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Dynamics of Crude Oil Volatility: An Application of Markov-Switching GARCH Models

Muhammad Ishfaaq Khodabocus, Jason Narsoo


Firstly, this study assesses the efficiency of several volatility estimators with respect to WTI crude oil returns. It is found that the Rogers-Satchell volatility estimator is the most efficient among the different volatility estimators considered. Secondly, this study provides an extensive and systematic evaluation of the relative forecasting performance of the Markov Regime-Switching GARCH model and different single regime GARCH models for the volatility of the daily spot crude oil prices. The volatility forecasting performance of GARCH(1,1), EGARCH(1,1) and MS-GARCH(1,1), using the normal distribution, Student's-t distribution and Generalized Error Distribution for each model, were compared over the weekly, monthly and quarterly horizons. It was found that EGARCH models performed better for the weekly horizon while for the longer horizons, the MS-GARCH models performed better. Also, for a quarterly horizon, it was found that the MS-GARCH-t was the model providing the best volatility forecasts while for other horizons, no single model emerged as the best one.


GARCH-type models, Hybrid model, Markov-Switching GARCH, Innovation distribution, Volatility, Crude Oil

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