MODELLING CRUDE OIL PRICE TIME-VARYING VOLATILITY USING JUMP-DIFFUSION MODEL

Abstract

The objective of this paper is to capture the time-varying volatility in crude oil prices. The time-varying volatility dynamics are characterized by high volatility, high intensity jumps, and strong upward drift, indicating that oil markets were constantly out-of-equilibrium. The method of maximum likelihood and cumulants are utilized. The Jump-Diffusion model, generalized autoregressive conditional heteroskedasticity (GARCH) model and autoregressive model of order two (AR(2)) are used to empirically model the crude oil price (January, 1986-July, 2015). The results show that the three models performed well in estimating the crude oil price. However, Jump-Diffusion model out-performed the other two models as it captures the drift as well as the jumps in the crude oil price within the sampled period. The results establish that commodity price risk plays a dominant role in the energy industries, and the use of derivatives has become a common means of helping energy firms, investors and customers to manage risks that arise from the high volatility.