Valuation of Cyber Catastrophe Bonds and Their Role in Portfolio Efficiency: An Analysis of Model Selection and Investment Implications

This study evaluates and compares five pricing models for cyber catastrophe (cat) bonds—the Loss Distribution Framework, Unified Bayesian Framework, Signal-Processing Approach, Regression Approach, and Copula-POT Model—to identify the most effective method for loss prediction and portfolio optimization under Modern Portfolio Theory (MPT). Using CISSM data, the Copula-POT Model shows the highest predictive accuracy and robustness, making it the preferred framework despite a zero trigger probability due to limited extreme value data. Integrating cyber cat bonds priced by this model into an MPT-optimized portfolio improves diversification and risk-adjusted returns, outperforming traditional high-yield bonds. The study highlights challenges including data scarcity, parameter sensitivity, and model uncertainty, and proposes hybrid modeling and data enrichment as directions for future research. Overall, these findings emphasize the potential of cyber cat bonds as an innovative asset class and an effective tool for cyber risk transfer in capital markets.