Modelling and Forecasting of Brent Crude Oil Price Volatility

Abstract

This paper aims at providing an in-depth analysis of forecasting ability of different GARCH models and finding the best GARCH model for Value at Risk (VaR) estimation for Brent crude oil. Analysis of VaR forecasting performance of different GARCH models is done using both Kupiecs test and Christoffersens test. Also, Backtesting VaR Loss Function.  Sharp oil price changes delay business investment because they raise uncertainty thus reducing aggregate output. Continued development and improvement of models used in analyzing prices improve forecasting accuracy which in turns leads to better costs and revenue prediction by businesses. This paper uses Brent Crude Oil prices data over a period of ten years from the year 2014 to 2024. The study finds that the IGARCH with T-distribution model is the best model out of the four models for VaR estimation based on LR.uc and LR.cc Statistics which are the least among the values realized. ME and RMSE for the four models used for forecasting have negligible difference. However, the IGARCH model stands out with IGARCH T-distribution being the best out of the four models we used. We therefore conclude that the IGARCH with T-distribution model is the best model out of the four models used in this study for forecasting Brent crude oil price volatility as well as for VaR estimations