price volatility, and compares their forecasting performance to the standard GARCH, fractionally integrated GARCH (FIGARCH … criteria and forecast horizons, while MS-GARCH mostly comes out as the least successful model. Applying various VaR backtesting …) and the two-state Markov-switching GARCH (MS-GARCH) models via three loss functions (the mean squared error, the mean …

The paper examines the volatility predictive ability of the CBOE crude oil volatility index (OVX), GARCH and Stochastic …. The GJR-GARCH model outperforms its competition volatility models in both oil markets. It also reveals that WTI exhibits a … Volatility Models in the crude oil market. Specifically, the dynamics of two major crude oil pricing benchmarks - Brent in Europe …

In this paper, we study the methods of combining different volatility forecasts using various GARCH models. Given that … the major risk exposure for many investors in energy is the volatility of the electricity price, our motivation stems from … the fact that there is no single best model for forecasting such volatility. Ample evidence suggests that most of the …

price volatility. To address this issue, we find a phenomenon, "momentum of jumps" (MoJ), that the predictive ability of the … jump component is persistent when forecasting the oil futures market volatility. Specifically, we propose a strategy that … according to their recent past forecasting performance. The volatility data are based on the intraday prices of West Texas …

heteroscedasticity (GARCH)-type models to forecast oil price volatility over the time periods from January 02, 1875 to December 31, 1895 … indicate that none of our volatility models can uniformly outperform other models across all six different loss functions … outperformed by other models, with long memory GARCH-type models coming out second best. …