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Markov Regime Switching Generalized Autoregressive Conditional Heteroskedastic Model and Volatility Modeling for Oil Returns

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  • Samet G nay

    (Department of Finance, Business School, American University of the Middle East, Kuwait.)

Abstract

In conjunction with the recent alternative models, a wide literature has been established for volatility modeling in finance theory. In this study, we examine return volatility of Brent oil returns through generalized autoregressive conditional heteroskedastic (GARCH), exponential GARCH, Glosten-Jagannathan-Runkle GARCH and Markov regime-switching GARCH (MRS-GARCH) models. As a preliminary test concerning the potential regimes, first, we use modified iterative cumulative sum of squares test in order to examine the existence of breaks in the variance of return series. All volatility models are formed under normal, generalized error distribution and Student s t distributions. According to the Akaike information criterion and Bayesian information criterion values, MRS-GARCH model outperforms all other alternative models. Another interesting result is the failure of the models that formed under normal distribution.

Suggested Citation

  • Samet G nay, 2015. "Markov Regime Switching Generalized Autoregressive Conditional Heteroskedastic Model and Volatility Modeling for Oil Returns," International Journal of Energy Economics and Policy, Econjournals, vol. 5(4), pages 979-985.
  • Handle: RePEc:eco:journ2:2015-04-09
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    References listed on IDEAS

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    Cited by:

    1. Halkos, George & Tzirivis, Apostolos, 2018. "Effective energy commodities’ risk management: Econometric modeling of price volatility," MPRA Paper 90781, University Library of Munich, Germany.

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    More about this item

    Keywords

    Markov Regime Switching Generalized Autoregressive Conditional Heteroskedastic; Oil Volatility; Variance Breaks;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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