCH, using observations 2007/06/01-2010/08/11 (T = 834) Dependent variable: retjci Standard errors based on Hessian Coefficient Std. Error Const 0.00119088 0.000506397 Retset 0.0259006 0.0355295 retfbmklci_inde 0.0556889 0.0560895 Retfssti 0.00152385 0.0296245 Retidr 0.00719751 0.0712532 Retmyr -0.247524 0.122619 Retsgd 0.243843 0.14483 Retpeso 0.0816309 0.106845 Retthb -0.0707309 0.17237 Retpsei -0.0254544 0.0334898 alpha(0) 1.29768e-05 3.87661e-06 alpha(1) 0.14556 0.0263961 beta(1) 0.817742 0.0290423 Mean dependent var 0.000554 Log-likelihood 2262.394 Schwarz criterion -4430.622 z p-value Const 2.3517 0.01869 ** Retset 0.7290 0.46601 retfbmklci_inde 0.9929 0.32078 Retfssti 0.0514 0.95898 Retidr 0.1010 0.91954 Retmyr -2.0186 0.04352 ** Retsgd 1.6837 0.09225 * Retpeso 0.7640 0.44486 Retthb -0.4103 0.68155 Retpsei -0.7601 0.44722 alpha(0) 3.3475 0.00082 *** alpha(1) 5.5144 <0.00001 *** beta(1) 28.1569 <0.00001 *** Mean dependent var S.D. dependent var 0.018595 Log-likelihood Akaike criterion -4496.789 Schwarz criterion Hannan-Quinn -4471.420 Unconditional error variance = 0.000353605 Table 17: Model 14: GARCH, using observations 2007/06/01-2010/08/11 (T = 834) Dependent variable: retset Standard errors based on Hessian Coefficient Std. Error const 0.00125671 0.000441034 retfbmklc_inde -0.0220331 0.0482013 retfssti -0.00241563 0.028872 retidr 0.0289232 0.0725929 retmyr 0.200491 0.114925 retsgd -0.0535125 0.136243 retpeso -0.213458 0.102941 retthb -0.230843 0.144696 retpsei 0.0100106 0.0304021 retjci 0.018723 0.0282761 alpha(0) 5.13683e-06 2.02241e-06 alpha(1) 0.119977 0.0208121 beta(1) 0.863977 0.0205942 Mean dependent var 0.000319 Log-likelihood 2357.312 Schwarz criterion -4620.458 Z p-value const 2.8495 0.00438 *** retfbmklc_inde -0.4571 0.64760 retfssti -0.0837 0.93332 retidr 0.3984 0.69031 retmyr 1.7445 0.08106 * retsgd -0.3928 0.69449 retpeso -2.0736 0.03812 ** retthb -1.5954 0.11063 retpsei 0.3293 0.74195 retjci 0.6621 0.50788 alpha(0) 2.5400 0.01109 ** alpha(1) 5.7648 <0.00001 *** beta(1) 41.9524 <0.00001 *** Mean dependent var S.D. dependent var 0.016576 Log-likelihood Akaike criterion -4686.625 Schwarz criterion Hannan-Quinn -4661.257 Unconditional error variance = 0.000320141 Table 18: Model 15: GARCH, using observations 2007/06/01-2010/08/11 (T = 834) Dependent variable: retfbmklci_inde Standard errors based on Hessian Coefficient Std. Error const 0.000758725 0.000252621 retfssti 0.0113419 0.0168251 retidr 0.0254291 0.0365779 retmyr -0.0187246 0.0607504 retsgd 0.0460012 0.0720567 retpeso -0.187645 0.0627862 retthb -0.0611178 0.0836623 retpsei 0.0289376 0.0199808 retjci 0.0126959 0.0158835 retset -0.0129801 0.0181719 alpha(0) 1.70056e-06 7.68163e-07 alpha(1) 0.174617 0.028838 beta(1) 0.825383 0.0272578 Mean dependent var 0.000107 Log-likelihood 2785.127 Schwarz criterion -5476.086 Z p-value const 3.0034 0.00267 *** retfssti 0.6741 0.50024 retidr 0.6952 0.48693 retmyr -0.3082 0.75791 retsgd 0.6384 0.52321 retpeso -2.9886 0.00280 *** retthb -0.7305 0.46507 retpsei 1.4483 0.14754 retjci 0.7993 0.42411 retset -0.7143 0.47504 alpha(0) 2.2138 0.02684 ** alpha(1) 6.0551 <0.00001 *** beta(1) 30.2806 <0.00001 *** Mean dependent var S.D. dependent var 0.010085 Log-likelihood Akaike criterion -5542.253 Schwarz criterion Hannan-Quinn -5516.885 Unconditional error variance = 1.84212e+007 Table 19: Model 16: : GARCH, using observations 2007/06/01-2010/08/11 (T = 834) Dependent variable: retfssti Standard errors based on Hessian