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How volatile are East Asian stocks during high volatility periods?

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  • Carlos Bautista

Abstract

This study reports estimates of the magnitude of volatility during abnormal times relative to normal periods for seven East Asian economies using a rudimentary univariate Markov-switching ARCH method. The results show that global and regional events such as the 1990 Gulf War and the 1997 Asian currency crisis led to high volatility episodes whose magnitude relative to normal times differ from country to country. Country-specific events such as the opening up of country borders in the mid-1990s are also observed to lead to high volatility periods. Additional insights are obtained when volatility is assumed to evolve according to a three-state Markov regime switching process.

Suggested Citation

  • Carlos Bautista, 2005. "How volatile are East Asian stocks during high volatility periods?," Applied Economics Letters, Taylor & Francis Journals, vol. 12(5), pages 319-326.
    • Handle: RePEc:taf:apeclt:v:12:y:2005:i:5:p:319-326
    DOI: 10.1080/13504850500044138
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    References listed on IDEAS

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    1. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
    2. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    3. C. C. Bautista, 2003. "Stock market volatility in the Philippines," Applied Economics Letters, Taylor & Francis Journals, vol. 10(5), pages 315-318, April.
    4. Bollerslev, Tim & Chou, Ray Y. & Kroner, Kenneth F., 1992. "ARCH modeling in finance : A review of the theory and empirical evidence," Journal of Econometrics, Elsevier, vol. 52(1-2), pages 5-59.
    5. Ming-Yuan Leon Li & Hsiou-wei William Lin, 2004. "Estimating value-at-risk via Markov switching ARCH models - an empirical study on stock index returns," Applied Economics Letters, Taylor & Francis Journals, vol. 11(11), pages 679-691.
    6. Gray, Stephen F., 1996. "Modeling the conditional distribution of interest rates as a regime-switching process," Journal of Financial Economics, Elsevier, vol. 42(1), pages 27-62, September.
    7. Hamilton, James D. & Susmel, Raul, 1994. "Autoregressive conditional heteroskedasticity and changes in regime," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 307-333.
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    Cited by:

    1. Pandey, Dharen Kumar & Lucey, Brian M. & Kumar, Satish, 2023. "Border disputes, conflicts, war, and financial markets research: A systematic review," Research in International Business and Finance, Elsevier, vol. 65(C).
    2. K. Maris & K. Nikolopoulos & K. Giannelos & V. Assimakopoulos, 2007. "Options trading driven by volatility directional accuracy," Applied Economics, Taylor & Francis Journals, vol. 39(2), pages 253-260.

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