 Analysis of credit ABS based on Markov chain approachesFengming Liu and Yingda Song Finance Research Letters, 2025, vol. 71, issue C Abstract: 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. Keywords: Credit ABS; Dynamic asset pool; Markov chain; Delinquency and default; Prepayment (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:finlet:v:71:y:2025:i:c:s1544612324014612 DOI: 10.1016/j.frl.2024.106432 Access Statistics for this article Finance Research Letters is currently edited by R. Gençay More articles in Finance Research Letters from Elsevier |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.