Robust and reliable portfolio optimization formulation of a chance constrained problem

• Autorzy: Sengupta R. N. , Kumar R.

Identyfikatory

DOI

10.1515/fcds-2017-0004

• Języki publikacji: EN

Abstrakty

EN

We solve a linear chance constrained portfolio optimization problem using Robust Optimization (RO) method wherein financial script/asset loss return distributions are considered as extreme valued. The objective function is a convex combination of portfolio’s CVaR and expected value of loss return, subject to a set of randomly perturbed chance constraints with specified probability values. The robust deterministic counterpart of the model takes the form of Second Order Cone Programming (SOCP) problem. Results from extensive simulation runs show the efficacy of our proposed models, as it helps the investor to (i) utilize extensive simulation studies to draw insights into the effect of randomness in portfolio decision making process, (ii) incorporate different risk appetite scenarios to find the optimal solutions for the financial portfolio allocation problem and (iii) compare the risk and return profiles of the investments made in both deterministic as well as in uncertain and highly volatile financial markets.

Słowa kluczowe

EN

risk management; Investment Analysis; Robust Optimization; Conditional Value at Risk (CVaR); Extreme Value Distribution

• Wydawca: Wydawnictwo Politechniki Poznańskiej
• Czasopismo: Foundations of Computing and Decision Sciences
• Rocznik: 2017
• Tom: Vol. 42, No. 1
• Strony: 83--117
• Opis fizyczny: Bibliogr. 71 poz., fig., tab.

Twórcy

autor

Sengupta R. N.

• IME department, IIT Kanpur, Kanpur – 208016, India
• Department of Statistics and Econometrics, Faculty of Economic Sciences, University of Warsaw, 00-241 Warsaw, POLAND

autor

Kumar R.

• Ernst & Young LLP, Gurgaon, Haryana – 122 002, INDIA

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Uwagi

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).