Building a Risk Measurement Framework for Hedge Funds and Funds of Funds

In the first of two papers, we present a factor-decomposition based framework that facilitates non-parametric risk analysis for complex hedge fund portfolios in the absence of portfolio level transparency. Our approach has been designed specifically for use within the hedge funds-offunds environment, but is equally relevant to those who seek to construct risk managed portfolios of hedge funds under less than perfect underlying portfolio transparency. Using dynamic multivariate regression analysis coupled with a top-down qualitative understanding of hedge fund return drivers, we are able to perform a robust factor decomposition to attribute risk within any hedge fund portfolio with an identifiable strategy. Furthermore, through use of Bayesian-adjusted correlated Monte Carlo simulation techniques, these factors can be employed to generate implied risk profiles at either the constituent fund or aggregate funds-of-funds level. As well as being pertinent to risk forecasting and monitoring, such methods also have application to style analysis, profit attribution, portfolio stress testing and diversification studies. In this first paper we present the technical foundations of such a framework. The follow-up paper (Part II) will present detailed application of the concepts discussed in Part I to a broad base of hedge fund strategies and funds-of-funds.