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Portfolio optimization through Kriging methods

Author

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  • Marcelo Rosário da Barrosa
  • Arthur Valle Salles
  • Celma de Oliveira Ribeiro

Abstract

This article presents a new methodology for optimizing financial asset portfolios. The proposed methodology, based on the Kriging method, allows for approximating the risk surface – and thus the optimal solution to the problem – in a generalized fashion, relaxing every restrictive hypothesis inherent to the available methods and with the ability to estimate the error in the risk surface approximation. Illustratively, the proposed methodology is applied to the portfolio problem with the Variance, VaR and CVaR as objective functions. The results are compared to those obtained using the Khun–Tucker technique, for the former, and the Rockafellar method, for the latter.

Suggested Citation

  • Marcelo Rosário da Barrosa & Arthur Valle Salles & Celma de Oliveira Ribeiro, 2016. "Portfolio optimization through Kriging methods," Applied Economics, Taylor & Francis Journals, vol. 48(50), pages 4894-4905, October.
    • Handle: RePEc:taf:applec:v:48:y:2016:i:50:p:4894-4905
    DOI: 10.1080/00036846.2016.1167827
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    Cited by:

    1. Stéphane Crépey & Matthew F Dixon, 2020. "Gaussian process regression for derivative portfolio modeling and application to credit valuation adjustment computations," Post-Print hal-03910109, HAL.
    2. St'ephane Cr'epey & Matthew Dixon, 2019. "Gaussian Process Regression for Derivative Portfolio Modeling and Application to CVA Computations," Papers 1901.11081, arXiv.org, revised Oct 2019.

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