Modelling and forecasting of crude oil price volatility: comparative analysis of volatility models
Ng’ang’a, Faith Wacuka
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This study aims at providing an in-depth analysis of forecasting ability of different GARCH models and to find the best GARCH model for VaR estimation for crude oil. The VaR forecasting performance of GARCH-type models are analyzed and compared in a long horizon; based on the Kupiecs POF-test and Christoffersens interval forecast test as well as a 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. Modelling and forecasting of crude oil prices plays a significant role in supporting policy and decision making in the economy. Successive developments of models used provide opportunities to analyse crude oil market in depth and improve the accuracy of oil price forecasting. 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 IGARCHT 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.