EconPapers    
Economics at your fingertips  
 

A dynamic binomial expansion technique for credit risk measurement: a Bayesian filtering approach

Wing Hoe Woo and Tak Kuen Siu

Applied Mathematical Finance, 2004, vol. 11, issue 2, 165-186

Abstract: Credit risk measurement and management are important and current issues in the modern finance world from both the theoretical and practical perspectives. There are two major schools of thought for credit risk analysis, namely the structural models based on the asset value model originally proposed by Merton and the intensity-based reduced form models. One of the popular credit risk models used in practice is the Binomial Expansion Technique (BET) introduced by Moody's. However, its one-period static nature and the independence assumption for credit entities' defaults are two shortcomings for the use of BET in practical situations. Davis and Lo provided elegant ways to ease the two shortcomings of BET with their default infection and dynamic continuous-time intensity-based approaches. This paper first proposes a discrete-time dynamic extension to the BET in order to incorporate the time-dependent and time-varying behaviour of default probabilities for measuring the risk of a credit risky portfolio. In reality, the 'true' default probabilities are unobservable to credit analysts and traders. Here, the uncertainties of 'true' default probabilities are incorporated in the context of a dynamic Bayesian paradigm. Numerical studies of the proposed model are provided.

Keywords: credit risk measurement; binomial expansion technique (BET); default probabilities; Bayesian filtering method; value at risk (VaR) (search for similar items in EconPapers)
Date: 2004
References: Add references at CitEc
Citations: Track citations by RSS feed

Downloads: (external link)
http://www.tandfonline.com/doi/abs/10.1080/13504860410001682669 (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:apmtfi:v:11:y:2004:i:2:p:165-186

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/RAMF20

Access Statistics for this article

Applied Mathematical Finance is currently edited by Professor Ben Hambly and Christoph Reisinger

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

 
Page updated 2019-07-26
Handle: RePEc:taf:apmtfi:v:11:y:2004:i:2:p:165-186