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.

中文翻译：

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基于马尔可夫链方法的信用 ABS 分析


信贷资产支持证券 （ABS） 是一种重要的金融工具，在提高金融市场效率和优化社会信用结构方面发挥着重要作用。然而，信用 ABS 的定价和分析具有挑战性，因为其估值受路径依赖性复杂因素的影响。本研究提出了一种使用动态资产池的建模方法，并从连续时间马尔可夫链近似中得出显式表达式。该方法避免了访问基础借款人的私人信息，并有效地区分了拖欠和违约，同时在一般的马尔可夫框架内扩展了提前还款强度表格。进行了数值实验以检查基础池的信用矩阵以及提前还款对价格、delta 和凸性的影响。该方法具有高度的灵活性和实用性，为信用ABS的建模、定价分析和风险管理提供了理论和计算支持。