Forecasting the Crude Oil Prices Volatility With Stochastic Volatility Models

Abstract

In this article, the stochastic volatility model is introduced to forecast crude oil volatility by using data from the West Texas Intermediate (WTI) and Brent markets. Not only that the model can capture stylized facts of multiskilling, extended memory, and structural breaks in volatility, it is also more frugal in parameterizations. The Euler–Maruyama scheme was applied to approximate the Heston model. On the contrary, the root mean square error (RMSE) and the mean average error (MAE) were used to approximate the generalized autoregressive conditional heteroskedasticity (GARCH)–type models (symmetric and asymmetric). Based on the approximation results obtained, the study established that the stochastic volatility model fits oil return data better than the traditional GARCH-class models.