Portfolio optimization using second order conic programming approach

Jagdeep Kaur Brar, Warren Hare


In this paper, we examine the framework to estimate financial risk called conditional-value-at-risk (CVaR) and examine models to optimize portfolios by minimizing CVaR. We note that total risk can be a function of multiple risk factors combined in a linear or nonlinear forms. We demonstrate that, when using CVaR, several common nonlinear models can be expressed as second order cone programming problems and therefore efficiently solved using modern algorithms. This property is not shared with the more classical estimation of financial risk based on value-at-risk.

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Published: 2020-03-22

How to Cite this Article:

Jagdeep Kaur Brar, Warren Hare, Portfolio optimization using second order conic programming approach, Math. Finance Lett., 2021 (2021), Article ID 1

Copyright © 2021 Jagdeep Kaur Brar, Warren Hare. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Mathematical Finance Letters

ISSN 2051-2929

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