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Volatility states and international diversification of international stock markets

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  • Ming-Yuan Leon Li

Abstract

This study uses a Markov-switching technique to identify the volatility state of international stock markets. Further, we consider four possible state combinations of the individual and world stock markets to examine an interesting issue regarding the relationship between international diversification and market volatility. Last, we adopt a framework based on the state-varying correlation to establish a more efficient international investment strategy. Our empirical results are consistent with the two following notions. First, the situation of both the individual and world stock markets during high volatility states will be associated with the minimum benefit of risk-reduction from international diversification and a maximum cross-market correlation. Second, by incorporating the character of state-varying correlation into the establishment of an international portfolio, we can create a more efficient investment strategy with less risk, or greater return for a given risk.

Suggested Citation

  • Ming-Yuan Leon Li, 2007. "Volatility states and international diversification of international stock markets," Applied Economics, Taylor & Francis Journals, vol. 39(14), pages 1867-1876.
    • Handle: RePEc:taf:applec:v:39:y:2007:i:14:p:1867-1876
    DOI: 10.1080/00036840500428088
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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. Kim Hiang Liow & Qing Ye, 2018. "Regime dependent volatilities and correlation in international securitized real estate markets," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 45(3), pages 457-487, August.
    3. Li, Ming-Yuan Leon, 2009. "Could the jump diffusion technique enhance the effectiveness of futures hedging models?," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(10), pages 3076-3088.
    4. 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.
    5. Turhan Korkmaz & Emrah I. Çevik & Elif Birkan & Nesrin ÖzataÇ, 2010. "Testing Capm using Markov Switching Model: The Case of Coal Firms," Economic Research-Ekonomska Istraživanja, Taylor & Francis Journals, vol. 23(2), pages 44-59, January.
    6. Earl D. Benson & Sophie X. Kong, 2015. "The Co-Movement of U.S. Equity Returns with the Developed and Emerging Markets of Australasia and Asia," International Journal of Business and Social Research, MIR Center for Socio-Economic Research, vol. 5(1), pages 102-117, January.
    7. Ming‐yuan leon Li, 2009. "Change In Volatility Regimes And Diversification In Emerging Stock Markets," South African Journal of Economics, Economic Society of South Africa, vol. 77(1), pages 59-80, March.
    8. Donald Lien & Ziling Wang & Xiaojian Yu, 2021. "Quantile information share under Markov regime‐switching," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(4), pages 493-513, April.
    9. Alex YiHou Huang, 2012. "Volatility forecasting by quantile regression," Applied Economics, Taylor & Francis Journals, vol. 44(4), pages 423-433, February.
    10. Juan Reboredo, 2010. "Nonlinear effects of oil shocks on stock returns: a Markov-switching approach," Applied Economics, Taylor & Francis Journals, vol. 42(29), pages 3735-3744.
    11. Ming-Yuan Leon Li, 2009. "Nonlinear interrelations between ADRs and their underlying stocks revisited: application of threshold VECM," Applied Economics Letters, Taylor & Francis Journals, vol. 16(18), pages 1867-1873.
    12. Emrah İ. Çevik & Turhan Korkmaz & Erdal Atukeren, 2012. "Business confidence and stock returns in the USA: a time-varying Markov regime-switching model," Applied Financial Economics, Taylor & Francis Journals, vol. 22(4), pages 299-312, February.
    13. Donald Lien & Ziling Wang & Xiaojian Yu, 2021. "Optimal quantile hedging under Markov regime switching," Empirical Economics, Springer, vol. 60(5), pages 2177-2201, May.
    14. 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.
    15. Emrah Çevik & Erdal Atukeren & Turhan Korkmaz, 2013. "Nonlinearity and nonstationarity in international art market prices: evidence from Markov-switching ADF unit root tests," Empirical Economics, Springer, vol. 45(2), pages 675-695, October.
    16. Simmons-Süer, Banu, 2018. "“How relevant is capital structure for aggregate investment? a regime-switching approach”," International Review of Economics & Finance, Elsevier, vol. 53(C), pages 109-117.

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