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Portfolio selection under model uncertainty: a penalized moment-based optimization approach

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  • Jonathan Li
  • Roy Kwon

Abstract

We present a new approach that enables investors to seek a reasonably robust policy for portfolio selection in the presence of rare but high-impact realization of moment uncertainty. In practice, portfolio managers face difficulty in seeking a balance between relying on their knowledge of a reference financial model and taking into account possible ambiguity of the model. Based on the concept of Distributionally Robust Optimization (DRO), we introduce a new penalty framework that provides investors flexibility to define prior reference models using the distributional information of the first two moments and accounts for model ambiguity in terms of extreme moment uncertainty. We show that in our approach a globally-optimal portfolio can in general be obtained in a computationally tractable manner. We also show that for a wide range of specifications our proposed model can be recast as semidefinite programs. Computational experiments show that our penalized moment-based approach outperforms classical DRO approaches in terms of both average and downside-risk performance using historical data. Copyright Springer Science+Business Media, LLC. 2013

Suggested Citation

  • Jonathan Li & Roy Kwon, 2013. "Portfolio selection under model uncertainty: a penalized moment-based optimization approach," Journal of Global Optimization, Springer, vol. 56(1), pages 131-164, May.
  • Handle: RePEc:spr:jglopt:v:56:y:2013:i:1:p:131-164
    DOI: 10.1007/s10898-012-9969-1
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    5. Fernandes, Betina & Street, Alexandre & Valladão, Davi & Fernandes, Cristiano, 2016. "An adaptive robust portfolio optimization model with loss constraints based on data-driven polyhedral uncertainty sets," European Journal of Operational Research, Elsevier, vol. 255(3), pages 961-970.

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