CVaR minimization by the SRA algorithm

Ágoston, Kolos Csaba

doi:10.1007/s10100-011-0194-7

Cited by 7 publications

(2 citation statements)

References 12 publications

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“…Fábián [12] proposes decomposition frameworks for handling CVaR objectives and constraint in two-stage stochastic model. Ágoston [3] uses SRA algorithm for solving CVaR minimization problems. Bardou [6] solves CVaR hedging using quantization based stochastic approximation algorithm.…”

Section: ð1:1þmentioning

confidence: 99%

Risk management in portfolio applications of non-convex stochastic programming

Pang

Chen

Wang

2015

Applied Mathematics and Computation

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Section: ð1:1þmentioning

confidence: 99%

Risk management in portfolio applications of non-convex stochastic programming

Pang

Chen

Wang

2015

Applied Mathematics and Computation

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“…These facts have highlighted the importance of CVaR for making decisions in uncertain situations. In fact, the mean-CVaR model has been widely studied to solve efficiently, e.g., linear optimization approaches [22,27], nonsmooth optimization approaches [7,18,24,30], scenario representation by factor model [21], cutting-plane algorithms [2,17,23,33], level method [13], smoothing methods [3,35] and successive regression approximations [1].…”

Section: Introductionmentioning

confidence: 99%

Bilevel Cutting-plane Algorithm for Solving Cardinality-constrained Mean-CVaR Portfolio Optimization Problems

Kobayashi,

Takano,

Nakata

2020

Preprint

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This paper studies the mean-risk portfolio optimization problems with a constraint of the number of assets to be invested. We employ conditional value-at-risk (CVaR) as a risk measure. While several studies aim at efficiently solving mean-CVaR model, it has been computationally expensive to solve cardinality-constrained mean-CVaR model, and especially for exact algorithms, the size of a problem that can be handled has been limited. In this paper, we devised an exact and efficient algorithm to handle a largesized mean-CVaR optimization problem with a cardinality constraint. For computational efficiency, our proposed method integrates two cutting-plane algorithms, upper-level cutting-plane algorithm and lower-level one, and each algorithm solves the master problem and the subproblem, respectively. Numerical experiments demonstrated that our algorithms can attain optimal solutions to large-scale problems in a reasonable amount of time.

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