A Bayesian Approach to Detecting Nonlinear Risk Exposures in Hedge Fund Strategies

Abstract:     
This paper proposes a model that allows for nonlinear risk exposures of hedge funds to various risk factors. A flexible threshold regression model is introduced and a Bayesian approach is developed for model selection and estimation of the thresholds and their unknown number. Relevant risk factors and/or threshold values are identified through a computationally flexible Markov chain Monta Carlo stochastic search algorithm. Our analysis of several hedge fund returns reveals that different strategies exhibit nonlinear relations to different risk factors. We also explore potential economic impacts of our approach by analysing hedge fund strategy return series and by constructing style portfolios.

Number of Pages in PDF File: 27

Keywords: Hedge Funds, GARCH, MCMC methods, Model uncertainty, Risk factors, Style portfolio construction

JEL Classification: G11, G12, C11

working papers series