Oil tail-risk forecasts: from financial crisis to COVID-19

The coronavirus outbreak has caused unprecedented volatility in oil prices. This paper extends previous studies on oil Value-at-Risk (VaR) by providing extra insights into Expected Shortfall (ES) forecasting over the last decade, including several oil crises. We introduce a conditional volatility model combined with the Cornish-Fisher expansion for ES forecasting. In comparison to the widely used volatility models and innovation distributions, this approach is superior for predicting the ES of long positions but overestimates VaR for short positions. Overall, the volatility model addressing leverage effects with skewed t innovation produces the most accurate joint VaR and ES forecasting. Moreover, the magnitude of ES relative to VaR varies across models and time, implying that ES should be used in conjunction with VaR to inform timely risk management decisions. The results would be of interest to the regulatory authorities, energy companies, and financial institutions for oil tail-risk forecasting.

Oil value-at-risk forecasts: a filtered semiparametric approach

The Covid-19 pandemic has set the stage for greater volatility in oil prices. Given this unprecedentedly volatile environment, protection against market risk has never been more important. Value-at-risk (VaR) is a popular metric to measure and control risk. However, the widely used historical simulation approach is unresponsive to upticks in stress. Therefore, the need has arisen for an alternative method that is easy to implement while still achieving forecast accuracy. We propose the generalized autoregressive conditional heteroscedasticity (GARCH) model combined with the Cornish–Fisher expansion (a semiparametric approach to address skewness and excess kurtosis as well as volatility dynamics) for the oil VaR forecast. We com-pare the performance of the proposed approach with that of historical simulation and GARCH-type models with alternative residual distributions: historical simulation, normal, skewed Student t and generalized Pareto. The study is based on the daily spot data from the Energy Information Administration for the period from December 19, 2012 to October 30, 2020 for Brent and from November 13, 2012 to October 30, 2020 for West Texas Intermediate, each with a total of 2001 observations. We find that the historical simulation approach significantly underestimates the risks for both long and short positions during the recent market turmoil, which confirms the importance of the filtering process in VaR forecasts. Moreover, the proposed approach provides the most accurate VaR forecasts, especially at high confidence levels for the long position. The analysis serves as a useful guide to energy market risk quantification for practitioners and policy makers. © Infopro Digital Limited 2022. All rights reserved.