Original title: Sparse robust portfolio optimization via NLP regularizations
Authors: Branda, Martin ; Červinka, Michal ; Schwartz, A.
Document type: Research reports
Year: 2016
Language: eng
Series: Research Report, volume: 2358
Abstract: [eng] [eng]
We deal with investment problems where we minimize a risk measure\nunder a condition on the sparsity of the portfolio. Various risk measures\nare considered including Value-at-Risk and Conditional Value-at-Risk\nunder normal distribution of returns and their robust counterparts are\nderived under moment conditions, all leading to nonconvex objective\nfunctions. We propose four solution approaches: a mixed-integer formulation,\na relaxation of an alternative mixed-integer reformulation and\ntwo NLP regularizations. In a numerical study, we compare their computational\nperformance on a large number of simulated instances taken\nfrom the literature. 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.
Keywords: Conditional Value-at-Risk; risk measure; Value-at-Risk
Project no.: GA13-01930S (CEP), GA15-00735S (CEP)
Funding provider: GA ČR, GA ČR

Institution: Institute of Information Theory and Automation AS ČR (web)
Document availability information: Fulltext is available at external website.
External URL: http://library.utia.cas.cz/separaty/2016/E/branda-0468834.pdf
Original record: http://hdl.handle.net/11104/0266849

Permalink: http://www.nusl.cz/ntk/nusl-262590


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