Non-Parametric Analysis of Hedge Fund Returns: New Insights from High Frequency Data

Loriana Pelizzon (), Monica Billio and Mila Getmansky ()
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Mila Getmansky: Department of Finance and Operations Management Isenberg School of Management University of Massachusetts

No 2008_11, Working Papers from Department of Economics, University of Venice "Ca' Foscari"

Abstract: This paper examines four different daily datasets of hedge fund return indexes: MSCI, FTSE, Dow Jones and HFRX, all based on investable hedge funds, and three different monthly datasets of hedge fund return indexes: CSFB, CISDM and HFR which comprise both investable and non-investable hedge funds. Our study, based on standard statistical analysis, non-parametric analysis of the distribution and non-parametric regressions with respect to the S&P500 index shows that key data biases and disparate index construction methodologies lead to different statistical properties of hedge fund databases. One key variable that highly affects the statistical properties of hedge fund index returns is the �investability� of hedge funds

Keywords: Hedge Fund; Risk Management; High frequency data (search for similar items in EconPapers)
JEL-codes: C51 G12 G29 (search for similar items in EconPapers)
Pages: 40
Date: 2008
New Economics Papers: this item is included in nep-cfn, nep-eff, nep-fmk, nep-mst and nep-rmg
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Persistent link: https://EconPapers.repec.org/RePEc:ven:wpaper:2008_11

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