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Regular(Ized) Hedge Fund Clones

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  • Daniel Giamouridis
  • Sandra Paterlini

Abstract

Abstract This article addresses the problem of portfolio construction in the context of efficient hedge fund investments replication. We propose a modification to the standard Sharpe "style analysis" by introducing a constraint on the asset weights 1-norm and 2-norm. This constraint regularizes the optimization problem, allows efficient selection of relevant factor's and has significant effects on the stability of the resulting asset mix and the risk-return characteristics of the replicating portfolio. The empirical results suggest that the norm-constrained replicating portfolios exhibit significant correlations with their benchmarks, often higher than 0.9; have a fraction, which is about half to two-thirds, of active positions relative to those determined through the standard method; and are obtained with turnover, which is in some instances about one-fourth of that for the standard method. Copyright (c) 2010 The Southern Finance Association and the Southwestern Finance Association.

Suggested Citation

  • Daniel Giamouridis & Sandra Paterlini, 2010. "Regular(Ized) Hedge Fund Clones," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 33(3), pages 223-247.
  • Handle: RePEc:bla:jfnres:v:33:y:2010:i:3:p:223-247
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    References listed on IDEAS

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    1. Corielli, Francesco & Marcellino, Massimiliano, 2006. "Factor based index tracking," Journal of Banking & Finance, Elsevier, vol. 30(8), pages 2215-2233, August.
    2. Mark Mitchell, 2001. "Characteristics of Risk and Return in Risk Arbitrage," Journal of Finance, American Finance Association, vol. 56(6), pages 2135-2175, December.
    3. Fung, William & Hsieh, David A, 2001. "The Risk in Hedge Fund Strategies: Theory and Evidence from Trend Followers," Review of Financial Studies, Society for Financial Studies, vol. 14(2), pages 313-341.
    4. Vikas Agarwal, 2004. "Risks and Portfolio Decisions Involving Hedge Funds," Review of Financial Studies, Society for Financial Studies, vol. 17(1), pages 63-98.
    5. Ravi Jagannathan & Tongshu Ma, 2003. "Risk Reduction in Large Portfolios: Why Imposing the Wrong Constraints Helps," Journal of Finance, American Finance Association, vol. 58(4), pages 1651-1684, August.
    6. Victor DeMiguel & Lorenzo Garlappi & Francisco J. Nogales & Raman Uppal, 2009. "A Generalized Approach to Portfolio Optimization: Improving Performance by Constraining Portfolio Norms," Management Science, INFORMS, vol. 55(5), pages 798-812, May.
    7. ter Horst, Jenke R. & Nijman, Theo E. & de Roon, Frans A., 2004. "Evaluating style analysis," Journal of Empirical Finance, Elsevier, vol. 11(1), pages 29-53, January.
    8. Agarwal, Vikas & Naik, Narayan Y., 2000. "Multi-Period Performance Persistence Analysis of Hedge Funds," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 35(03), pages 327-342, September.
    9. Vrontos, Spyridon D. & Vrontos, Ioannis D. & Giamouridis, Daniel, 2008. "Hedge fund pricing and model uncertainty," Journal of Banking & Finance, Elsevier, vol. 32(5), pages 741-753, May.
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    Cited by:

    1. repec:spr:decfin:v:40:y:2017:i:1:d:10.1007_s10203-017-0191-y is not listed on IDEAS
    2. B. Fastrich & S. Paterlini & P. Winker, 2015. "Constructing optimal sparse portfolios using regularization methods," Computational Management Science, Springer, vol. 12(3), pages 417-434, July.
    3. Giuzio, Margherita & Ferrari, Davide & Paterlini, Sandra, 2016. "Sparse and robust normal and t- portfolios by penalized Lq-likelihood minimization," European Journal of Operational Research, Elsevier, vol. 250(1), pages 251-261.
    4. Yen, Yu-Min & Yen, Tso-Jung, 2014. "Solving norm constrained portfolio optimization via coordinate-wise descent algorithms," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 737-759.

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