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Title: Hedge fund return predictability in the presence of model risk Authors:  Christos Argyropoulos - Lancaster University (United Kingdom)
Ekaterini Panopoulou - University of Essex (United Kingdom)
Teng Zheng - Barclays PLC (United Kingdom)
Nikolaos Voukelatos - University of Kent (United Kingdom) [presenting]
Abstract: Hedge funds implement elaborate investment strategies that include a variety of positions and assets. As a result, there is significant time variation in the set of risk factors and their respective loadings which in turn introduces severe model risk in any attempt to model and forecast the hedge fund returns. We investigate the statistical and economic value of incorporating heteroscedasticity, non-normality, time-varying parameters, model selection risk and parameter estimation risk jointly in hedge fund return forecasting and fund of funds construction. Parameter estimation risk is dealt with by a time-varying parameter structure, while model selection uncertainty is mitigated by model averaging or model selection. We adopt a dynamic model averaging approach along with the conventional Bayesian averaging technique. Our empirical results suggest that accounting for model risk can significantly improve the hedge fund returns forecasting accuracy and consequently the performance of the hypothetical fund of funds.