Efficient estimation of transition rates between credit ratings from observations at discrete time points

Mogens Bladt and Michael Sørensen ()

Quantitative Finance, 2009, vol. 9, issue 2, 147-160

Abstract: The paper demonstrates how discrete time credit rating data (e.g. annual observations) can be analysed by means of a continuous-time Markov model. Two methods for estimating the transition intensities are given: the EM algorithm and an MCMC approach. The estimated transition intensities can be used to estimate the matrix of probabilities of transitions between all credit ratings, including default probabilities, over any time horizon. Thus the advantages of a continuous-time model can be obtained without continuous-time data. Estimates of the variance of estimators as well as confidence and credibility intervals are presented, and a test for equality of two intensity matrices is proposed. The methods are demonstrated by analysis of a large data set drawn from Moody's Corporate Bond Default Database, where reasonable estimates are obtained from annual observations.

Keywords: Continuous-time Markov chains; Confidence sets; Default probabilities; EM algorithm; Markov chain Monte Carlo (search for similar items in EconPapers)
Date: 2009
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

Downloads: (external link)
http://www.tandfonline.com/doi/abs/10.1080/14697680802624948 (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:9:y:2009:i:2:p:147-160

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

DOI: 10.1080/14697680802624948

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 ().