Počet záznamů: 1
Sparse robust portfolio optimization via NLP regularizations
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SYSNO ASEP 0468834 Druh ASEP V - Výzkumná zpráva Zařazení RIV Záznam nebyl označen do RIV Název Sparse robust portfolio optimization via NLP regularizations Tvůrce(i) Branda, Martin (UTIA-B) RID, ORCID
Červinka, Michal (UTIA-B) RID, ORCID
Schwartz, A. (DE)Celkový počet autorů 3 Vyd. údaje Praha: ÚTIA AV ČR v. v. i., 2016 Edice Research Report Č. sv. edice 2358 Poč.str. 19 s. Forma vydání Tištěná - P Jazyk dok. eng - angličtina Země vyd. CZ - Česká republika Klíč. slova Conditional Value-at-Risk ; Value-at-Risk ; risk measure Vědní obor RIV BB - Aplikovaná statistika, operační výzkum CEP GA15-00735S GA ČR - Grantová agentura ČR Institucionální podpora UTIA-B - RVO:67985556 Anotace We deal with investment problems where we minimize a risk measure under a condition on the sparsity of the portfolio. Various risk measures are considered including Value-at-Risk and Conditional Value-at-Risk under normal distribution of returns and their robust counterparts are derived under moment conditions, all leading to nonconvex objective functions. We propose four solution approaches: a mixed-integer formulation, a relaxation of an alternative mixed-integer reformulation and two NLP regularizations. In a numerical study, we compare their computational performance on a large number of simulated instances taken from the literature. Překlad anotace We deal with investment problems where we minimize a risk measure
under a condition on the sparsity of the portfolio. Various risk measures
are considered including Value-at-Risk and Conditional Value-at-Risk
under normal distribution of returns and their robust counterparts are
derived under moment conditions, all leading to nonconvex objective
functions. We propose four solution approaches: a mixed-integer formulation,
a relaxation of an alternative mixed-integer reformulation and
two NLP regularizations. In a numerical study, we compare their computational
performance on a large number of simulated instances taken
from the literature.Pracoviště Ústav teorie informace a automatizace Kontakt Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Rok sběru 2017
Počet záznamů: 1