Non-Parametric Analysis of Hedge Fund Returns: New Insights from High Frequency Data
Ca Foscari University of Venice - Dipartimento di Economia
University of Massachusetts at Amherst - Eugene M. Isenberg School of Management - Department of Finance
Goethe University Frankfurt - Faculty of Economics and Business Administration; Goethe University Frankfurt - Research Center SAFE; Ca Foscari University of Venice
April 12, 2009
Journal of Alternative Investments (forthcoming)
This paper examines four daily hedge fund return indices: MSCI, FTSE, Dow Jones, and HFRX, all based on investable hedge funds, and three monthly hedge fund return indices: CSFB Tremont, CISDM, and HFR, which comprise both investable and non-investable hedge funds. Our study, based on standard statistical analysis, non-parametric analysis of the return distribution, and non-parametric regressions with respect to the S&P 500 index shows that key biases like fund selection, asset liquidity, data frequency, sample period, and index construction methodologies are responsible for different statistical properties of hedge fund indices. One key variable that highly affects the statistical properties of hedge fund index returns is the “investability” of hedge fund indices.
Number of Pages in PDF File: 39
Keywords: Hedge Funds, Risk Management, High frequency data
JEL Classification: G12, G29, C51
Date posted: May 20, 2008 ; Last revised: April 25, 2012
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