This dissertation consists of three independent but thematically connected essays that explore the pricing dynamics, structural fragility, and forecasting challenges in the credit derivatives market—specifically focusing on credit default swaps (CDS). Each paper investigates a unique dimension of credit market inefficiency, emphasizing the role of market opacity, structural fragmentation, and strategic behavior in shaping prices and systemic risk.

The first paper develops a theoretical and simulation-based model of opaque CDS networks, showing how limited price transparency and strategic predation amplify spread volatility through nonlinear feedback and contagion effects. The second paper focuses on interoperability between central counterparties (CCPs), presenting a novel model that illustrates how linkages between CCPs can exacerbate fragmentation, increase inter-CCP price dispersion, and shift systemic risk dynamics. The third paper introduces a global machine learning forecasting model for single-name CDS, demonstrating that the structural relationships in the CDS network can be learned and exploited to forecast future spread movements more accurately than traditional models.

Together, these papers advance our understanding of how institutional frictions, strategic interaction, and information asymmetries affect credit risk pricing. The results offer novel insights for risk managers, regulators, and academics by proposing mechanisms for mitigating volatility, improving market design, and enhancing credit forecasting through data-driven, yet economically grounded, models.