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Hedge fund performance: The role of non-normality risks and conditional asset allocation

Hedge fund performance: The role of non-normality risks and conditional asset allocation. Harry M. Kat and Joëlle Miffre Cass Business School. Three contributions. Non-normality in the return distribution of hedge funds

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Hedge fund performance: The role of non-normality risks and conditional asset allocation

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  1. Hedge fund performance:The role of non-normality risksand conditional asset allocation Harry M. Kat and Joëlle Miffre Cass Business School

  2. Three contributions • Non-normality in the return distribution of hedge funds Fung and Hsieh (1997), (2001), Mitchell and Pulvino (2001), Agarwal and Naik (2004), Okunev and White (2003) • Dynamic asset allocation of hedge fund managers Ackermann, McEnally and Ravenscraft (1999),Brown, Goetzmann and Ibbotson (1999), Agarwal and Naik (2000a)… • Performance evaluation within a model that • Includes systematic skewness and kurtosis risk factors • Conditions risk and performance on public information

  3. Methodology • Multifactor model augmented with systematic skewness and kurtosis risk factors • Linear relation between  () and L mean-zero economic indicators (1) (2) (3)

  4. Methodology • Substituting  and  in (1) by (t|zt-1) and (t|zt-1) in (2) and (3) (Ferson and Schadt, 1996) • Hypothesis testing • Significance of systematic non-normality risk factors • Significance of conditioning information (1=0, 1=0, 1=1=0) • Abnormal performance (0=0) (4)

  5. Data: TASS hedge fund dataset • 2,239 US funds • At least 45 months of returns • January 1985 – August 2004 • Net of fees • Dead and surviving funds • Classified in 11 categories Convertible arbitrage, Dedicated short bias, Emerging markets, Equity market neutral, Event driven, Fixed income arbitrage, Fund of funds, Global macro, Long/short equity hedge, Managed futures, Other

  6. Summary statistics of hedge fund returns (Table 1) In brackets: Percentage of positive mean at the 5% level

  7. Data: Risk factors • Risk factors • Excess returns on MSCI world equity index • Excess returns on US Treasury-bond index • Excess returns on Reuters spot commodity index • Excess returns on the US dollar against major currency index • SMB (Fama and French, 1993) • HML (Fama and French, 1993) • Momentum (Carhart, 1997) • Systematic skewness and kurtosis mimicking portfolios

  8. Mimicking portfolios for systematicskewness and kurtosis risks • Mimicking portfolios as in Fama and French (1993) based on co-skewness or co-kurtosis of each stock (Kraus and Litzenberger, 1976) • Skewness mimicking portfolio defined as the difference in returns between the 30% of stocks with low coSK and the 30% of stocks with high coSK • Kurtosis mimicking portfolio defined as the difference in returns between the 30% of stocks with high coKU and the 30% of stocks with low coKU

  9. Summary statistics and pairwise correlations for the risk factors (Table 2) p-values in brackets

  10. Data: Information variables • Default spread • Term structure of interest rates • Hedge fund return (as predictor of abnormal performance) • Risk factor (as predictor of risk)

  11. Statistical significance of the risk factors (Table 3)

  12. Statistical significance of conditioning information (Table 4)

  13. The abnormal performance of hedge funds (Table 5) t-statistics in parentheses p-values in brackets

  14. Conclusions • Highlights shortcomings in previous tests • Systematic kurtosis and skewness risks are the main two drivers of hedge fund performance (69% and 66%, respectively) • Dynamics in asset allocation of managers captured with economic indicators • A failure to account for non-normality risks and conditional asset allocation lead to • Wrong statistical inference on performance on 30.2% of hedge funds • 1% overstatement of their annualized abnormal performance • Systematic non-normality risks and conditional asset allocation account for 15% of the abnormal performance that was previously identified: Hedge fund performance is not as good as once thought

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