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Reexamining the time-varying volatility spillover effects: A Markov switching causality approach

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  • Zheng, Tingguo
  • Zuo, Haomiao

Abstract

This paper intends to examine the volatility spillover effect between selective developed markets including U.S., U.K., Germany, Japan and Hong Kong over the sample period from 1996 to 2011. We introduce a Markov switching causality method to model the potential instability of volatility spillover relationships over market tranquil or turmoil periods. This method is more flexible as no prior information on the changing points or size of sample window is needed. From the empirical results, we find the evidence of the existence of spillover effects among most markets, and the bilateral volatility spillover effects are more prominent over turmoil or crisis episodes, especially during Asia crisis and subprime mortgage crisis periods. Moreover, the distinct role of each market is also investigated.

Suggested Citation

  • Zheng, Tingguo & Zuo, Haomiao, 2013. "Reexamining the time-varying volatility spillover effects: A Markov switching causality approach," The North American Journal of Economics and Finance, Elsevier, vol. 26(C), pages 643-662.
  • Handle: RePEc:eee:ecofin:v:26:y:2013:i:c:p:643-662
    DOI: 10.1016/j.najef.2013.05.001
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    3. Liu, Xueyong & An, Haizhong & Huang, Shupei & Wen, Shaobo, 2017. "The evolution of spillover effects between oil and stock markets across multi-scales using a wavelet-based GARCH–BEKK model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 374-383.
    4. Kangogo, Moses & Volkov, Vladimir, 2022. "Detecting signed spillovers in global financial markets: A Markov-switching approach," International Review of Financial Analysis, Elsevier, vol. 82(C).
    5. Oscar V. De la Torre-Torres & Evaristo Galeana-Figueroa & José Álvarez-García, 2020. "Markov-Switching Stochastic Processes in an Active Trading Algorithm in the Main Latin-American Stock Markets," Mathematics, MDPI, vol. 8(6), pages 1-22, June.
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    7. Usman M. Umer, Metin Coskun, Kasim Kiraci, 2018. "Time-varying Return and Volatility Spillover among EAGLEs Stock Markets: A Multivariate GARCH Analysis," Journal of Finance and Economics Research, Geist Science, Iqra University, Faculty of Business Administration, vol. 3(1), pages 23-42, March.
    8. Balcilar, Mehmet & Gungor, Hasan & Hammoudeh, Shawkat, 2015. "The time-varying causality between spot and futures crude oil prices: A regime switching approach," International Review of Economics & Finance, Elsevier, vol. 40(C), pages 51-71.
    9. Majdoub, Jihed & Mansour, Walid, 2014. "Islamic equity market integration and volatility spillover between emerging and US stock markets," The North American Journal of Economics and Finance, Elsevier, vol. 29(C), pages 452-470.
    10. Oscar V. De la Torre-Torres & Dora Aguilasocho-Montoya & José Álvarez-García, 2019. "Active portfolio management in the Andean countries'' stock markets with Markov-Switching GARCH models," Remef - Revista Mexicana de Economía y Finanzas Nueva Época REMEF (The Mexican Journal of Economics and Finance), Instituto Mexicano de Ejecutivos de Finanzas, IMEF, vol. 14(PNEA), pages 601-616, Agosto 20.
    11. Alotaibi, Abdullah R. & Mishra, Anil V., 2015. "Global and regional volatility spillovers to GCC stock markets," Economic Modelling, Elsevier, vol. 45(C), pages 38-49.
    12. Oscar V. De la Torre-Torres & Evaristo Galeana-Figueroa & María de la Cruz Del Río-Rama & José Álvarez-García, 2022. "Using Markov-Switching Models in US Stocks Optimal Portfolio Selection in a Black–Litterman Context (Part 1)," Mathematics, MDPI, vol. 10(8), pages 1-28, April.
    13. Oscar V. De la Torre-Torres & Dora Aguilasocho-Montoya & María de la Cruz del Río-Rama, 2020. "A Two-Regime Markov-Switching GARCH Active Trading Algorithm for Coffee, Cocoa, and Sugar Futures," Mathematics, MDPI, vol. 8(6), pages 1-19, June.
    14. Oscar V. De la Torre-Torres & Evaristo Galeana-Figueroa & José Álvarez-García, 2019. "A Test of Using Markov-Switching GARCH Models in Oil and Natural Gas Trading," Energies, MDPI, vol. 13(1), pages 1-24, December.
    15. Balcilar, Mehmet & Demirer, Rıza & Hammoudeh, Shawkat, 2014. "What drives herding in oil-rich, developing stock markets? Relative roles of own volatility and global factors," The North American Journal of Economics and Finance, Elsevier, vol. 29(C), pages 418-440.
    16. Chikashi Tsuji, 2016. "Did the expectations channel work? Evidence from quantitative easing in Japan, 2001–06," Cogent Economics & Finance, Taylor & Francis Journals, vol. 4(1), pages 1210996-121, December.
    17. Apergis, Nicholas & Baruník, Jozef & Lau, Marco Chi Keung, 2017. "Good volatility, bad volatility: What drives the asymmetric connectedness of Australian electricity markets?," Energy Economics, Elsevier, vol. 66(C), pages 108-115.

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    More about this item

    Keywords

    Volatility spillover; Markov switching; Granger causality; Range;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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