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Downside risk in multiperiod tracking error models

Author

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  • Diana Barro

    (Department of Economics, University Of Venice C� Foscari)

  • Elio Canestrelli

    (Department of Economics, University Of Venice C� Foscari)

Abstract

The recent crisis made it evident that replicating the performance of a benchmark is not a sufficient goal to meet the expectations of usually risk-averse investors. The manager should also consider that the investor are seeking for a downside protection when the benchmark performs poorly and thus they should integrate a form of downside risk control. We propose a multiperiod double tracking error portfolio model which combines these two goals and provide enough flexibility. In particular, the control of the downside risk is carried out through the presence of a floor benchmark with respect to which we can accept different levels of shortfall. The choice of a proper measure for downside risk leads to different problem formulations and investment strategies which can reflect different attitudes towards risk. The proposed model is tested through a set of out-of-sample rolling simulation in different market conditions.

Suggested Citation

  • Diana Barro & Elio Canestrelli, 2012. "Downside risk in multiperiod tracking error models," Working Papers 2012_17, Department of Economics, University of Venice "Ca' Foscari".
  • Handle: RePEc:ven:wpaper:2012_17
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Adam Borovička, 2022. "Stock portfolio selection under unstable uncertainty via fuzzy mean-semivariance model," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 30(2), pages 595-616, June.
    2. Diana Barro & Elio Canestrelli & Fabio Lanza, 2014. "Volatility vs. downside risk: optimally protecting against drawdowns and maintaining portfolio performance," Working Papers 2014:18, Department of Economics, University of Venice "Ca' Foscari".
    3. Kerem Uğurlu & Tomasz Brzeczek, 2023. "Distorted probability operator for dynamic portfolio optimization in times of socio-economic crisis," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 31(4), pages 1043-1060, December.
    4. Tobias F. Rötheli, 2018. "Should business rely on business cycle forecasting?," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 26(1), pages 121-133, March.
    5. Diana Barro & Elio Canestrelli & Giorgio Consigli, 2019. "Volatility versus downside risk: performance protection in dynamic portfolio strategies," Computational Management Science, Springer, vol. 16(3), pages 433-479, July.
    6. Julio Cezar Soares Silva & Adiel Teixeira de Almeida Filho, 2023. "A systematic literature review on solution approaches for the index tracking problem in the last decade," Papers 2306.01660, arXiv.org, revised Jun 2023.

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    More about this item

    Keywords

    Tracking error; Downside risk; Dynamic portfolio; Stochastic programming;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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