The article focuses on forecasting idiosyncratic hedge fund return volatility using a non-linear Markov switching GARCH (MS-GARCH) framework in which the conditional mean and volatility of systematic and idiosyncratic hedge fund return components may exhibit dynamic Markov switching behaviour....

This study establishes survival/hazard models with time-varying covariates, as well as fixed covariates, under three specifications of the Cox Proportional Hazards (CPH) model for Hedge Fund (HFs) and Funds-of-Hedge Funds (FOHFs). With the development of the SAS Macro program for generating...

This paper develops a unified approach to comprehensively analyse individual hedge fund return predictability, both in- and out-of-sample. In-sample, we find that variation in hedge fund performance across changing market conditions is widespread and economically significant. The predictability...

We show that fund-specific return skewness is associated with managerial skill and future hedge fund performance. Specifically, skewness in fund returns reflects managerial skill in avoiding large drawdowns. Using a new measure of investment skill that accounts for this managerial ability, we...

We apply four machine learning methods to cross-sectional return prediction for hedge fund selection. We equip the forecast model with a set of idiosyncratic features, which are derived from historical returns of a hedge fund and capture a variety of fund-specific information. Evaluating the...