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Regime-Switched Volatility Of Brent Crude Oil Futures With Markov-Switching Arch Model

Author

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  • TIEN-YU CHIU

    (Department of International Business, College of Commerce, National Cheng-Chi University, Taipei, Taiwan)

  • SHWU-JANE SHIEH

    (Department of International Business, College of Commerce, National Cheng-Chi University, Taipei, Taiwan)

Abstract

This paper investigates the volatility process of the Brent crude oil futures markets using Markov-switching ARCH (SWARCH) model. The SWARCH model allows the conditional disturbances to change as time passes and even to switch in different regimes. The empirical evidence shows that the SWARCH (3,3) model performs the best goodness of fit and the best forecast performance among different fitting models. The estimation of smoothing probabilities of data under different regimes facilitates to capture the characteristics of the data, and the high-volatility regime is associated with some extraordinary events, such as the 1990's Persian Gulf War, the 1997's Asia Financial Crisis, and the 2001's 911 terrorist attack.

Suggested Citation

  • Tien-Yu Chiu & Shwu-Jane Shieh, 2009. "Regime-Switched Volatility Of Brent Crude Oil Futures With Markov-Switching Arch Model," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 12(02), pages 113-124.
    • Handle: RePEc:wsi:ijtafx:v:12:y:2009:i:02:n:s021902490900521x
    DOI: 10.1142/S021902490900521X
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    References listed on IDEAS

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    1. Pagano Patrizio & Pisani Massimiliano, 2009. "Risk-Adjusted Forecasts of Oil Prices," The B.E. Journal of Macroeconomics, De Gruyter, vol. 9(1), pages 1-28, June.
    2. Mr. Manmohan S. Kumar, 1991. "Forecasting Accuracy of Crude Oil Futures Prices," IMF Working Papers 1991/093, International Monetary Fund.
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    Cited by:

    1. Zhiqiang HU & Yizhu WANG, 2013. "The IPO Cycles in China's A-share IPO Market: Detection Based on a Three Regimes Markov Switching Model," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 115-131, October.

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