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Testing Nonlinear Dependence in the Hedge Fund Industry

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  • Javier Mencía

Abstract

This paper proposes a parsimonious approach to test nonlinear dependence on the conditional mean and variance of hedge funds with respect to several market factors. My approach introduces nonlinear dependence by means of empirically relevant polynomial functions of the factors. For comparison purposes, I also consider multifactor extensions of tests based on piecewise linear alternatives. I apply these tests to a database of monthly returns on 1071 hedge funds. I find that nonlinear dependence on the mean is highly sensitive to the factors that I consider. However, I obtain a much stronger evidence of nonlinear dependence on the conditional variance. JEL: C12, C32, C22 Copyright The Author 2011. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com., Oxford University Press.

Suggested Citation

  • Javier Mencía, 2012. "Testing Nonlinear Dependence in the Hedge Fund Industry," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 10(3), pages 545-587, June.
  • Handle: RePEc:oup:jfinec:v:10:y:2012:i:3:p:545-587
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    File URL: http://hdl.handle.net/10.1093/jjfinec/nbr018
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    References listed on IDEAS

    as
    1. Antonio Diez De Los Rios & René Garcia, 2011. "Assessing and valuing the nonlinear structure of hedge fund returns," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(2), pages 193-212, March.
    2. Giovanni Barone Adesi & Patrick Gagliardini & Giovanni Urga, 2004. "Testing Asset Pricing Models With Coskewness," Journal of Business & Economic Statistics, American Statistical Association, vol. 22, pages 474-485, October.
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    More about this item

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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