Oil Price Volatility : GARCH, SVR-GARCH and EVT APPROACH

Abstract

Oil prices have been volatile over the past few years. Several models have been developed to describe volatility but the frequently used models are the ARCH and GARCH models. Research on GARCH and SVR-GARCH models have received little attention for studies on volatility especially in South Africa. This research seeks to assess the effectiveness of GAR CH and SVR-GARCH models in modelling oil price volatility in South Africa. The study further employed EVT to fit and model the tails of oil prices. Daily data was collected from the JSE covering the period 7th August 2008 - 7th August 2018. The period was selected to cover the most recent trends of oil prices for the past 10 years. The study applied GAR CH (1, 1 }, FIGARCH(1,d, 1) , EGARCH(1, 1) and GJR-GARCH(1, 1) and in comparison with SVR­GARCH(1, 1 ), SVR-EGARCH(1, 1) ,SVR-GJR-GARCH(1, 1 ), SVR-FIGARCH(1,d, 1) to model Brent Crude oil Prices in South Africa. Preliminary data analysis was conducted before the actual analysis to quantify the behaviour of oil prices. The results indicated that Brent crude oil prices are heteroscedastic and auto correlated; hence the GARCH models are applicable. A detailed analysis of GAR CH and SVR­GARCH was given. The study found SVR-EGARCH (1, 1) superior to the GARCH models. For the GARCH models, EGARCH (1, 1) was the best. EVT was used to fit the tails of the returns. The study fitted EGAR CH (1, 1) and SVR-EGARCH (1, 1 ). The POT (Peak over threshold) method was employed in evaluating the GPD exceedances. The results showed that GPD fits adequately well and is sufficient in estimating tail risks. The study recommends the use of SVR-EGARCH (1, 1) model as it is superior to EGARCH (1, 1 ). Multivariate data sets should be used for future studies. In addition, Stochastic Volatility models should be compared with the Support Vector Regression-GARCH models.