Analysis of credit ABS based on Markov chain approaches

Credit asset-backed security (ABS) is a crucial financial instrument that plays a significant role in enhancing financial market efficiency and optimizing the social credit structure. However, pricing and analyzing credit ABS is challenging as its valuation is influenced by complex factors with path-dependency. This study proposes a modeling approach using a dynamic asset pool and derives explicit expressions from continuous-time Markov chain approximation. The method avoids accessing underlying borrowers' private information and effectively distinguishes between delinquency and default while extending the prepayment intensity form within a general Markov framework. Numerical experiments were conducted to examine the credit matrix of the underlying pool and the impact of prepayment on price, delta, and convexity. This approach demonstrates high flexibility and practicality and provides theoretical and computational support for modeling, pricing analysis, and risk management of credit ABS.