FORECASTING DEPENDENT TAIL VALUE-AT-RISK BY ARMA-GJR-GARCH-COPULA METHOD AND ITS APPLICATION IN ENERGY RISK

One widely known risk measure is Tail Value-at-Risk (TVaR), which is the average of the values of random risk that exceed the Value-at-Risk (VaR). This classic risk measure of TVaR does not take into account the excess of another random risk (associated risk) that may have an effect on target risk. Copula function expresses a methodology that represents the dependence structure of random variables and has been used to create a risk measure of Dependent Tail Value-at-Risk (DTVaR). Incorporating copula into the forecast function of the ARMA-GJR-GARCH model, this article argues a novel approach, called ARMA-GJR-GARCH-copula with Monte Carlo method, to calculate the DTVaR of dependent energy risks. This work shows an implementation of the ARMA-GJR-GARCH-copula model in forecasting the DTVaR of energy risks of NYH Gasoline and Heating oil associated with energy risk of WTI Crude oil. The empirical results demonstrate that, the simpler GARCH-Clayton copula is better in forecasting DTVaR of Gasoline energy risk than the MA-GJR-GARCH-Clayton copula. On the other hand, the more complicated MA-GJR-GARCH-Frank copula is better in forecasting DTVaR of Heating oil energy risk than the GARCH-Frank copula. In this context, energy sector market players should invest in Heating oil because the DTVaR forecast of Heating oil is more accurate than that of Gasoline.