Quantile Regression Analysis of Hedge Fund Strategies

Abstract:     
Extending previous work on hedge fund pricing, this paper introduces the idea of modelling the conditional quantiles of hedge fund returns using a set of risk factors. Quantile regression analysis provides a way of understanding how the relationship between hedge fund returns and risk factors changes across the distribution of conditional returns. We propose a Bayesian approach to model comparison which provides posterior probabilities for different risk factor models. The most relevant risk factors are identified for different quantiles and compared with those obtained for the conditional expectation model. We find evidence of model uncertainty in quantile regression models and evidence that different risk factors affect differently the tails of the distribution of hedge fund returns. We explore potential economic impacts of our approach by analysing hedge fund strategies return series and by constructing style portfolios.

Number of Pages in PDF File: 37

Keywords: Conditional quantiles, Model selection techniques, Model uncertainty, Hedge funds, Bayesian model avereging, Risk factors, Style portfolio construction

JEL Classification: G11, G12, C11

working papers series