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

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  • Loriana Pelizzon

    ()
    (Department of Economics, University Of Venice Cà Foscari)

  • Monica Billio

    ()
    (Department of Economics, University Of Venice Cà Foscari)

  • Mila Getmansky

    ()
    (Department of Finance and Operations Management Isenberg School of Management University of Massachusetts)

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

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Bibliographic Info

Paper provided by Department of Economics, University of Venice "Ca' Foscari" in its series Working Papers with number 2008_11.

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Length: 40
Date of creation: 2008
Date of revision:
Handle: RePEc:ven:wpaper:2008_11

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Keywords: Hedge Fund; Risk Management; High frequency data;

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  1. Billio, Monica & Getmansky, Mila & Pelizzon, Loriana, 2012. "Dynamic risk exposures in hedge funds," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 56(11), pages 3517-3532.
  2. Fung, William & Hsieh, David A, 1997. "Empirical Characteristics of Dynamic Trading Strategies: The Case of Hedge Funds," Review of Financial Studies, Society for Financial Studies, Society for Financial Studies, vol. 10(2), pages 275-302.
  3. Vikas Agarwal, 2004. "Risks and Portfolio Decisions Involving Hedge Funds," Review of Financial Studies, Society for Financial Studies, Society for Financial Studies, vol. 17(1), pages 63-98.
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