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Examining the Volatility of Taiwan Stock Index Returns Via a Three-Volatility-Regime Markov-Switching ARCH Model

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  • Li, Ming-Yuan Leon
  • Lin, Hsiou-Wei William

Abstract

This study adopts Hamilton and Susmel's (1994) Markov-switching ARCH (hereafter SWARCH) model to examine the volatility of the valued-weighted Taiwan Stock Index (hereafter TAIEX) returns. We also conduct sensitivity tests on comparison observations of Dow Jones and Nikkei stock indices. Our empirical findings are consistent with the following notions. First, the SWARCH model appears to outperform the competing ARCH and GARCH models in estimating the volatilities of TAIEX. Second, the three-volatility-regime setting is descriptive for TAIEX and Nikkei. In contrast with Hamilton and Susmel (1994), the contemporaneous Dow Jones adopted in this paper has only two regimes. Our test results suggest that the optimal number of volatility regimes is sensitive to the choice of sample periods. Third, our empirical results also lend an explanation to such phenomenon: the probability that TAIEX directly moves from a low (high) volatility regime to the high (low) volatility regime approaches zero, whereas TAEIX happened to be in a low volatility regime during the pre-financial-crisis period from April, 1996 to July, 1997. These can explain why Taiwan was one of Asia's few star performers compared with recession-hit neighbors during the first eighteen months of Asia's financial crisis. Copyright 2003 by Kluwer Academic Publishers

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  • Li, Ming-Yuan Leon & Lin, Hsiou-Wei William, 2003. "Examining the Volatility of Taiwan Stock Index Returns Via a Three-Volatility-Regime Markov-Switching ARCH Model," Review of Quantitative Finance and Accounting, Springer, vol. 21(2), pages 123-139, September.
    • Handle: RePEc:kap:rqfnac:v:21:y:2003:i:2:p:123-39
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    Citations

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    Cited by:

    1. Ming‐Yuan Leon Li, 2009. "The dynamics of the relationship between spot and futures markets under high and low variance regimes," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 25(6), pages 696-718, November.
    2. Wang, Ping & Theobald, Mike, 2008. "Regime-switching volatility of six East Asian emerging markets," Research in International Business and Finance, Elsevier, vol. 22(3), pages 267-283, September.
    3. Georgios Kouretas & Manolis Syllignakis, 2012. "Switching Volatility in Emerging Stock Markets and Financial Liberalization: Evidence from the new EU Member Countries," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 4(2), pages 65-93, June.
    4. José Dias & Sofia Ramos, 2014. "The aftermath of the subprime crisis: a clustering analysis of world banking sector," Review of Quantitative Finance and Accounting, Springer, vol. 42(2), pages 293-308, February.
    5. Abounoori, Esmaiel & Elmi, Zahra (Mila) & Nademi, Younes, 2016. "Forecasting Tehran stock exchange volatility; Markov switching GARCH approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 445(C), pages 264-282.
    6. Kim Hiang Liow & Qing Ye, 2014. "Switching volatility and cross-market linkages in public property markets," Journal of Property Research, Taylor & Francis Journals, vol. 31(4), pages 287-314, December.
    7. Jiang, Yu & Fang, Xianming, 2015. "Bull, bear or any other states in US stock market?," Economic Modelling, Elsevier, vol. 44(C), pages 54-58.
    8. Massimo Costabile & Arturo Leccadito & Ivar Massabó & Emilio Russo, 2014. "A reduced lattice model for option pricing under regime-switching," Review of Quantitative Finance and Accounting, Springer, vol. 42(4), pages 667-690, May.
    9. Arturo Leccadito & Stefania Veltri, 2015. "A regime switching Ohlson model," Quality & Quantity: International Journal of Methodology, Springer, vol. 49(5), pages 2015-2035, September.

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