满足准则下的分布鲁棒稀疏投资组合优化模型

IF 1.3 Q3 COMPUTER SCIENCE, THEORY & METHODS
Zhongyan Wang, Xiaodong Zhu, Shaojian Qu, M. Faisal Nadeem, Beibei Zhang
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引用次数: 0

摘要

在满足准则下,提出了一个具有基数约束的分布鲁棒投资组合优化模型。我们的目标是在投资者持有的资产数量有限的情况下,使所提出的投资组合选择模型实现目标收益的概率最大化。从实际意义上讲,我们引入了投资组合优化问题的缺口感知期望水平度量,并将其转化为CVaR度量。在我们的模型中,我们考虑最坏情况,并假设资产收益的分布是模糊的。由于其可追溯性,我们将基于cvar的度量等效地重新表述为半确定规划。设计了一种Benders分解算法来有效求解该模型。通过实际市场数据进行了数值试验,验证了所提方法的有效性。结果表明,我们的算法能够有效地求解所提出的模型,满足条件下的稀疏投资组合模型具有较高的鲁棒性,性能优于经典模型。进一步证明了以资产数量作为决策变量是一种更为有效的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Distributionally robust sparse portfolio optimization model under satisfaction criterion
We propose a distributionally robust portfolio optimization model with cardinality constraints under the satisfaction criterion. We aim to maximize the probability of achieving the target return of the proposed portfolio selection model while the number of assets the investors hold is limited. For practical significance, we cite a measure of shortfall-aware aspiration level to the portfolio optimization problem and convert it into a CVaR measure. In our model, we consider a worst-case and assume the distribution of returns of assets is ambiguous. We reformulate the CVaR-based measure equivalently to semi-definite programming for its tractability. A Benders' decomposition algorithm is designed to solve the proposed model efficiently. Numerical tests are utilized through actual market data to validate the proposed method. The results indicate that our algorithm can effectively solve the proposed model, and the sparse portfolio selection model under the satisfaction criterion achieves high robustness and perform better than classical models. Furthermore, we prove that taking the number of assets as the decision variable is a much more efficient method.
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CiteScore
1.50
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