Risk Management Study for Managed Futures

This paper describes some statistical aspects to implement the Value at Risk (VaR) for a Fund which is actively trading the Euro Bund Future(FGBL). First, we describe FGBL instrument and review the FGBL statistical properties. As it is usually the case for financial series, the distribution of FGBL returns exhibits fat tails. Dierent VaR models are estimated: normal VaR, historical VaR, Cornish-Fisher approximation, Extreme Value Theory, Pareto fitting, volatility models (RiskMetrics, GARCH). The Conditional Value at Risk (CVaR) is also studied. VaR is blind on actual losses beyond the VaR. The CVaR defined as the expected losses in case the VaR is exceeded. CVaR is also a more consistent risk measure while VaR is not. In presence of fat tails, the CVaR will lead to less risky and more consistent positions. The dierent models will lead to dierent values of risk measures (Var, CVaR, Maximum Drawdowns). The dierences between estimated values can be interpreted as a model risk that is minimized by taking the worst case over all models.