A Markov switching long memory model of crude oil price return volatility

Silvestro Di Sanzo

Energy Economics, 2018, vol. 74, issue C, 351-359

Abstract: I propose a time series model that simultaneously captures long memory and Markov switching dynamics to analyze and forecast oil price return volatility. I compare the fit and forecasting performance of the model to that of a range of linear and nonlinear GARCH models widely adopted in the literature. Complexity-penalized likelihood criteria show that the Markov switching long memory model improves the description of the data. The out-of-sample results at several time horizons show that the model produces superior forecasts over those obtained from the selected GARCH competitors. Results are obtained using Patton's robust loss functions and the Hansen's superior predictive ability test. I conclude that the proposed model provides a useful alternative to the usually employed GARCH models.

Keywords: Crude oil volatility; Long memory; Markov switching; GARCH modelling; Volatility forecast (search for similar items in EconPapers)
JEL-codes: C32 C52 Q47 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (24)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:74:y:2018:i:c:p:351-359

DOI: 10.1016/j.eneco.2018.06.015

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