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Sparse robust portfolio optimization via NLP regularizations
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SYSNO ASEP 0468834 Document Type V - Research Report R&D Document Type The record was not marked in the RIV Title Sparse robust portfolio optimization via NLP regularizations Author(s) Branda, Martin (UTIA-B) RID, ORCID
Červinka, Michal (UTIA-B) RID, ORCID
Schwartz, A. (DE)Number of authors 3 Issue data Praha: ÚTIA AV ČR v. v. i., 2016 Series Research Report Series number 2358 Number of pages 19 s. Publication form Print - P Language eng - English Country CZ - Czech Republic Keywords Conditional Value-at-Risk ; Value-at-Risk ; risk measure Subject RIV BB - Applied Statistics, Operational Research R&D Projects GA15-00735S GA ČR - Czech Science Foundation (CSF) Institutional support UTIA-B - RVO:67985556 Annotation 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. Description in English 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.Workplace Institute of Information Theory and Automation Contact Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Year of Publishing 2017
Number of the records: 1