A sequential elimination approach to value-at-risk and conditional value-at-risk selection
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Authors
Hepworth, Adam J.
Atkinson, Michael P.
Szechtman, Roberto
Subjects
Advisors
Date of Issue
2017-12
Date
Publisher
IEEE
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Abstract
Conditional Value-at-Risk (CVaR) is a widely used metric of risk in portfolio analysis, interpreted as the expected loss when the loss is larger than a threshold defined by a quantile. This work is motivated by situations where the CVaR is given, and the objective is to find the portfolio with the largest or smallest quantile that meets the CVaR constraint. We define our problem within the classic stochastic multi-armed bandit (MAB) framework, and present two algorithms. One that can be used to find the portfolio with largest or smallest loss threshold that satisfies the CVaR constraint with high probability, and another that determines the portfolio with largest or smallest probability of exceeding a loss threshold implied by a CVaR constraint, also at some desired probability level.
Type
Conference Paper
Description
The article of record as published may be found at https://doi.org/10.1109/WSC.2017.8247963
From Proceedings of the 2017 Winter Simulation Conference, W. K. V. Chan, A. D’Ambrogio, G. Zacharewicz, N. Mustafee, G. Wainer, and E. Page, eds.
From Proceedings of the 2017 Winter Simulation Conference, W. K. V. Chan, A. D’Ambrogio, G. Zacharewicz, N. Mustafee, G. Wainer, and E. Page, eds.
Series/Report No
Department
Operations Research (OR)
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Format
12 p.
Citation
Hepworth, Adam J., Michael P. Atkinson, and Roberto Szechtman. "A sequential elimination approach to value-at-risk and conditional value-at-risk selection." 2017 Winter Simulation Conference (WSC). IEEE, 2017.
Distribution Statement
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.