Energy portfolio risk management using time-varying extreme value copula methods

• Published in: Economic modelling 2014-02, Vol.38, p.470-485
• Main Authors: Ghorbel, Ahmed, Trabelsi, Abdelwahed
• Format: Article
• Language: English
• Subjects: Commodity markets; Copulas; Energy market; Energy portfolio; Energy resources; Estimation; Extreme value theory; FIGARCH; Oil and gas futures prices; Probability distribution; Risk management; Statistical models; Stochastic models; Studies; Time series; Value-at-Risk; Vector-autoregressive models
• ISSN: 0264-9993; 1873-6122
• DOI: 10.1016/j.econmod.2013.12.023

This work is concerned with the statistical modeling of the dependence structure between three energy commodity markets (WTI crude oil, natural gas and heating oil) using the concept of copulas and proposes a method for estimating the Value at risk (VaR) of energy portfolio based on the combination of time series models with models of the extreme value theory before fitting a copula. Each return series is modeled by AR-(FI) GARCH univariate model. Then, we fit the GPD distribution to the tails of the residuals to model marginal residuals distributions. The extreme value copula to the iid residuals is fitted and we simulate from it to construct N portfolios and estimate VaR. As a first step, the method is applied to a two-dimensional energy portfolio. In second step, we extend method in trivariate context to measure VaR of three-dimensional energy portfolio. Dependences between residuals are modeled using a trivariate nested Gumbel copulas. Methods proposed are compared with various univariate and multivariate conventional VaR methods. The reported results demonstrate that GARCH-t, conditional EVT and FIGARCH extreme value copula methods produce acceptable estimates of risk both for standard and more extreme VaR quantiles. Generally, copula methods are less accurate compared with their predictive performances in the case of portfolio composed of exchange market indices. •Measure VaR of 2 and 3-dimensional energy portfolio using multivariate Conditional EVT nested copulas•Compare this method with traditional univariate and multivariate methods•Superiority of GARCH-t, conditional EVT and FIGARCH extreme value copula methods in forecasting and quantifying VaR•Copula methods are less accurate compared with their predictive performances in the case of assets portfolios.