Replication and optimization of hedge fund risk factor exposures

In this paper, we propose a novel approach for decomposing hedge fund returns onto observable risk factors. We utilize a vector stochastic-volatility model to extract the time-varying exposure of low frequency hedge fund returns on high frequency market data. We implement the estimation by using particle filtering and the concept of Rao-Blackwellization. With the latter, we remove all the static parameters of the model and thereby reduce the dimension of the parameter space for particle generation. Thus, we are able to obtain accurate estimates of the posterior distributions of the model states. For our model, this reduction is significant because the number of static parameters is large. We use the proposed model to analyze hedge fund performance and to optimally replicate hedge fund strategies economically. We demonstrate the validity and effectiveness of the method by computer simulations.