DOI: 10.36347/sjebm.2017.v04i12.009

Pages: 919-926

This study investigates the modeling of default correlations using copula-based approaches, focusing on the application of the factor copula model developed by Ackerer and Vatter (2016). Traditional linear correlation models often underestimate joint credit risk, especially under stress conditions, making them inadequate for pricing and risk assessment in structured finance. This paper applies a factor copula framework to a simulated dataset representing tranche-level losses across credit portfolios. Using three illustrative figures, the analysis explores cumulative losses, inter-tranche dependencies, and loss concentration dynamics. The results demonstrate the ability of copula models to capture nonlinear dependencies and tail events more effectively than conventional techniques. Tranche-level simulations confirm increasing exposure to systemic risk from senior to junior tranches, while the correlation matrix reveals strong co-movement among subordinated instruments. The study contributes to the field by visualizing model behavior in formats accessible for educational and operational use, offering practical insights for risk managers and policymakers. It concludes with recommendations for dynamic model calibration and future research directions involving machine learning and time-varying copulas. Overall, the copula-based methodology offers a robust alternative for modern credit risk management.