Role of Trading Volume on the Estimation of Dynamic Extreme Value-at-Risk in Futures Markets

Abstract

The dynamic EVT-based GARCH model has evolved as a preferred approach in the estimation of value-at-risk (VaR), in global financial institutions. Sophisticated risk models also require full information, however, the traditional standard dynamic VaR model failed to account for an important nature of return volatility driven by asymmetric volume changes in the financial markets. The main objective of this study is to investigate whether an incorporation of trading volume improve the accuracy in the estimation of VaR in future markets. Using alternative dynamic EVT-based GARCH family VaR models including GARCH, GJR and EGARCH, over the period from Jan. 1997 to Dec. 2001, the study examine VaRs of three major US futures markets, NASDAQ INDEX, S&P 500 INDEX and NATURAL GAS. Consistent with our a-priori expectation, the finding indicates that the proposed alternative dynamic EVT-based GARCH family VaR models with volumes, in general, outperform traditional dynamic EVT-based VaR models. In particular, GJR+GPD+V is the best model among the others in terms of both rate of violation and RMSE.