Systemic risk in the insurance sector: A semi‐parametric approach based on Spearman's rho

Abstract We propose a new method to measure systemic risk in the global insurance sector by analyzing interconnectedness among firms under different market conditions. Using a semi‐parametric approach that relies on the Spearman correlation and copula‐based partial dependence, we assess relationships in relatively stable, extremely bullish, and extremely bearish markets. Our approach provides a more flexible and robust framework than traditional methods that rely on linear correlation and the multivariate normal distribution. We show that geographic proximity and shared stock exchanges drive interconnectedness, while the Russo–Ukrainian war had a notable impact on the sector's network under relatively stable market conditions.