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Tracking hedge funds returns using sparse clones

Author

Listed:
  • Margherita Giuzio

    (EBS Universität für Wirtschaft und Recht)

  • Kay Eichhorn-Schott

    (EBS Universität für Wirtschaft und Recht)

  • Sandra Paterlini

    (EBS Universität für Wirtschaft und Recht)

  • Vincent Weber

    (Prime Capital AG)

Abstract

Whether hedge fund returns could be attributed to systematic risk exposures rather than managerial skills is an interesting debate among academics and practitioners. Academic literature suggests that hedge fund performance is mostly determined by alternative betas, which justifies the construction of investable hedge fund clones or replicators. Practitioners often claim that management skills are instrumental for successful performance. In this paper, we study the risk exposure of different hedge fund indices to a set of liquid asset class factors by means of style analysis. We extend the classical style analysis framework by including a penalty that allows to retain only relevant factors, dealing effectively with collinearity, and to capture the out-of-sample properties of hedge fund indices by closely mimicking their returns. In particular, we introduce a Log-penalty and discuss its statistical properties, showing then that Log-clones are able to closely track the returns of hedge fund indices with a smaller number of factors and lower turnover than the clones built from state-of-art methods.

Suggested Citation

  • 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.
  • Handle: RePEc:spr:annopr:v:266:y:2018:i:1:d:10.1007_s10479-016-2371-5
    DOI: 10.1007/s10479-016-2371-5
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    References listed on IDEAS

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    1. Giannone, Domenico & De Mol, Christine & Daubechies, Ingrid & Brodie, Joshua, 2007. "Sparse and Stable Markowitz Portfolios," CEPR Discussion Papers 6474, C.E.P.R. Discussion Papers.
    2. 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 1651-1683, August.
    3. Andrew J. Patton & Tarun Ramadorai, 2013. "On the High-Frequency Dynamics of Hedge Fund Risk Exposures," Journal of Finance, American Finance Association, vol. 68(2), pages 597-635, April.
    4. Roncalli, Thierry, 2013. "Introduction to Risk Parity and Budgeting," MPRA Paper 47679, University Library of Munich, Germany.
    5. 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.
    6. Akiko Takeda & Mahesan Niranjan & Jun-ya Gotoh & Yoshinobu Kawahara, 2013. "Simultaneous pursuit of out-of-sample performance and sparsity in index tracking portfolios," Computational Management Science, Springer, vol. 10(1), pages 21-49, February.
    7. 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.
    8. Fan J. & Li R., 2001. "Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1348-1360, December.
    9. Bj�rn Fastrich & Sandra Paterlini & Peter Winker, 2014. "Cardinality versus q -norm constraints for index tracking," Quantitative Finance, Taylor & Francis Journals, vol. 14(11), pages 2019-2032, November.
    10. Jun-ya Gotoh & Akiko Takeda, 2011. "On the role of norm constraints in portfolio selection," Computational Management Science, Springer, vol. 8(4), pages 323-353, November.
    11. 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.
    12. repec:bla:jfinan:v:58:y:2003:i:4:p:1651-1684 is not listed on IDEAS
    13. Liang, Bing, 2000. "Hedge Funds: The Living and the Dead," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 35(3), pages 309-326, September.
    14. Brown, Stephen J & Goetzmann, William N & Ibbotson, Roger G, 1999. "Offshore Hedge Funds: Survival and Performance, 1989-95," The Journal of Business, University of Chicago Press, vol. 72(1), pages 91-117, January.
    15. Vikas Agarwal, 2004. "Risks and Portfolio Decisions Involving Hedge Funds," The Review of Financial Studies, Society for Financial Studies, vol. 17(1), pages 63-98.
    16. 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(3), pages 327-342, September.
    17. Fung, William & Hsieh, David A, 1997. "Empirical Characteristics of Dynamic Trading Strategies: The Case of Hedge Funds," The Review of Financial Studies, Society for Financial Studies, vol. 10(2), pages 275-302.
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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. Anubha Goel & Damir Filipovi'c & Puneet Pasricha, 2024. "Sparse Portfolio Selection via Topological Data Analysis based Clustering," Papers 2401.16920, arXiv.org, revised Dec 2024.
    3. Emmanuel Mamatzakis & Mike G. Tsionas, 2021. "Testing for persistence in US mutual funds’ performance: a Bayesian dynamic panel model," Annals of Operations Research, Springer, vol. 299(1), pages 1203-1233, April.
    4. Gabriele Torri & Rosella Giacometti & Sandra Paterlini, 2024. "Penalized enhanced portfolio replication with asymmetric deviation measures," Annals of Operations Research, Springer, vol. 332(1), pages 481-531, January.

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