Abstract
Over the past decade, crude oil prices have risen dramatically, making the oil market very volatile and risky; hence, implementing an efficient risk management tool against market risk is crucial. Value-at-risk (VaR) has become the most common tool in this context to quantify market risk. Financial data typically have certain features such as volatility clustering, asymmetry, and heavy and semi-heavy tails, making it hard, if not impossible, to model them by using a normal distribution. In this paper, we propose the subclasses of the generalised hyperbolic distributions (GHDs), as appropriate models for capturing these characteristics for the crude oil and gasoline returns. We also introduce the new subclass of GHDs, namely normal reciprocal inverse Gaussian distribution (NRIG), in evaluating the VaR for the crude oil and gasoline market. Furthermore, VaR estimation and backtesting procedures using the Kupiec likelihood ratio test are conducted to test the extreme tails of these models. The main findings from the Kupiec likelihood test statistics suggest that the best GHD model should be chosen at various VaR levels. Thus, the final results of this research allow risk managers, financial analysts, and energy market academics to be flexible in choosing a robust risk quantification model for crude oil and gasoline returns at their specific VaR levels of interest. Particularly for NRIG, the results suggest that a better VaR estimation is provided at the long positions.
Highlights
Crude oil is among the most important energy commodities in the world and forms the basis for many products, including transport fuels such as gasoline, diesel and jet fuel
This section first describes the characteristics of the energy returns and the robust VaR models are selected for each returns series
We examine the relative performance of the GARCH models combined with generalised hyperbolic distributions (GHDs) models with regard to evaluation and forecasting VaR in energy market
Summary
Crude oil is among the most important energy commodities in the world and forms the basis for many products, including transport fuels such as gasoline, diesel and jet fuel. Demand and supply are among the main drivers of crude oil prices. Political events, extreme weather, and speculation in the financial markets are, among others, main factors of the crude oil market, increasing the degree of price volatility in the oil markets. The volatility of the energy commodity price does have a significant macroeconomic impact, but it has an impact on stock markets Sadorsky (1999, 2003). Quantifying risk for oil-related commodities is very important to economic agents and policymakers, it still remains one of the most challenging issues because of many fluctuations over time.
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