Hedge fund returns

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This work advances research on hedge fund returns in three key areas. First, it assesses the statistical properties of this alternative asset class, examining the extent of non-normality, autocorrelation, and heteroscedasticity in returns. Second, it employs advanced econometric methods to analyze the forecastability of monthly hedge fund returns. Third, it identifies and explains the economic risks impacting the performance of various hedge fund strategy styles. The empirical findings indicate that monthly hedge fund returns can be forecasted using multivariate regression models that incorporate economic predictors, such as interest rate changes and business outlook shifts. The econometric models account for the non-normal distribution, heteroscedasticity, and time-varying exposure to risk factors, demonstrating significant out-of-sample predictive power. Moreover, the study reveals that the relationships between monthly changes in risk factors and subsequent hedge fund returns remain stable over time, suggesting that hedge fund performance is sensitive to common business cycle movements. Overall, the results are valuable for researchers exploring contemporary return prediction methods and for investors seeking a deeper understanding of the drivers behind hedge fund returns.