Modelling and Forecasting of Crude Oil Price Volatility Comparative Analysis of Volatility Models

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