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Detecting structural breaks and identifying risk factors in hedge fund returns: A Bayesian approach

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  • Meligkotsidou, Loukia
  • Vrontos, Ioannis D.

Abstract

Extending previous work on asset-based style factor models, this paper proposes a model that allows for the presence of structural breaks in hedge fund return series. We consider a Bayesian approach to detecting structural breaks occurring at unknown times and identifying relevant risk factors to explain the monthly return variation. Exact and efficient Bayesian inference for the unknown number and positions of the breaks is performed by using filtering recursions similar to those of the forward-backward algorithm. Existing methods of testing for structural breaks are also used for comparison. We investigate the presence of structural breaks in several hedge fund indices; our results are consistent with market events and episodes that caused substantial volatility in hedge fund returns during the last decade.

Suggested Citation

  • Meligkotsidou, Loukia & Vrontos, Ioannis D., 2008. "Detecting structural breaks and identifying risk factors in hedge fund returns: A Bayesian approach," Journal of Banking & Finance, Elsevier, vol. 32(11), pages 2471-2481, November.
    • Handle: RePEc:eee:jbfina:v:32:y:2008:i:11:p:2471-2481
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    3. Darolles, Serge & Vaissié, Mathieu, 2012. "The alpha and omega of fund of hedge fund added value," Journal of Banking & Finance, Elsevier, vol. 36(4), pages 1067-1078.
    4. Eling, Martin & Faust, Roger, 2010. "The performance of hedge funds and mutual funds in emerging markets," Journal of Banking & Finance, Elsevier, vol. 34(8), pages 1993-2009, August.
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    6. Yang, Fan & Havranek, Tomas & Irsova, Zuzana & Novak, Jiri, 2022. "Hedge Fund Performance: A Quantitative Survey," EconStor Preprints 260612, ZBW - Leibniz Information Centre for Economics.
    7. David Ardia & Arnaud Dufays & Carlos Ordás Criado, 2024. "Linking Frequentist and Bayesian Change-Point Methods," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 42(4), pages 1155-1168, October.
    8. Viet Do & Robert Faff & Paul Lajbcygier & Madhu Veeraraghavan & Mikhail Tupitsyn, 2016. "Factors affecting the birth and fund flows of CTAs," Australian Journal of Management, Australian School of Business, vol. 41(2), pages 324-352, May.
    9. Meligkotsidou, Loukia & Vrontos, Ioannis D. & Vrontos, Spyridon D., 2009. "Quantile regression analysis of hedge fund strategies," Journal of Empirical Finance, Elsevier, vol. 16(2), pages 264-279, March.
    10. Wegener, Christian & von Nitzsch, Rüdiger & Cengiz, Cetin, 2010. "An advanced perspective on the predictability in hedge fund returns," Journal of Banking & Finance, Elsevier, vol. 34(11), pages 2694-2708, November.
    11. Fan Yang & Tomas Havranek & Zuzana Irsova & Jiri Novak, 2024. "Is research on hedge fund performance published selectively? A quantitative survey," Journal of Economic Surveys, Wiley Blackwell, vol. 38(4), pages 1085-1131, September.
    12. Harry J. Turtle & Chengping Zhang, 2015. "Structural breaks and portfolio performance in global equity markets," Quantitative Finance, Taylor & Francis Journals, vol. 15(6), pages 909-922, June.
    13. Koop, Gary & Korobilis, Dimitris, 2014. "A new index of financial conditions," European Economic Review, Elsevier, vol. 71(C), pages 101-116.
    14. Massimo Guidolin & Alexei Orlov, 2020. "Are Unconventional Monetary Policies a Priced Risk Factor for Hedge Fund Strategies?," BAFFI CAREFIN Working Papers 20146, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
    15. Laurent Bodson & Alain Coën & Georges Hübner, 2010. "Dynamic Hedge Fund Style Analysis With Errors‐In‐Variables," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 33(3), pages 201-221, September.

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