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Regime-switching stochastic volatility: Evidence from the crude oil market

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  • Vo, Minh T.
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    Abstract

    This paper incorporates regime-switching into the stochastic volatility (SV) framework in an attempt to explain the behavior of crude oil prices in order to forecast their volatility. More specifically, it models the volatility of oil return as a stochastic volatility process whose mean is subject to shifts in regime. The shift is governed by a two-state first-order Markov process. The Bayesian Markov Chain Monte Carlo method is used to estimate the models. The main findings are: first, there is clear evidence of regime-switching in the oil market. Ignoring it will lead to a false impression that the volatility is highly persistent and therefore highly predictable. Second, incorporating regime-switching into the SV framework significantly enhances the forecasting power of the SV model. Third, the regime-switching stochastic volatility model does a good job in capturing major events affecting the oil market.

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    Bibliographic Info

    Article provided by Elsevier in its journal Energy Economics.

    Volume (Year): 31 (2009)
    Issue (Month): 5 (September)
    Pages: 779-788

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    Handle: RePEc:eee:eneeco:v:31:y:2009:i:5:p:779-788

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    Web page: http://www.elsevier.com/locate/eneco

    Related research

    Keywords: Crude oil price Regime switching Stochastic volatility MCMC method Forecasting;

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    Cited by:
    1. Amélie Charles & Olivier Darné, 2014. "Volatility persistence in crude oil markets," Post-Print hal-00940312, HAL.
    2. Xu, Haiyan & Zhang, ZhongXiang, 2010. "A trend deduction model of fluctuating oil prices," MPRA Paper 26947, University Library of Munich, Germany, revised 17 Nov 2010.
    3. Xu, Bing & Ouenniche, Jamal, 2012. "A data envelopment analysis-based framework for the relative performance evaluation of competing crude oil prices' volatility forecasting models," Energy Economics, Elsevier, vol. 34(2), pages 576-583.
    4. Moez Khalfallah & Bruno-Laurent Moschetto & Frédéric Teulon, 2014. "Evaluation of the profitability of companies financed by venture capital (CVC) listed on the French market," Working Papers 2014-085, Department of Research, Ipag Business School.
    5. Liu, Li & Wan, Jieqiu, 2012. "A study of Shanghai fuel oil futures price volatility based on high frequency data: Long-range dependence, modeling and forecasting," Economic Modelling, Elsevier, vol. 29(6), pages 2245-2253.
    6. Gronwald, Marc, 2012. "A characterization of oil price behavior — Evidence from jump models," Energy Economics, Elsevier, vol. 34(5), pages 1310-1317.
    7. Afees A. Salisu & Ismail O. Fasanya, 2012. "Comparative Performance of Volatility Models for Oil Price," International Journal of Energy Economics and Policy, Econjournals, vol. 2(3), pages 167-183.
    8. Larsson, Karl & Nossman, Marcus, 2011. "Jumps and stochastic volatility in oil prices: Time series evidence," Energy Economics, Elsevier, vol. 33(3), pages 504-514, May.
    9. Vo, Minh, 2011. "Oil and stock market volatility: A multivariate stochastic volatility perspective," Energy Economics, Elsevier, vol. 33(5), pages 956-965, September.
    10. Rania Jammazi & Duc Khuong Nguyen, 2014. "Responses of international stock markets to oil price surges: a regimeswitching perspective," Working Papers 2014-080, Department of Research, Ipag Business School.
    11. Kang, Sang Hoon & Cheong, Chongcheul & Yoon, Seong-Min, 2011. "Structural changes and volatility transmission in crude oil markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(23), pages 4317-4324.
    12. Lin, Boqiang & Wesseh, Presley K. & Appiah, Michael Owusu, 2014. "Oil price fluctuation, volatility spillover and the Ghanaian equity market: Implication for portfolio management and hedging effectiveness," Energy Economics, Elsevier, vol. 42(C), pages 172-182.
    13. Chang, Kuang-Liang, 2012. "Volatility regimes, asymmetric basis effects and forecasting performance: An empirical investigation of the WTI crude oil futures market," Energy Economics, Elsevier, vol. 34(1), pages 294-306.
    14. Wang, Yudong & Wu, Chongfeng, 2012. "Forecasting energy market volatility using GARCH models: Can multivariate models beat univariate models?," Energy Economics, Elsevier, vol. 34(6), pages 2167-2181.
    15. Nomikos, Nikos K. & Pouliasis, Panos K., 2011. "Forecasting petroleum futures markets volatility: The role of regimes and market conditions," Energy Economics, Elsevier, vol. 33(2), pages 321-337, March.

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