TITLE:
Modelling and Forecasting of Crude Oil Price Volatility Comparative Analysis of Volatility Models
AUTHORS:
Faith Wacuka Ng’ang’a, Meleah Oleche
KEYWORDS:
GARCH Model, Volatility, Value at Risk, Modelling and Forecasting, Brent Crude Oil, Backtesting
JOURNAL NAME:
Journal of Financial Risk Management,
Vol.11 No.1,
March
15,
2022
ABSTRACT: This paper aims at providing an in-depth analysis of forecasting ability
of different GARCH (Generalized Autoregressive Conditional Heteroskedasticity)
models and finding 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.