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

Spiridon Penev, Pavel V. Shevchenko, Wei Wu

Research output: Contribution to journalArticle

1 Citation (Scopus)


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.
Original languageEnglish
Pages (from-to)772-784
Number of pages13
JournalEuropean Journal of Operational Research
Issue number2
Early online date23 Aug 2018
Publication statusPublished - 1 Mar 2019



  • Multivariate statistics
  • Robust portfolio allocation
  • Pseudo dynamic programming
  • Mean-standard-deviation
  • Kullback–Leibler divergence

Cite this