Coefficient Std. Error const 0.000500276 0.000402032 retpsei -0.033138 0.0289111 retjci 0.0289614 0.0210753 retidr 0.00101502 0.0738605 retpeso -0.0630895 0.0864475 retthb -0.22987 0.117993 retmyr 0.176334 0.105469 retsgd 0.0108712 0.131793 retset 0.00896206 0.0252185 retfbmklci_inde 0.0866703 0.0445245 alpha(0) 2.35098e-06 1.30289e-06 alpha(1) 0.13431 0.0230304 beta(1) 0.863584 0.0209251 Mean dependent var -0.000149 Log-likelihood 2373.436 Schwarz criterion -4652.704 Z p-value const 1.2444 0.21336 retpsei -1.1462 0.25171 retjci 1.3742 0.16938 retidr 0.0137 0.98904 retpeso -0.7298 0.46551 retthb -1.9482 0.05140 * retmyr 1.6719 0.09454 * retsgd 0.0825 0.93426 retset 0.3554 0.72231 retfbmklci_inde 1.9466 0.05159 * alpha(0) 1.8044 0.07116 * alpha(1) 5.8319 <0.00001 *** beta(1) 41.2703 <0.00001 *** Mean dependent var S.D. dependent var 0.016893 Log-likelihood Akaike criterion -4718.871 Schwarz criterion Hannan-Quinn -4693.503 Unconditional error variance = 0.00111672 Table 20: Model 17: GARCH, using observations 2007/06/01-2010/08/11 (T = 834) Dependent variable: retpeso Standard errors based on Hessian Coefficient Std. Error Const -4.53241e-05 0.000144234 Retidr 0.0108114 0.0201272 Retmyr 0.0209637 0.0364225 Retsgd 0.0149479 0.0386298 retthb -0.111454 0.0469043 retpsei 0.00495771 0.00973482 retjci 0.00926667 0.00805189 retset -0.0135335 0.00936735 retfbmklci_inde -0.0322533 0.0151236 retfssti -0.00772692 0.00863735 alpha(0) 6.28294e-07 3.02312e-07 alpha(1) 0.103505 0.0253881 beta(1) 0.867795 0.0306009 Mean dependent var -0.000076 Log-likelihood 3342.952 Schwarz criterion -6591.736 z p-value Const -0.3142 0.75334 Retidr 0.5372 0.59116 Retmyr 0.5756 0.56491 Retsgd 0.3870 0.69879 retthb -2.3762 0.01749 ** retpsei 0.5093 0.61056 retjci 1.1509 0.24979 retset -1.4447 0.14853 retfbmklci_inde -2.1326 0.03295 ** retfssti -0.8946 0.37100 alpha(0) 2.0783 0.03768 ** alpha(1) 4.0769 0.00005 *** beta(1) 28.3585 <0.00001 *** Mean dependent var S.D. dependent var 0.004605 Log-likelihood Akaike criterion -6657.903 Schwarz criterion Hannan-Quinn -6632.535 Unconditional error variance = 2.1892e-005 Table 21: Model 18: GARCH, using observations 2007/06/01-2010/08/11 (T = 834) Dependent variable: retidr Standard errors based on Hessian Coefficient Std. Error const -5.24677e-05 0.000139118 retmyr 0.184379 0.0450983 retsgd -0.0168322 0.0449055 retthb 0.00859123 0.0315043 retpsei -0.00255088 0.00691328 retjci -0.00785515 0.00694385 retset 0.00661844 0.00652647 retfbmklc_inde -0.00176059 0.0120432 retfssti 0.0155717 0.00661603 Retpeso 0.0328715 0.0265139 alpha(0) 5.78678e-07 1.69907e-07 alpha(1) 0.149061 0.0290481 beta(1) 0.846949 0.025351 Mean dependent var -0.000013 Log-likelihood 3243.926 Schwarz criterion -6393.684 Z p-value const -0.3771 0.70607 retmyr 4.0884 0.00004 *** retsgd -0.3748 0.70778 retthb 0.2727 0.78508 retpsei -0.3690 0.71214 retjci -1.1312 0.25796 retset 1.0141 0.31054 retfbmklc_inde -0.1462 0.88377 retfssti 2.3536 0.01859 ** Retpeso 1.2398 0.21506 alpha(0) 3.4058 0.00066 *** alpha(1) 5.1315 <0.00001 *** beta(1) 33.4089 <0.00001 *** Mean dependent var S.D. dependent var 0.006830 Log-likelihood Akaike criterion -6459.851 Schwarz criterion Hannan-Quinn -6434.483 Unconditional error variance = 0.000145035 Table 22: Model 19: GARCH, using observations 2007/06/01-2010/08/11 (T = 834) Dependent variable: retthb Standard errors based on Hessian Coefficient Std. Error const -8.62565e-05 6.80432e-05 retmyr 0.0016322 0.0185389 retsgd 0.238865 0.0258216 