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Regular(Ized) Hedge Fund Clones

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  • Daniel Giamouridis
  • Sandra Paterlini

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.

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, September.
    • Handle: RePEc:bla:jfnres:v:33:y:2010:i:3:p:223-247
    DOI: 10.1111/j.1475-6803.2010.01269.x
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    References listed on IDEAS

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    Cited by:

    1. Margherita Giuzio, 2017. "Genetic algorithm versus classical methods in sparse index tracking," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 40(1), pages 243-256, November.
    2. Jun Duanmu & Yongjia Li & Alexey Malakhov, 2020. "Capturing hedge fund risk factor exposures: Hedge fund return replication with ETFs," The Financial Review, Eastern Finance Association, vol. 55(3), pages 405-431, August.
    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. Bartram, Söhnke & Branke, Jürgen & Motahari, Mehrshad, 2020. "Artificial Intelligence in Asset Management," CEPR Discussion Papers 14525, C.E.P.R. Discussion Papers.
    5. 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.
    6. 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.
    7. Anubha Goel & Damir Filipovi'c & Puneet Pasricha, 2024. "Sparse Portfolio Selection via Topological Data Analysis based Clustering," Papers 2401.16920, arXiv.org.
    8. Philipp J. Kremer & Andreea Talmaciu & Sandra Paterlini, 2018. "Risk minimization in multi-factor portfolios: What is the best strategy?," Annals of Operations Research, Springer, vol. 266(1), pages 255-291, July.
    9. Margherita Giuzio & Kay Eichhorn-Schott & Sandra Paterlini & Vincent Weber, 2018. "Tracking hedge funds returns using sparse clones," Annals of Operations Research, Springer, vol. 266(1), pages 349-371, July.

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