Modelling Stock Market Volatility: Evidence from Vietnam
Main Article Content
Abstract
Abtract: This study empirically investigates the volatility pattern of Vietnam stock market based on time series data which consists of daily closing prices of VN-Index in the period 2005-2016. The analysis has used both symmetric and asymmetric Generalized Autoregressive Conditional Heteroscedastic (GARCH) models. Based on Akaike Information Criterion and Schwarz Information Criterion criteria, the study proves that GARCH (1,1) and EGARCH (1,1) are the most appropriate model to measure the symmetric and asymmetric volatility of VN-Index respectively. The study also provides evidence of the existence of asymmetric effects (leverage) via the parameters of the EGARCH (1,1) model that show that negative shocks have significant effects on conditional variance (fluctuation). Meanwhile, in the TGARCH (1,1) model, the findings are not as expected. This study also provides investors with a tool to forecast the rate of return of the stock market. At the same time, the findings will help investors determine the profitability and volatility of the market so that they can make the right decisions on holding the securities.
Keywords
Asymmetric volatility, conditional volatility, GARCH models, leverage effect
References
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[2] Bollerslev, T., “Generalized autoregressive conditional heteroskedasticity”, Journal of econometrics, 31 (1986) 3, 307-327.
[3] Engle, R. F., Lilien, D. M., & Robins, R. P., “Estimating time varying risk premia in the term structure: The ARCH-M model”, Econometrica: Journal of the Econometric Society (1987), 391-407.
[4] Nelson, D. B., “Conditional heteroskedasticity in asset returns: A new approach”, Econometrica: Journal of the Econometric Society, 1991, 347-370.
[5] Zakoian, J. M., “Threshold heteroskedastic models”, Journal of Economic Dynamics and control, 18 (1994) 5, 931-955.
[6] Glosten, L. R., Jagannathan, R., & Runkle, D. E., “On the relation between the expected value and the volatility of the nominal excess return on stocks”, The Journal of Finance, 48 (1993) 5, 1779-1801.
[7] Schwert, G. W., “Why does stock market volatility change over time?”, The Journal of Finance, 44 (1989) 5, 1115-1153.
[8] Ding, Z., Granger, C. W., & Engle, R. F., “A long memory property of stock market returns and a new model”, Journal of Empirical Finance, 1 (1993) 1, 83-106.
[9] Baillie, R. T., & DeGennaro, R. P., “Stock returns and volatility”, Journal of financial and Quantitative Analysis, 25 (1990) 2, 203-214.
[10] Bekaert, G., & Wu, G., “Asymmetric volatility and risk in equity markets”, Review of Financial Studies, 13 (2000) 1, 1-42.
[11] Chand, S., Kamal, S., & Ali, I., “Modelling and volatility analysis of share prices using ARCH and GARCH models”, World Applied Sciences Journal, 19 (2012) 1, 77-82.
[12] Chou, R. Y., “Volatility persistence and stock valuations: Some empirical evidence using GARCH”, Journal of Applied Econometrics, 3 (1988) 4, 279-294.
[13] French, K. R., Schwert, G. W., & Stambaugh, R. F., “Expected stock returns and volatility”, Journal of Financial Economics, 19 (1987) 1, 3-29.
[14] Tah, K. A., “Relationship between volatility and expected returns in two emerging markets”, Business and Economics Journal, 84 (2013), 1-7.
[15] Floros, C., “Modelling volatility using GARCH models: evidence from Egypt and Israel”, Middle Eastern Finance and Economics 2 (2008), 31-41.
[16] AbdElaal, M. A., “Modeling and forecasting time varying stock return volatility in the Egyptian stock market”, International Research Journal of Finance and Economics, 78 (2011).
[17] GC, S. B., “Volatility analysis of Nepalese stock market”, Journal of Nepalese Business Studies, 5 (2009) 1, 76-84.
[18a] Karmakar, M., “Modeling conditional volatility of the Indian stock markets”, Vikalpa, 30 (2005) 3, 21.
[18b] Karmakar, M., “Asymmetric volatility and risk-return relationship in the Indian stock market”, South Asia Economic Journal, 8 (2007) 1, 99-116.
[19a] Goudarzi, H., & Ramanarayanan, C., “Modeling and estimation of volatility in the Indian stock market”, International Journal of Business and Management, 5 (2010) 2, 85.
[19b] Goudarzi, H., & Ramanarayanan, C., “Modeling asymmetric volatility in the Indian stock market”, International Journal of Business and Management, 6 (2011) 3, 221.
[20] Singh, S., & Tripathi, L., “Modelling Stock Market Return Volatility: Evidence from India”, Research Journal of Finance and Accounting, 7 (2016) 16, 93-101.
[21] Kulshreshtha, P., & Mittal, A., :Volatility in the Indian Financial Market Before, During and After the Global Financial Crisis”, Journal of Accounting and Finance, 15 (2015) 3, 141.
[22] Võ Thị Thúy Anh, Nguyễn Anh Tùng, “Mô hình giá trị chịu rủi ro trong đầu tư cổ phiếu đối với VN-Index”, Tạp chí Công nghệ Ngân hàng 57 (2010), 42.
[23] Đặng Hữu Mẫn, Hoàng Dương Việt Anh, “Quality of market risk prediction based on the VN-Index”, Economic Studies, 397 (2013) 6, 19-27.
[24] Bùi Hữu Phước, Phạm Thị Thu Hồng, Ngô Văn Toàn, “Biến động giá trị tài sản của các công ty niêm yết trên thị trường chứng khoán Việt Nam”, Tạp chí Công nghệ Ngân hàng, 127 (2016), 2.
