A Total Risk Measurement Framework for Hedge Funds and Funds of Funds

Abstract

This paper illustrates how qualitative analysis can be incorporated into quantitative risk measurement in order to construct an expected distribution of hedge fund returns that explicitly allows for market, residual and tail risk. We show how the combination of statistical criteria with out-of-sample model evaluation techniques, coupled with a qualitative understanding of the particular hedge fund strategy can lead to more robust risk factor models that capture the out-of-sample rather than the historical variation in hedge fund returns. Using Monte Carlo simulation techniques, that allow the most appropriate data generating process for each risk factor, we proceed to build a market risk based expected distribution of returns which is then adjusted for the presence of residual and tail risk. The residual risk distribution of expected returns is entirely based on the out-of-sample errors of the risk factor model and by using the out-of-sample explanatory power of the model as the weighting parameter we allow the model to ‘self correct’ when the actual returns deviate significantly from the model conditional expected returns. The tail risk distribution of returns and the correlation of these tails are solely based on qualitative analysis. We propose a methodology for the quantification of the potential impact of factors such as leverage, liquidity and concentration on the size and probability of excess losses due to tail risks. The proposed framework allows investors to explicitly measure, monitor and manage the modelable and non-modelable risks in a hedge fund portfolio.