A chance constrained investment problem with portfolio variance and skewness criteria - solution technique based on the Successive Iterative Regularization

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F16%3A10326688" target="_blank" >RIV/00216208:11320/16:10326688 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

A chance constrained investment problem with portfolio variance and skewness criteria - solution technique based on the Successive Iterative Regularization

Popis výsledku v původním jazyce

We deal with an investment problem, where the variance of a portfolio is minimized and at the same time the skewness is maximized. Moreover, we impose a chance (probabilistic) constraint on the portfolio return which must be fulfilled with a high probability. This leads to a difficult nonconvex multiobjective stochastic programming problem. Under discretely distributed returns, this problem can be solved using the CCP-SIR solver (Chance Constrained Problems: Successive Iterative Regularization) which has been recently introduced by Adam and Branda [1]. This algorithm relies on a relaxed nonlinear programming problem and its regularized version obtained by enlarging the set of feasible solutions using regularizing functions. These both formulations as well as the solution technique are discussed in details. We report the results for a real life portfolio problem of a small investor. We compare the CCP-SIR solver with BONMIN applied to the deterministic mixed-integer reformulation.

Název v anglickém jazyce

A chance constrained investment problem with portfolio variance and skewness criteria - solution technique based on the Successive Iterative Regularization

Popis výsledku anglicky

We deal with an investment problem, where the variance of a portfolio is minimized and at the same time the skewness is maximized. Moreover, we impose a chance (probabilistic) constraint on the portfolio return which must be fulfilled with a high probability. This leads to a difficult nonconvex multiobjective stochastic programming problem. Under discretely distributed returns, this problem can be solved using the CCP-SIR solver (Chance Constrained Problems: Successive Iterative Regularization) which has been recently introduced by Adam and Branda [1]. This algorithm relies on a relaxed nonlinear programming problem and its regularized version obtained by enlarging the set of feasible solutions using regularizing functions. These both formulations as well as the solution technique are discussed in details. We report the results for a real life portfolio problem of a small investor. We compare the CCP-SIR solver with BONMIN applied to the deterministic mixed-integer reformulation.

Druh

D - Stať ve sborníku

CEP obor

BB - Aplikovaná statistika, operační výzkum

OECD FORD obor

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Projekt

<a href="/cs/project/GBP402%2F12%2FG097" target="_blank" >GBP402/12/G097: DYME-Dynamické modely v ekonomii</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2016

Kód důvěrnosti údajů

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

Název statě ve sborníku

34TH INTERNATIONAL CONFERENCE MATHEMATICAL METHODS IN ECONOMICS (MME 2016)

ISBN

978-80-7494-296-9

ISSN

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e-ISSN

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Počet stran výsledku

6

Strana od-do

67-72

Název nakladatele

Technical University of Liberec

Místo vydání

Liberec

Místo konání akce

Liberec, Czech Republic

Datum konání akce

6. 9. 2016

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000385239500012