The use of parametric GARCH models to characterise crude oil price volatility is widely
observed in the empirical literature. In this paper, we consider an alternative approach …

This paper extends the work of Kang et al.(2009). We use a greater number of linear and
nonlinear generalized autoregressive conditional heteroskedasticity (GARCH) class models …

GARCH-type models are frequently used to forecast crude oil price volatility, and whether
we should consider multiple regimes for the GARCH-type models is of great significance for …

Forecasts of oil price volatility are important inputs into macroeconometric models, financial
market risk assessment calculations like value at risk, and option pricing formulas for futures …

We examine the usefulness of several ARIMA-GARCH models for modeling and forecasting
the conditional mean and volatility of weekly crude oil spot prices in eleven international …

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 …

We use high-frequency intra-day realized volatility data to evaluate the relative forecasting
performances of various models that are used commonly for forecasting the volatility of …

We compare the forecasting performance of the generalized autoregressive conditional
heteroscedasticity (GARCH)-type models with support vector regression (SVR) for futures …

This article studies the forecasting properties of linear GARCH models for closing-day
futures prices on crude oil, first position, traded in the New York Mercantile Exchange from …

Modelling and forecasting of crude oil volatility have been widely examined using GARCH-
type models, and evidence suggests the presence of time-varying jumps in the crude oil …