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 journalArticleResearchpeer-review

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.
LanguageEnglish
Pages772-784
Number of pages13
JournalEuropean Journal of Operational Research
Volume273
Issue number2
Early online date23 Aug 2018
DOIs
Publication statusPublished - 1 Mar 2019

Fingerprint

Portfolio Selection
Standard deviation
Quantify
Uncertainty
Kullback-Leibler Divergence
Relative Entropy
System of Nonlinear Equations
Nonlinear equations
Entropy
Model
Numerical Results
Scenarios
Strategy
Dynamic portfolio selection
Model risk

Keywords

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

Cite this

@article{77722d9836d14aa08915098ee633c15c,
title = "The impact of model risk on dynamic portfolio selection under multi-period mean-standard-deviation criterion",
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.",
keywords = "Multivariate statistics, Robust portfolio allocation, Pseudo dynamic programming, Mean-standard-deviation, Kullback–Leibler divergence",
author = "Spiridon Penev and Shevchenko, {Pavel V.} and Wei Wu",
year = "2019",
month = "3",
day = "1",
doi = "10.1016/j.ejor.2018.08.026",
language = "English",
volume = "273",
pages = "772--784",
journal = "European Journal of Operational Research",
issn = "0377-2217",
publisher = "Elsevier",
number = "2",

}

The impact of model risk on dynamic portfolio selection under multi-period mean-standard-deviation criterion. / Penev, Spiridon; Shevchenko, Pavel V.; Wu, Wei.

In: European Journal of Operational Research, Vol. 273, No. 2, 01.03.2019, p. 772-784.

Research output: Contribution to journalArticleResearchpeer-review

TY - JOUR

T1 - The impact of model risk on dynamic portfolio selection under multi-period mean-standard-deviation criterion

AU - Penev, Spiridon

AU - Shevchenko, Pavel V.

AU - Wu, Wei

PY - 2019/3/1

Y1 - 2019/3/1

N2 - 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.

AB - 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.

KW - Multivariate statistics

KW - Robust portfolio allocation

KW - Pseudo dynamic programming

KW - Mean-standard-deviation

KW - Kullback–Leibler divergence

UR - http://www.scopus.com/inward/record.url?scp=85052842820&partnerID=8YFLogxK

U2 - 10.1016/j.ejor.2018.08.026

DO - 10.1016/j.ejor.2018.08.026

M3 - Article

VL - 273

SP - 772

EP - 784

JO - European Journal of Operational Research

T2 - European Journal of Operational Research

JF - European Journal of Operational Research

SN - 0377-2217

IS - 2

ER -