Fast gradient descent method for Mean-CVaR optimization

Garud Iyengar

doi:10.1007/s10479-012-1245-8

Fast gradient descent method for Mean-CVaR optimization

2013-02-07

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AI

DOI：10.1007/s10479-012-1245-8

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Abstract

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We propose an iterative gradient descent algorithm for solving scenario-based Mean-CVaR portfolio selection problem. The algorithm is fast and does not require any LP solver. It also has efficiency advantage over the LP approach for large scenario size.

Keywords:

Conditional value-at-risk

Portfolio optimization

Fast gradient descent method for Mean-CVaR optimization

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