Extreme Value Theory and Copula Theory: A Risk Management Application with Energy Futures

Deregulation of the energy market and surging trading activities have made the energy markets even more volatile in recent years. Under such circumstances, it becomes increasingly important to assess the probability of rare and extreme price movement in the risk management of energy futures. Similar to other financial time series, energy futures exhibit time varying volatility and fat tails. An appropriate risk measurement of energy futures should be able to capture these two features of the returns. In the first portion of this dissertation, we use the conditional Extreme Value Theory model to estimate Value-at-Risk (VaR) and Expected Shortfall (ES) for long and short trading positions in the energy markets. The statistical tests on the backtests show that this approach provides a significant improvement over the widely used Normal distribution based VaR and ES models.;In the second portion of this dissertation, we extend our analysis from a single security to a portfolio of energy futures. In recent years, commodity futures have gained tremendous popularity as many investors believe they provide much needed diversification to their portfolios. In order to properly account for any diversification benefits, we employ a time-varying conditional bivariate copula approach to model the dependence structure between energy futures. In contrast to previous studies on the same subject, we introduce fundamental supply and demand factors into the copula models to study the dependence structure between energy futures. We find that energy futures are more likely to move together during down markets than up markets.;In the third part of this dissertation, we extend our study of bivariate copula models to multivariate copula theory. We employ a pair-copula approach to estimate VaR and ES of a portfolio consisting of energy futures, the S&P 500 index and the US Dollar index. Our empirical results show that although the pair copula approach does not offer any added advantage in VaR and ES estimation over a long backtest horizon, it provides much more accurate estimates of risk during the period of high co-dependence among assets after the recent financial crisis.