Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/93886
Title: | Mean-variance portfolio selection under partial information with drift uncertainty | Authors: | Xiong, J Xu, ZQ Zheng, J |
Issue Date: | 2021 | Source: | Quantitative finance, 2021, v. 21, no. 9, p. 1461-1473 | Abstract: | In this paper, we study the mean–variance portfolio selection problem under partial information with drift uncertainty. First we show that the market model is complete even in this case while the information is not complete and the drift is uncertain. Then, the optimal strategy based on partial information is derived, which reduces to solving a related backward stochastic differential equation (BSDE). Finally, we propose an efficient numerical scheme to approximate the optimal portfolio that is the solution of the BSDE mentioned above. Malliavin calculus and the particle representation play important roles in this scheme. | Keywords: | Drift uncertainty Malliavin calculus Mean–variance portfolio selection Partial information |
Publisher: | Routledge, Taylor & Francis Group | Journal: | Quantitative finance | ISSN: | 1469-7688 | EISSN: | 1469-7696 | DOI: | 10.1080/14697688.2021.1889650 | Rights: | © 2021 Informa UK Limited, trading as Taylor & Francis Group This is an Accepted Manuscript of an article published by Taylor & Francis in Quantitative Finance on 08 Apr 2021 (published online), available at: http://www.tandfonline.com/10.1080/14697688.2021.1889650 |
Appears in Collections: | Journal/Magazine Article |
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Xu_Mean–Variance_Portfolio_Selection.pdf | Pre-Published version | 425.69 kB | Adobe PDF | View/Open |
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