Spectral Risk Measures with an Application to Futures Clearinghouse Variation Margin Requirements

TL;DR

This paper estimates spectral risk measures for major futures contracts using an AR-GARCH model, compares them to traditional risk measures, and evaluates their effectiveness in setting variation margin requirements under changing market conditions.

Contribution

It introduces a method to compute spectral risk measures for futures using AR-GARCH models and compares their performance to VaR and ES in margin setting.

Findings

Spectral risk measures provide a coherent alternative to VaR and ES.

The model's fit is validated through multiple backtests.

Spectral measures better capture time-varying market risks.

Abstract

This paper applies an AR(1)-GARCH (1, 1) process to detail the conditional distributions of the return distributions for the S&P500, FT100, DAX, Hang Seng, and Nikkei225 futures contracts. It then uses the conditional distribution for these contracts to estimate spectral risk measures, which are coherent risk measures that reflect a user's risk-aversion function. It compares these to more familiar VaR and Expected Shortfall (ES) measures of risk, and also compares the precision and discusses the relative usefulness of each of these risk measures in setting variation margins that incorporate time-varying market conditions. The goodness of fit of the model is confirmed by a variety of backtests.