Risk modeling in crude oil market: a comparison of Markov switching and GARCH models

– The purpose of this paper is to deal with the different phases of volatility behavior and the dependence of the variability of the time series on its own past, models allowing for heteroscedasticity like autoregressive conditional heteroscedasticity (ARCH), generalized autoregressive conditional heteroscedasticity (GARCH), or regime‐switching models have been suggested by reserachers. Both types of models are widely used in practice., – Both regime‐switching models and GARCH are used in this paper to model and explain the behavior of crude oil prices in order to forecast their volatility. In regime‐switching models, the oil return volatility has a dynamic process whose mean is subject to shifts, which is governed by a two‐state first‐order Markov process., – The GARCH models are found to be very useful in modeling a unique stochastic process with conditional variance; regime‐switching models have the advantage of dividing the observed stochastic behavior of a time series into several separate phases with different underlying stochastic processes., – The regime‐switching models show similar goodness‐of‐fit result to GARCH modeling, while has the advantage of capturing major events affecting the oil market. Daily data of crude oil prices are used from NYMEX Crude Oil market for the period 13 February 2006 up to 21 July 2009.

[1]  Paolo Agnolucci,et al.  Volatility in crude oil futures: A comparison of the predictive ability of GARCH and implied volatility models , 2009 .

[2]  Robert W. Rich,et al.  Oil and the Macroeconomy: A Markov State-Switching Approach , 1997 .

[3]  Matteo Manera,et al.  The Asymmetric Effects of Oil Shocks on Output Growth: A Markov-Switching Analysis for the G-7 Countries , 2006 .

[4]  Franc J. G. M. Klaassen,et al.  Improving GARCH volatility forecasts with regime-switching GARCH , 2002 .

[5]  Arti Prasad,et al.  Understanding the oil price-exchange rate nexus for the Fiji islands , 2008 .

[6]  T. Bollerslev,et al.  Generalized autoregressive conditional heteroskedasticity , 1986 .

[7]  Rania Jammazi,et al.  The effects of crude oil shocks on stock market shifts behaviour: A regime switching approach , 2009 .

[8]  E. Fama.  EFFICIENT CAPITAL MARKETS: A REVIEW OF THEORY AND EMPIRICAL WORK* , 1970 .

[9]  Amir H. Alizadeh,et al.  A Markov regime switching approach for hedging energy commodities , 2008 .

[10]  Denis Cousineau,et al.  Fitting distributions using maximum likelihood: Methods and packages , 2004, Behavior research methods, instruments, & computers : a journal of the Psychonomic Society, Inc.

[11]  Yi-Ming Wei,et al.  Estimating ‘Value at Risk’ of crude oil price and its spillover effect using the GED-GARCH approach , 2008 .

[12]  James D. Hamilton.  A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle , 1989 .

[13]  Jui-Cheng Hung,et al.  Estimation of value-at-risk for energy commodities via fat-tailed GARCH models , 2008 .

[14]  Minh-Vuong Vo,et al.  Regime-switching stochastic volatility: Evidence from the crude oil market , 2009 .

[15]  Bradley T. Ewing,et al.  Volatility transmission between oil prices and equity sector returns , 2009 .

[16]  C. Engel.  Can the Markov Switching Model Forecast Exchange Rates? , 1992 .

[17]  Robert Bacon,et al.  Coping with Oil Price Volatility , 2008 .

[18]  Daniel B. Nelson.  CONDITIONAL HETEROSKEDASTICITY IN ASSET RETURNS: A NEW APPROACH , 1991 .

[19]  Chaker Aloui,et al.  Value-at-risk estimations of energy commodities via long-memory, asymmetry and fat-tailed GARCH models , 2010 .

[20]  R. Engle.  Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation , 1982 .

[21]  J. Wooldridge,et al.  Quasi-maximum likelihood estimation and inference in dynamic models with time-varying covariances , 1992 .