Parameterizing credit risk models with rating data

Mark Carey and Mark Hrycay

No 2000-47, Finance and Economics Discussion Series from Board of Governors of the Federal Reserve System (U.S.)

Abstract: Estimates of average default probabilities for borrowers assigned to each of a financial institution's internal credit risk rating grades are crucial inputs to portfolio credit risk models. Such models are increasingly used in setting financial institution capital structure, in internal control and compensation systems, in asset-backed security design, and are being considered for use in setting regulatory capital requirements for banks. This paper empirically examines properties of the major methods currently used to estimate average default probabilities by grade. Evidence of potential problems of bias, instability, and gaming is presented. With care, and perhaps judicious application of multiple methods, satisfactory estimates may be possible. In passing, evidence is presented about other properties of internal and rating-agency ratings.

Keywords: Credit; Risk management; Credit ratings (search for similar items in EconPapers)
Date: 2000
New Economics Papers: this item is included in nep-cfn, nep-fmk and nep-ias
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

Downloads: (external link)
http://www.federalreserve.gov/pubs/feds/2000/200047/200047abs.html (text/html)
http://www.federalreserve.gov/pubs/feds/2000/200047/200047pap.pdf (application/pdf)

Related works:
Journal Article: Parameterizing credit risk models with rating data (2001)
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:fip:fedgfe:2000-47

Access Statistics for this paper

More papers in Finance and Economics Discussion Series from Board of Governors of the Federal Reserve System (U.S.) Contact information at EDIRC.
Bibliographic data for series maintained by Ryan Wolfslayer ; Keisha Fournillier ().