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The impact of model risk on dynamic portfolio selection under multi-period mean-standard-deviation criterion

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  • Spiridon Penev
  • Pavel V. Shevchenko
  • Wei Wu

Abstract

We quantify model risk of a financial portfolio whereby a multi-period mean-standard-deviation criterion is used as a selection criterion. In this work, model risk is defined as the loss due to uncertainty of the underlying distribution of the returns of the assets in the portfolio. The uncertainty is measured by the Kullback-Leibler divergence, i.e., the relative entropy. In the worst case scenario, the optimal robust strategy can be obtained in a semi-analytical form as a solution of a system of nonlinear equations. Several numerical results are presented which allow us to compare the performance of this robust strategy with the optimal non-robust strategy. For illustration, we also quantify the model risk associated with an empirical dataset.

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  • Spiridon Penev & Pavel V. Shevchenko & Wei Wu, 2021. "The impact of model risk on dynamic portfolio selection under multi-period mean-standard-deviation criterion," Papers 2108.02633, arXiv.org.
    • Handle: RePEc:arx:papers:2108.02633
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    References listed on IDEAS

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    1. Paul Glasserman & Xingbo Xu, 2013. "Robust Portfolio Control with Stochastic Factor Dynamics," Operations Research, INFORMS, vol. 61(4), pages 874-893, August.
    2. Duan Li & Wan‐Lung Ng, 2000. "Optimal Dynamic Portfolio Selection: Multiperiod Mean‐Variance Formulation," Mathematical Finance, Wiley Blackwell, vol. 10(3), pages 387-406, July.
    3. Gregory Connor & Lisa R. Goldberg & Robert A. Korajczyk, 2010. "Portfolio Risk Analysis," Economics Books, Princeton University Press, edition 1, number 9224.
    4. Kapsos, Michalis & Christofides, Nicos & Rustem, Berç, 2014. "Worst-case robust Omega ratio," European Journal of Operational Research, Elsevier, vol. 234(2), pages 499-507.
    5. Arellano-Valle, Reinaldo B. & Genton, Marc G., 2005. "On fundamental skew distributions," Journal of Multivariate Analysis, Elsevier, vol. 96(1), pages 93-116, September.
    6. Yuhong Xu, 2014. "Robust valuation and risk measurement under model uncertainty," Papers 1407.8024, arXiv.org.
    7. Chen, Zhi-ping & Li, Gang & Guo, Ju-e, 2013. "Optimal investment policy in the time consistent mean–variance formulation," Insurance: Mathematics and Economics, Elsevier, vol. 52(2), pages 145-156.
    8. Landsman, Zinoviy & Makov, Udi, 2012. "Translation-invariant and positive-homogeneous risk measures and optimal portfolio management in the presence of a riskless component," Insurance: Mathematics and Economics, Elsevier, vol. 50(1), pages 94-98.
    9. Jiang, Chonghui & Ma, Yongkai & An, Yunbi, 2010. "An analysis of portfolio selection with background risk," Journal of Banking & Finance, Elsevier, vol. 34(12), pages 3055-3060, December.
    10. Kang Boda & Jerzy Filar, 2006. "Time Consistent Dynamic Risk Measures," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 63(1), pages 169-186, February.
    11. Kim, Woo Chang & Kim, Min Jeong & Kim, Jang Ho & Fabozzi, Frank J., 2014. "Robust portfolios that do not tilt factor exposure," European Journal of Operational Research, Elsevier, vol. 234(2), pages 411-421.
    12. Schneider, Judith C. & Schweizer, Nikolaus, 2015. "Robust measurement of (heavy-tailed) risks: Theory and implementation," Journal of Economic Dynamics and Control, Elsevier, vol. 61(C), pages 183-203.
    13. Henry Lam, 2016. "Robust Sensitivity Analysis for Stochastic Systems," Mathematics of Operations Research, INFORMS, vol. 41(4), pages 1248-1275, November.
    14. Thomas Breuer & Imre Csiszár, 2016. "Measuring Distribution Model Risk," Mathematical Finance, Wiley Blackwell, vol. 26(2), pages 395-411, April.
    15. Paul Glasserman & Xingbo Xu, 2014. "Robust risk measurement and model risk," Quantitative Finance, Taylor & Francis Journals, vol. 14(1), pages 29-58, January.
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