Robust approaches in portfolio optimization with stochastic dominance constraints

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3A10507987" target="_blank" >RIV/00216208:11320/25:10507987 - isvavai.cz</a>

Výsledek na webu

<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=m9Ec82ybar" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=m9Ec82ybar</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s00291-025-00814-1" target="_blank" >10.1007/s00291-025-00814-1</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Robust approaches in portfolio optimization with stochastic dominance constraints

Popis výsledku v původním jazyce

The paper deals with a modern approach of stochastic dominance in portfolio optimization. Since the distribution of returns is often just estimated from data, we look for the worst-case distribution that differs from the empirical distribution by no more than some prescribed value. First, we define in what sense the distribution is the worst one for stochastic dominance. Then, using Wasserstein distance, we derive a reformulation for robust second-order stochastic dominance and find the worst-case distribution as the optimal solution of a non-linear optimization problem. Finally, we derive programs to maximize an objective function over the weights of the portfolio with the robust stochastic dominance condition in constraints. We consider robustness in returns for second-order stochastic dominance. We apply all derived optimization programs to real-life data, specifically to returns of assets captured by the Dow Jones Industrial Average, and analyze the problems in detail using optimal solutions of optimization programs with multiple setups. The empirical analysis proceeded with an out-of-sample evaluation of portfolios formulated through the robust optimization program, employing a moving window methodology. The findings of this study indicate that for some of the values of epsilondocumentclass[12pt]{minimal} usepackage{amsmath} usepackage{wasysym} usepackage{amsfonts} usepackage{amssymb} usepackage{amsbsy} usepackage{mathrsfs} usepackage{upgreek} setlength{oddsidemargin}{-69pt} begin{document}$$varepsilon $$end{document} the robustified portfolios consistently out-of-sample outperform those derived from the non-robust optimization approach.

Název v anglickém jazyce

Robust approaches in portfolio optimization with stochastic dominance constraints

Popis výsledku anglicky

The paper deals with a modern approach of stochastic dominance in portfolio optimization. Since the distribution of returns is often just estimated from data, we look for the worst-case distribution that differs from the empirical distribution by no more than some prescribed value. First, we define in what sense the distribution is the worst one for stochastic dominance. Then, using Wasserstein distance, we derive a reformulation for robust second-order stochastic dominance and find the worst-case distribution as the optimal solution of a non-linear optimization problem. Finally, we derive programs to maximize an objective function over the weights of the portfolio with the robust stochastic dominance condition in constraints. We consider robustness in returns for second-order stochastic dominance. We apply all derived optimization programs to real-life data, specifically to returns of assets captured by the Dow Jones Industrial Average, and analyze the problems in detail using optimal solutions of optimization programs with multiple setups. The empirical analysis proceeded with an out-of-sample evaluation of portfolios formulated through the robust optimization program, employing a moving window methodology. The findings of this study indicate that for some of the values of epsilondocumentclass[12pt]{minimal} usepackage{amsmath} usepackage{wasysym} usepackage{amsfonts} usepackage{amssymb} usepackage{amsbsy} usepackage{mathrsfs} usepackage{upgreek} setlength{oddsidemargin}{-69pt} begin{document}$$varepsilon $$end{document} the robustified portfolios consistently out-of-sample outperform those derived from the non-robust optimization approach.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

OR Spektrum

ISSN

0171-6468

e-ISSN

1436-6304

Svazek periodika

47

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

33

Strana od-do

1421-1453

Kód UT WoS článku

001489530200001

EID výsledku v databázi Scopus

2-s2.0-105005095837