Rating frailty, Bayesian updates, and portfolio credit risk analysis*

Shang Bu, Nan Guo and Lingfei Li

Quantitative Finance, 2022, vol. 22, issue 4, 777-797

Abstract: This paper studies how to utilize individual ratings and credit performance for portfolio credit risk analysis and surveillance. We model the default intensity of firms using a proportional form, with rating specific individual frailty to account for heterogeneity within a rating group, as well as rating specific exposure to observable macro covariates, industries and a latent mean-reverting macro frailty factor. To estimate the model, we take the Bayesian approach and develop a Markov chain Monte Carlo-based algorithm. This approach enables us to quantify parameter uncertainty which is crucial for forecasting and it also provides a convenient tool for performing updates. Using a large default dataset spanning a period of 45 years including the 2008 financial crisis, we provide strong evidence for the dependence of individual frailty and exposure to systematic risk factors on credit rating. In out-of-sample testing, we showcase the ability of our model to forecast the number of defaults through business cycles and particularly in the financial crisis. Furthermore, by monitoring a collateralized loan obligation (CLO), we show that our model can perform reasonably well for the surveillance purpose with timely updates, even if the data used for the initial calibration of the model does not contain the firms in the CLO.

Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/14697688.2021.2013519 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:quantf:v:22:y:2022:i:4:p:777-797

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/RQUF20

DOI: 10.1080/14697688.2021.2013519

Access Statistics for this article

Quantitative Finance is currently edited by Michael Dempster and Jim Gatheral

More articles in Quantitative Finance from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().