Unveiling Mispricing Risks: Nonlarge Homogeneous Portfolio Factor Copula Models for Enhanced Valuation of Subordinated Loan Securitization
提出一种非大组合假设的因子Copula模型，用于担保贷款凭证（CLO）分档定价，发现该模型能更准确反映低评级分档的利差，并揭示大组合模型可能高估高评级分档的利差风险。
Abstract This paper presents an innovative factor copula model for collateralized loan obligation (CLO) tranche valuation, incorporating non‐Gaussian distributions and dynamic correlations without relying on the large homogeneous portfolio (LHP) assumption. Through numerical analyses and comparisons with LHP models, I find that non‐LHP models produce higher tranche spreads, especially for lower‐rated tranches. Sensitivity analysis reveals varying sensitivities to changes in the number of collaterals, risk‐free rate, average collateral ratings, recovery rates, and time to maturity. The non‐LHP one‐factor copula models, including stochastic correlation and random factor loading models, outperform LHP models in root mean squared errors when calibrated to market data. The results underscore the importance of considering model limitations in CLO tranche pricing and highlight potential mispricing of spread risk in higher‐rated tranches using LHP models. The proposed models contribute to a more comprehensive understanding of CLO tranche pricing by accounting for various factors and assumptions influencing fair premiums.