This paper aims at providing an in-depth analysis of forecasting ability of different GARCH (Generalized Autoregressive Conditional Heteroskedasticity) models and find ing the best GARCH model for VaR estimation for crude oil. Analysis of VaR forecasting performance of different GARCH models is done using Kupiecs POF test, Christoffersens test and Backtesting VaR Loss Function. Crude oil is one of the most important fuel sources and has contributed to over a third of the world’s energy consumption. Oil shocks have influence on macroeconomic activities through various ways. Sharp oil price changes delay business investment because they raise uncertainty thus reducing aggregate output for some time. Analysis of crude oil prices trends is instrumental in informing the economy’s policy and decision making. 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. The study uses Brent Crude Oil prices data over a period of ten years from the year 2011 to 2020. The study finds that the IGARCH T - distribution model is the best model out of the five models for VaR estimation based on LR.uc Statistic (0.235) and LR.cc Statistic (0.317) which are the least among the values realized. ME and RMSE for the five models used for forecasting have negligible difference. However, the IGARCH model stands out with IGARCH T-distribution being the best out of the five models in this study with ME of 0.0000963591 and RMSE of 0.05304335. We therefore conclude that the IGARCH T - distribution model is the best model out of the five models used in this study for forecasting Brent crude oil price volatility as well as for VaR estimations.
KeywordsGARCH ModelVolatilityValue at RiskModelling and ForecastingBrent Crude OilBacktesting
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