[25] Dickey, D. A., & Fuller, W. A., “Distribution of the estimators for autoregressive time series with a unit root”, Journal of the American Statistical Association, 74 (1979) 366a, 427-431.
[26] Phillips, P. C., & Perron, P., “Testing for a unit root in time series regression”, Biometrika, 75 (1988) 2, 335-346.
[27] Mandimika, N. Z., & Chinzara, Z., “Risk–return trade‐off and behaviour of volatility on the south african stock market: Evidence from both aggregate and disaggregate data”, South African Journal of Economics, 80 (2012) 3, 345-366.
References
[2] Bollerslev, T., “Generalized autoregressive conditional heteroskedasticity”, Journal of econometrics, 31 (1986) 3, 307-327.
[3] Engle, R. F., Lilien, D. M., & Robins, R. P., “Estimating time varying risk premia in the term structure: The ARCH-M model”, Econometrica: Journal of the Econometric Society (1987), 391-407.
[4] Nelson, D. B., “Conditional heteroskedasticity in asset returns: A new approach”, Econometrica: Journal of the Econometric Society, 1991, 347-370.
[5] Zakoian, J. M., “Threshold heteroskedastic models”, Journal of Economic Dynamics and control, 18 (1994) 5, 931-955.
[6] Glosten, L. R., Jagannathan, R., & Runkle, D. E., “On the relation between the expected value and the volatility of the nominal excess return on stocks”, The Journal of Finance, 48 (1993) 5, 1779-1801.
[7] Schwert, G. W., “Why does stock market volatility change over time?”, The Journal of Finance, 44 (1989) 5, 1115-1153.
[8] Ding, Z., Granger, C. W., & Engle, R. F., “A long memory property of stock market returns and a new model”, Journal of Empirical Finance, 1 (1993) 1, 83-106.
[9] Baillie, R. T., & DeGennaro, R. P., “Stock returns and volatility”, Journal of financial and Quantitative Analysis, 25 (1990) 2, 203-214.
[10] Bekaert, G., & Wu, G., “Asymmetric volatility and risk in equity markets”, Review of Financial Studies, 13 (2000) 1, 1-42.
[11] Chand, S., Kamal, S., & Ali, I., “Modelling and volatility analysis of share prices using ARCH and GARCH models”, World Applied Sciences Journal, 19 (2012) 1, 77-82.
[12] Chou, R. Y., “Volatility persistence and stock valuations: Some empirical evidence using GARCH”, Journal of Applied Econometrics, 3 (1988) 4, 279-294.
[13] French, K. R., Schwert, G. W., & Stambaugh, R. F., “Expected stock returns and volatility”, Journal of Financial Economics, 19 (1987) 1, 3-29.
[14] Tah, K. A., “Relationship between volatility and expected returns in two emerging markets”, Business and Economics Journal, 84 (2013), 1-7.
[15] Floros, C., “Modelling volatility using GARCH models: evidence from Egypt and Israel”, Middle Eastern Finance and Economics 2 (2008), 31-41.
[16] AbdElaal, M. A., “Modeling and forecasting time varying stock return volatility in the Egyptian stock market”, International Research Journal of Finance and Economics, 78 (2011).
[17] GC, S. B., “Volatility analysis of Nepalese stock market”, Journal of Nepalese Business Studies, 5 (2009) 1, 76-84.
[18a] Karmakar, M., “Modeling conditional volatility of the Indian stock markets”, Vikalpa, 30 (2005) 3, 21.
[18b] Karmakar, M., “Asymmetric volatility and risk-return relationship in the Indian stock market”, South Asia Economic Journal, 8 (2007) 1, 99-116.
[19a] Goudarzi, H., & Ramanarayanan, C., “Modeling and estimation of volatility in the Indian stock market”, International Journal of Business and Management, 5 (2010) 2, 85.
[19b] Goudarzi, H., & Ramanarayanan, C., “Modeling asymmetric volatility in the Indian stock market”, International Journal of Business and Management, 6 (2011) 3, 221.
[20] Singh, S., & Tripathi, L., “Modelling Stock Market Return Volatility: Evidence from India”, Research Journal of Finance and Accounting, 7 (2016) 16, 93-101.
[21] Kulshreshtha, P., & Mittal, A., :Volatility in the Indian Financial Market Before, During and After the Global Financial Crisis”, Journal of Accounting and Finance, 15 (2015) 3, 141.
[22] Võ Thị Thúy Anh, Nguyễn Anh Tùng, “Mô hình giá trị chịu rủi ro trong đầu tư cổ phiếu đối với VN-Index”, Tạp chí Công nghệ Ngân hàng 57 (2010), 42.
[23] Đặng Hữu Mẫn, Hoàng Dương Việt Anh, “Quality of market risk prediction based on the VN-Index”, Economic Studies, 397 (2013) 6, 19-27.
[24] Bùi Hữu Phước, Phạm Thị Thu Hồng, Ngô Văn Toàn, “Biến động giá trị tài sản của các công ty niêm yết trên thị trường chứng khoán Việt Nam”, Tạp chí Công nghệ Ngân hàng, 127 (2016), 2.
[25] Dickey, D. A., & Fuller, W. A., “Distribution of the estimators for autoregressive time series with a unit root”, Journal of the American Statistical Association, 74 (1979) 366a, 427-431.
[26] Phillips, P. C., & Perron, P., “Testing for a unit root in time series regression”, Biometrika, 75 (1988) 2, 335-346.
[27] Mandimika, N. Z., & Chinzara, Z., “Risk–return trade‐off and behaviour of volatility on the south african stock market: Evidence from both aggregate and disaggregate data”, South African Journal of Economics, 80 (2012) 3, 345-366.