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Forecasting Volatility in Asian Stock Markets: Contributions of Local, Regional, and Global Factors

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This paper examines volatility forecasting for the broad market indices of 12 Asian stock markets. After considering the long memory in volatility and volatility jumps, the paper incorporates local, regional, and global factors into a heterogeneous autoregressive model for volatility forecasting. Compared to several existing studies, the model produces smaller forecasting errors. The empirical findings shed new light on the spillover effect from regional and global factors to local market volatility. Despite the common perception of increased globalization, the paper finds that volatility in Asia is primarily driven by local factors. During the period January 2005 to April 2010, regional and global factors explain 2%–3% of the volatility in the next 10 days for Asian emerging markets, and 3%–6% for Asian developed markets. There was no significant increase in the contribution of global factors to local market volatility.

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  • Wamg, Jianxin, 2011. "Forecasting Volatility in Asian Stock Markets: Contributions of Local, Regional, and Global Factors," Asian Development Review, Asian Development Bank, vol. 28(2), pages 32-57.
  • Handle: RePEc:ris:adbadr:2730
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    as
    1. Kaminsky, Graciela L. & Reinhart, Carmen M., 2002. "Financial markets in times of stress," Journal of Development Economics, Elsevier, vol. 69(2), pages 451-470, December.
    2. Nour Meddahi, 2002. "A theoretical comparison between integrated and realized volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(5), pages 479-508.
    3. Harald Hau & Hélène Rey, 2004. "Can Portfolio Rebalancing Explain the Dynamics of Equity Returns, Equity Flows, and Exchange Rates?," American Economic Review, American Economic Association, vol. 94(2), pages 126-133, May.
    4. Conrad, Christian & Karanasos, Menelaos & Zeng, Ning, 2011. "Multivariate fractionally integrated APARCH modeling of stock market volatility: A multi-country study," Journal of Empirical Finance, Elsevier, vol. 18(1), pages 147-159, January.
    5. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold, 2007. "Roughing It Up: Including Jump Components in the Measurement, Modeling, and Forecasting of Return Volatility," The Review of Economics and Statistics, MIT Press, vol. 89(4), pages 701-720, November.
    6. Levine, Ross & Zervos, Sara, 1998. "Stock Markets, Banks, and Economic Growth," American Economic Review, American Economic Association, vol. 88(3), pages 537-558, June.
    7. Chiang, Thomas C. & Jeon, Bang Nam & Li, Huimin, 2007. "Dynamic correlation analysis of financial contagion: Evidence from Asian markets," Journal of International Money and Finance, Elsevier, vol. 26(7), pages 1206-1228, November.
    8. Twm Evans & David McMillan, 2007. "Volatility forecasts: the role of asymmetric and long-memory dynamics and regional evidence," Applied Financial Economics, Taylor & Francis Journals, vol. 17(17), pages 1421-1430.
    9. Yilmaz, Kamil, 2010. "Return and volatility spillovers among the East Asian equity markets," Journal of Asian Economics, Elsevier, vol. 21(3), pages 304-313, June.
    10. Martens, Martin & van Dijk, Dick & de Pooter, Michiel, 2009. "Forecasting S&P 500 volatility: Long memory, level shifts, leverage effects, day-of-the-week seasonality, and macroeconomic announcements," International Journal of Forecasting, Elsevier, vol. 25(2), pages 282-303.
    11. Lo, Andrew W, 1991. "Long-Term Memory in Stock Market Prices," Econometrica, Econometric Society, vol. 59(5), pages 1279-1313, September.
    12. Muller, Ulrich A. & Dacorogna, Michel M. & Dave, Rakhal D. & Olsen, Richard B. & Pictet, Olivier V. & von Weizsacker, Jacob E., 1997. "Volatilities of different time resolutions -- Analyzing the dynamics of market components," Journal of Empirical Finance, Elsevier, vol. 4(2-3), pages 213-239, June.
    13. Fulvio Corsi, 2009. "A Simple Approximate Long-Memory Model of Realized Volatility," Journal of Financial Econometrics, Oxford University Press, vol. 7(2), pages 174-196, Spring.
    14. Andersen, Torben G & Bollerslev, Tim, 1998. "Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 885-905, November.
    15. Schill, Michael J., 2004. "Sailing in rough water: market volatility and corporate finance," Journal of Corporate Finance, Elsevier, vol. 10(5), pages 659-681, November.
