This article uses Bayesian model averaging to study model uncertainty in hedge fund pricing. We show how to incorporate heteroscedasticity, thus, we develop a framework that jointly accounts for model uncertainty and heteroscedasticity. Relevant risk factors are identified and compared with...

This paper studies hedge fund return predictability in a multivariate setting. Our research design and analysis is motivated by the empirical observations that a specific forecasting model that is going to perform well is not known ex-ante and that modelling time varying return...

This article studies the impact of modeling time varying covariances/correlations of hedge fund returns in terms of hedge fund portfolio construction and risk measurement. We use a variety of static and dynamic covariance/correlation prediction models and compare the optimized portfolios'...

In this paper we suggest a Bayesian approach for inferring stationary autoregressive models allowing for possible structural changes (known as breaks) in both the mean and the error variance of economic series occuring at unknown times. Efficient Bayesian inference for the unknown number and...

Extending previous work on mutual fund pricing, this paper introduces the idea of modeling the conditional distribution of mutual fund returns using a fat tailed density and a time-varying conditional variance. This approach takes into account the stylized facts of mutual fund return series,...

Extending previous work on hedge fund return predictability, this paper introduces the idea of modelling the conditional distribution of hedge fund returns using Student-t full-factor multivariate GARCH models. This class of models takes into account the stylized facts of hedge fund return...