We present a multi-objective portfolio optimization framework that accounts for both systemic risk arising from overlapping portfolios and individual risk. To address non-convexity in the objective function, we introduce an Evolutionary Search algorithm that enables efficient exploration of the solution space. Applying our framework to EBA data on sovereign exposures, we find that minimizing systemic risk results in highly concentrated and diverse portfolios, adding empirical evidence to a growing literature on the ambiguous effects of diversification on systemic risk. In contrast, individual risk optimal allocations exhibit high portfolio diversification and homogeneity. By characterizing a set of Pareto frontiers, we identify a trade-off between the two risk components. Even a small preference for minimizing systemic risk leads to optimal portfolios on the frontier that differ significantly from the observed ones, suggesting potential inefficiencies in actual portfolio structures.

中文翻译：

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重叠投资组合的系统性风险：多目标优化框架


我们提出了一个多目标投资组合优化框架，该框架既考虑了重叠投资组合产生的系统性风险，也考虑了单个风险。为了解决目标函数中的非凸性问题，我们引入了一种进化搜索算法，可以有效地探索解决方案空间。将我们的框架应用于主权风险敞口的 EBA 数据，我们发现，最大限度地降低系统性风险会导致高度集中和多样化的投资组合，为越来越多的关于多元化对系统性风险的模糊影响的文献增加了实证证据。相比之下，单个风险最优配置表现出高度的投资组合多元化和同质性。通过描述一组帕累托边界，我们确定了两个风险组成部分之间的权衡。即使是对最小化系统性风险的微小偏好，也会导致前沿的最佳投资组合与观察到的投资组合大相径庭，这表明实际投资组合结构可能存在效率低下的情况。