Loss-Aversion with Kinked Linear Utility Functions

Best, Michael J.; Grauer, Robert R.; Hlouskova, JaroslavaORCID: https://orcid.org/0000-0002-2298-0068 and Zhang, Xili (2014) Loss-Aversion with Kinked Linear Utility Functions. Computational Economics, 44 (1), pp. 45-65. https://doi.org/10.1007/s10614-013-9391-x

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Prospect theory postulates that the utility function is characterized by a kink (a point of non-differentiability) that distinguishes gains from losses. In this paper we present an algorithm that efficiently solves the linear version of the kinked-utility problem. First, we transform the non-differentiable kinked linear-utility problem into a higher dimensional, differentiable, linear program. Second, we introduce an efficient algorithm that solves the higher dimensional linear program in a smaller dimensional space. Third, we employ a numerical example to show that solving the problem with our algorithm is 15 times faster than solving the higher dimensional linear program using the interior point method of Mosek. Finally, we provide a direct link between the piece-wise linear programming problem and conditional value-at-risk, a downside risk measure. (author's abstract)

Item Type: Article in Academic Journal
Keywords: Prospect theory, Kinked linear utility, Portfolio optimization, Linear programming
Date Deposited: 19 Feb 2015 11:40
Last Modified: 13 Dec 2023 12:45
DOI: 10.1007/s10614-013-9391-x
ISSN: 0927-7099
URI: https://irihs.ihs.ac.at/id/eprint/3044

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