Modelling Energy Market Volatility Using Garch Models And Estimating Value-At-Risk

Abstract

Purpose: The study focused on modelling the volatility of energy markets spot prices using GARCH models and estimating Value-at-Risk. Methodology: The conditional heteroscedasticity models are used to model the volatility of gasoline and crude oil energy commodities. In estimating Value at Risk; GARCH-EVT model is utilized in comparison with other conventional approaches. The accuracy of the VaR forecasts is assessed by using standard statistical back testing procedures. Results: The empirical results suggests that the gasoline and crude oil prices exhibit highly stylized features such as extreme price spikes, price dependency between markets, correlation asymmetry and non-linear dependency. We also conclude that the EGARCH-EVT model is more robust, provides the best t and outperforms the other conventional models in terms of forecasting accuracy and VaR prediction. Generally, the GARCH-EVT model can be used to plays an integral role as a risk management tool in the energy industry. Unique contribution to theory, practice and policy: In light of the research findings, the study recommends that organizations should leverage modern technology as a basis of realizing efficiency, effectiveness, and sustainability of projects. The study likewise recommends that organizations should build capacities to enhance labour productivity. In addition, the study recommends that organizations should adopt transformational leadership approaches as a basis of enhancing performance. The study recommends the need to revise the legal framework with a view to ensure that it reflects the changing needs of the project requirements. Keywords: Back testing, extreme value theory (EVT), Peak-over-threshold (POT), GARCH-EVT model, Value-at-Risk (VaR).