Portfolio construction with Gaussian mixture returns and exponential utility via convex optimization

IF 2 3区工程技术 Q2 ENGINEERING, MULTIDISCIPLINARY

Optimization and Engineering Pub Date : 2023-07-27 DOI:10.1007/s11081-023-09814-y

Eric Luxenberg, Stephen Boyd

引用次数: 0

Abstract

We consider the problem of choosing an optimal portfolio, assuming the asset returns have a Gaussian mixture distribution, with the objective of maximizing expected exponential utility. In this paper we show that this problem is convex, and readily solved exactly using domain-specific languages for convex optimization, without the need for sampling or scenarios. We then show how the closely related problem of minimizing entropic value at risk can also be formulated as a convex optimization problem.

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基于凸优化的高斯混合收益和指数效用组合构建

我们考虑选择最优投资组合的问题，假设资产收益具有高斯混合分布，目标是期望指数效用最大化。在本文中，我们证明了这个问题是凸的，并且很容易使用特定于领域的语言进行凸优化，而不需要采样或场景。然后，我们展示了如何密切相关的最小化风险熵值问题也可以表述为凸优化问题。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Optimization and Engineering 工程技术-工程：综合

CiteScore

4.80

自引率

14.30%

发文量

审稿时长

>12 weeks

期刊介绍： Optimization and Engineering is a multidisciplinary journal; its primary goal is to promote the application of optimization methods in the general area of engineering sciences. We expect submissions to OPTE not only to make a significant optimization contribution but also to impact a specific engineering application. Topics of Interest: -Optimization: All methods and algorithms of mathematical optimization, including blackbox and derivative-free optimization, continuous optimization, discrete optimization, global optimization, linear and conic optimization, multiobjective optimization, PDE-constrained optimization & control, and stochastic optimization. Numerical and implementation issues, optimization software, benchmarking, and case studies. -Engineering Sciences: Aerospace engineering, biomedical engineering, chemical & process engineering, civil, environmental, & architectural engineering, electrical engineering, financial engineering, geosciences, healthcare engineering, industrial & systems engineering, mechanical engineering & MDO, and robotics.