    16. Ole E. Barndorff-Nielsen, 2004. "Power and Bipower Variation with Stochastic Volatility and Jumps," Journal of Financial Econometrics, Oxford University Press, vol. 2(1), pages 1-37.
    17. Parkinson, Michael, 1980. "The Extreme Value Method for Estimating the Variance of the Rate of Return," The Journal of Business, University of Chicago Press, vol. 53(1), pages 61-65, January.
    18. Meddahi, N., 2001. "A Theoretical Comparison Between Integrated and Realized Volatilies," Cahiers de recherche 2001-26, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
    19. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 2003. "Modeling and Forecasting Realized Volatility," Econometrica, Econometric Society, vol. 71(2), pages 579-625, March.
    20. Ser-Huang Poon & Clive W.J. Granger, 2003. "Forecasting Volatility in Financial Markets: A Review," Journal of Economic Literature, American Economic Association, vol. 41(2), pages 478-539, June.
    21. Am�lie Charles, 2010. "Does the day-of-the-week effect on volatility improve the volatility forecasts?," Applied Economics Letters, Taylor & Francis Journals, vol. 17(3), pages 257-262, February.
    22. Lee, Wayne Y. & Jiang, Christine X. & Indro, Daniel C., 2002. "Stock market volatility, excess returns, and the role of investor sentiment," Journal of Banking & Finance, Elsevier, vol. 26(12), pages 2277-2299.
    23. Bollerslev, Tim & Jubinski, Dan, 1999. "Equity Trading Volume and Volatility: Latent Information Arrivals and Common Long-Run Dependencies," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(1), pages 9-21, January.
    24. Bessembinder, Hendrik & Seguin, Paul J, 1992. "Futures-Trading Activity and Stock Price Volatility," Journal of Finance, American Finance Association, vol. 47(5), pages 2015-2034, December.
    25. Engle, Robert F. & Marcucci, Juri, 2006. "A long-run Pure Variance Common Features model for the common volatilities of the Dow Jones," Journal of Econometrics, Elsevier, vol. 132(1), pages 7-42, May.
    26. Lux, Thomas & Kaizoji, Taisei, 2007. "Forecasting volatility and volume in the Tokyo Stock Market: Long memory, fractality and regime switching," Journal of Economic Dynamics and Control, Elsevier, vol. 31(6), pages 1808-1843, June.
    27. Sassan Alizadeh & Michael W. Brandt & Francis X. Diebold, 2002. "Range‐Based Estimation of Stochastic Volatility Models," Journal of Finance, American Finance Association, vol. 57(3), pages 1047-1091, June.
    28. Patton, Andrew J., 2011. "Volatility forecast comparison using imperfect volatility proxies," Journal of Econometrics, Elsevier, vol. 160(1), pages 246-256, January.
    29. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-438, July.
    30. Allen, Franklin & Gale, Douglas, 1994. "Limited Market Participation and Volatility of Asset Prices," American Economic Review, American Economic Association, vol. 84(4), pages 933-955, September.
    31. Khan, Saleheen & Park, Kwang Woo (Ken), 2009. "Contagion in the stock markets: The Asian financial crisis revisited," Journal of Asian Economics, Elsevier, vol. 20(5), pages 561-569, September.
    32. Malay Bhattacharyya & Dileep Kumar M & Ramesh Kumar, 2009. "Optimal sampling frequency for volatility forecast models for the Indian stock markets," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(1), pages 38-54.
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    Cited by:

    1. Mr. Fabian Lipinsky & Ms. Li L Ong, 2014. "Asia’s Stock Markets: Are There Crouching Tigers and Hidden Dragons?," IMF Working Papers 2014/037, International Monetary Fund.
    2. Malik Shahzad Shabbir & Laila Refiana Said & Irem Pelit & Esma Irmak, 2023. "The Dynamic Relationship among Domestic Stock Returns Volatility, Oil Prices, Exchange Rate and Macroeconomic Factors of Investment," International Journal of Energy Economics and Policy, Econjournals, vol. 13(3), pages 560-565, May.
    3. Maria Socorro Gochoco-Bautista & Jianxin Wang & Minxian Yang, 2014. "Commodity Price, Carry Trade, and the Volatility and Liquidity of Asian Currencies," The World Economy, Wiley Blackwell, vol. 37(6), pages 811-833, June.
    4. Gochoco-Bautista, Maria Socorro & Remolona, Eli M., 2012. "Going Regional: How to Deepen ASEAN's Financial Markets," ADB Economics Working Paper Series 300, Asian Development Bank.

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    More about this item

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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