Conditional and Unconditional Risk Management Estimates for European Stock Index Futures

Accurate forecasting of risk is the key to successful risk management techniques. Correct modelling of a variable’s extreme values located at the distributional tails accounting for the fat-tail phenomena is paramount, and this paper presents an overview of a theoretically and statistically robust approach to this problem. Underpinned by Extreme Value Theory that explicitly allows for fat-tailed densities, this paper presents four precise measures of downside risk. Two Value at Risk and two Excess Loss Probability estimators evolve from the conditional and unconditional distributions. Fitting an AR(1)-GARCH(1, 1) filter provides the description of the current volatility climate. Results are presented for a panorama of twelve stock index futures traded on European bourses.