retpsei -0.00757537 0.00522802 retjci -0.00165233 0.00440304 retset -0.0121791 0.00494023 retfbmklc_inde -0.00918824 0.00676398 retfssti -0.00685871 0.00439909 retpeso -0.0548585 0.0172497 retidr -0.00316257 0.012035 alpha(0) 3.28729e-07 1.08083e-07 alpha(1) 0.262849 0.0452133 beta(1) 0.737151 0.0403357 Mean dependent var -0.000124 Log-likelihood 3794.682 Schwarz criterion -7495.197 z p-value const -1.2677 0.20491 retmyr 0.0880 0.92984 retsgd 9.2506 <0.00001 *** retpsei -1.4490 0.14734 retjci -0.3753 0.70746 retset -2.4653 0.01369 ** retfbmklc_inde -1.3584 0.17433 retfssti -1.5591 0.11897 retpeso -3.1803 0.00147 *** retidr -0.2628 0.79272 alpha(0) 3.0415 0.00235 *** alpha(1) 5.8135 <0.00001 *** beta(1) 18.2754 <0.00001 *** Mean dependent var S.D. dependent var 0.003580 Log-likelihood Akaike criterion -7561.364 Schwarz criterion Hannan-Quinn -7535.996 Unconditional error variance = 1.92831e+006 Table 23: Model 20: GARCH, using observations 2007/06/01-2010/08/11 (T = 834) Dependent variable: retmyr Standard errors based on Hessian Coefficient Std. Error const -0.000193129 0.000112758 retsgd 0.0677273 0.0384396 retpsei 0.00548671 0.0067486 retjci -5.19301e-05 0.00634895 retset -0.000655657 0.00756114 retfbmklci_inde -0.00225435 0.0109943 retfssti -0.00874604 0.00662814 retpeso 0.0171017 0.0234216 retidr 0.0770925 0.0235447 retthb 0.0108206 0.0254415 alpha(0) 3.07789e-07 1.4771e-07 alpha(1) 0.124471 0.0322547 beta(1) 0.868968 0.02823 Mean dependent var -0.000097 Log-likelihood 3490.296 Schwarz criterion -6886.425 z p-value const -1.7128 0.08675 * retsgd 1.7619 0.07808 * retpsei 0.8130 0.41621 retjci -0.0082 0.99347 retset -0.0867 0.93090 retfbmklci_inde -0.2050 0.83753 retfssti -1.3195 0.18699 retpeso 0.7302 0.46529 retidr 3.2743 0.00106 *** retthb 0.4253 0.67061 alpha(0) 2.0837 0.03718 ** alpha(1) 3.8590 0.00011 *** beta(1) 30.7817 <0.00001 *** Mean dependent var S.D. dependent var 0.003919 Log-likelihood Akaike criterion -6952.592 Schwarz criterion Hannan-Quinn -6927.224 Unconditional error variance = 4.69133e-005 Table 24: Model 21: GARCH, using observations 2007/06/01-2010/08/11 (T = 834) Dependent variable: retsgd Standard errors based on Hessian Coefficient Std. Error const -0.000118557 9.95123e-05 retpsei -0.000798756 0.00604538 retjci 0.00982239 0.00544697 retset 0.00354235 0.00669941 retfbmklc_inde -0.00737134 0.00984218 retfssti -0.00146166 0.00618743 retpeso -0.000992164 0.0210715 retidr -0.013964 0.0195587 retthb 0.129528 0.0310012 retmyr 0.0528361 0.0293694 alpha(0) 7.40557e-08 4.56461e-08 alpha(1) 0.0495539 0.0110603 beta(1) 0.944847 0.0121377 Mean dependent var -0.000112 Log-likelihood 3611.545 Schwarz criterion -7128.923 z p-value const -1.1914 0.23351 retpsei -0.1321 0.89488 retjci 1.8033 0.07134 * retset 0.5288 0.59698 retfbmklc_inde -0.7490 0.45389 retfssti -0.2362 0.81325 retpeso -0.0471 0.96244 retidr -0.7140 0.47526 retthb 4.1782 0.00003 *** retmyr 1.7990 0.07202 * alpha(0) 1.6224 0.10472 alpha(1) 4.4803 <0.00001 *** beta(1) 77.8441 <0.00001 *** Mean dependent var S.D. dependent var 0.003683 Log-likelihood Akaike criterion -7195.090 Schwarz criterion Hannan-Quinn -7169.721 Unconditional error variance = 1.32275e-005

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Author: | Kabigting, Leila C.; Hapitan, Rene B. |
---|---|

Publication: | Journal of International Business Research |

Geographic Code: | 4E |

Date: | Dec 1, 2011 |

Words: | 7351 |

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