Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/93908
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Applied Mathematics | en_US |
dc.creator | Kang, Z | en_US |
dc.creator | Li, X | en_US |
dc.creator | Li, Z | en_US |
dc.creator | Zhu, S | en_US |
dc.date.accessioned | 2022-08-03T01:24:10Z | - |
dc.date.available | 2022-08-03T01:24:10Z | - |
dc.identifier.issn | 1469-7688 | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/93908 | - |
dc.language.iso | en | en_US |
dc.publisher | Routledge, Taylor & Francis Group | en_US |
dc.rights | © 2018 Informa UK Limited, trading as Taylor & Francis Group | en_US |
dc.rights | This is an Accepted Manuscript of an article published by Taylor & Francis in Quantitative Finance on 11 Jun 2018 (published online), available at: http://www.tandfonline.com/10.1080/14697688.2018.1466057 | en_US |
dc.subject | Bootstrap | en_US |
dc.subject | Conic programmes | en_US |
dc.subject | Distributionally robust optimization | en_US |
dc.subject | Portfolio selection | en_US |
dc.subject | Zero net adjustment | en_US |
dc.title | Data-driven robust mean-CVaR portfolio selection under distribution ambiguity | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 105 | en_US |
dc.identifier.epage | 121 | en_US |
dc.identifier.volume | 19 | en_US |
dc.identifier.issue | 1 | en_US |
dc.identifier.doi | 10.1080/14697688.2018.1466057 | en_US |
dcterms.abstract | In this paper, we present a computationally tractable optimization method for a robust mean-CVaR portfolio selection model under the condition of distribution ambiguity. We develop an extension that allows the model to capture a zero net adjustment via a linear constraint in the mean return, which can be cast as a tractable conic programme. Also, we adopt a nonparametric bootstrap approach to calibrate the levels of ambiguity and show that the portfolio strategies are relatively immune to variations in input values. Finally, we show that the resulting robust portfolio is very well diversified and superior to its non-robust counterpart in terms of portfolio stability, expected returns and turnover. The results of numerical experiments with simulated and real market data shed light on the established behaviour of our distributionally robust optimization model. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Quantitative finance, 2019, v. 19, no. 1, p. 105-121 | en_US |
dcterms.isPartOf | Quantitative finance | en_US |
dcterms.issued | 2019 | - |
dc.identifier.scopus | 2-s2.0-85048355144 | - |
dc.identifier.eissn | 1469-7696 | en_US |
dc.description.validate | 202208 bcfc | en_US |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | AMA-0315 | - |
dc.description.fundingSource | RGC | en_US |
dc.description.pubStatus | Published | en_US |
dc.identifier.OPUS | 6845054 | - |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Li_Data-driven_Robust_Mean-CVaR.pdf | Pre-Published version | 476.5 kB | Adobe PDF | View/Open |
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