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A Bayesian approach to detecting nonlinear risk exposures in hedge fund strategies

Author

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  • Giannikis, Dimitrios
  • Vrontos, Ioannis D.

Abstract

This paper proposes a model that allows for nonlinear risk exposures of hedge funds to various risk factors. We introduce a flexible threshold regression model and develop a Bayesian approach for model selection and estimation of the thresholds and their unknown number. In particular, we present a computationally flexible Markov chain Monte Carlo stochastic search algorithm which identifies relevant risk factors and/or threshold values. Our analysis of several hedge fund returns reveals that different strategies exhibit nonlinear relations to different risk factors, and that the proposed threshold regression model improves our ability to evaluate hedge fund performance.

Suggested Citation

  • Giannikis, Dimitrios & Vrontos, Ioannis D., 2011. "A Bayesian approach to detecting nonlinear risk exposures in hedge fund strategies," Journal of Banking & Finance, Elsevier, vol. 35(6), pages 1399-1414, June.
  • Handle: RePEc:eee:jbfina:v:35:y:2011:i:6:p:1399-1414
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    References listed on IDEAS

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    Cited by:

    1. repec:eee:finana:v:56:y:2018:i:c:p:221-237 is not listed on IDEAS
    2. Olmo, José & Sanso-Navarro, Marcos, 2012. "Forecasting the performance of hedge fund styles," Journal of Banking & Finance, Elsevier, vol. 36(8), pages 2351-2365.
    3. Horst, Jenke ter & Salganik, Galla, 2014. "Style chasing by hedge fund investors," Journal of Banking & Finance, Elsevier, vol. 39(C), pages 29-42.
    4. Del Brio, Esther B. & Mora-Valencia, Andrés & Perote, Javier, 2014. "Semi-nonparametric VaR forecasts for hedge funds during the recent crisis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 401(C), pages 330-343.
    5. repec:eee:glofin:v:33:y:2017:i:c:p:69-87 is not listed on IDEAS

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