Systemic risk from overlapping portfolios: A multi-objective optimization framework
提出了一个多目标投资组合优化框架，同时考虑重叠投资组合导致的系统性风险和个体风险，并利用进化搜索算法解决非凸性问题。基于欧洲银行管理局主权风险暴露数据的实证分析发现，最小化系统性风险会导致高度集中且多样化的投资组合，而个体风险最优配置则呈现高分散性和同质性。帕累托前沿揭示了两种风险之间的权衡，且对系统性风险的微小偏好就会使最优组合显著偏离实际观察到的组合，表明实际投资组合结构可能存在低效。
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. • We propose a portfolio optimization framework balancing systemic and individual risk. • We develop a heuristic algorithm to address non-convexities in the problem. • We show that optimizing systemic risk generates concentrated portfolios. • Pareto frontiers reveal a trade-off between individual and systemic risk. • Our empirical application suggests inefficiencies in EU banks’ sovereign portfolios.