Oil Price Volatility Forecast with Mixture Memory GARCH

Abstract

First Version: 03/11/2015This Version: 04/01/2016We expand the literature of volatility and Value-at-Risk forecasting of oil price returns by comparing the recently proposed Mixture Memory GARCH (MMGARCH) model to other discrete volatility models (GARCH, FIGARCH, and HYGARCH). We incorporate an Expectation-Maximization algorithm for parameter estimation of the MMGARCH and find regimes that differ in volatility level as well as shock persistence. Furthermore, we observe dissimilar memory structure in variance of WTI and Brent crude oil prices which is confirmed by altering the mixture components. In regard of variance forecasting and Value-at-Risk prediction, we show that MMGARCH outperforms the aforementioned models due to its dynamic approach in varying the volatility level and memory of the process. We find MMGARCH superior for application in risk management as a result of its flexibility in adjusting to variance shifts and shocks.