oru.sePublikationer
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Industry shocks and empirical evidences on defaults comovements
Örebro University, Swedish Business School at Örebro University.
(English)Manuscript (preprint) (Other academic)
Abstract [en]

It is commonly agreed that the credit defaults are correlated. However, the structure and magnitude of such dependence is not yet fully understood. This paper contributes to the current understanding about the defaults comovement in the following way. Assuming that the industries provides the basis of defaults comovement it provides empirical evidence as to how such comovements can be modeled using correlated industry shocks. Generalized linear mixed model (GLMM) with correlated random effects is used to model the defaults comovement. It is also demonstrated as to how a GLMM with complex correlation structure can be estimated through a very simple way. Empirical evidences are drawn through analyzing quarterly individual borrower level credit history data obtained from two major Swedish banks between the period 1994 and 2000. The results show that, conditional on the borrower level accounting data and macro business cycle variables, the defaults are correlated both within and between industries but not over time (quarters). A discussion has also been presented as to how a GLMM for defaults correlation can be explained.

Keyword [en]
Credit risk, defaults contagion, GLMM, cluster correlation
National Category
Social Sciences Probability Theory and Statistics
Research subject
Statistics
Identifiers
URN: urn:nbn:se:oru:diva-14072OAI: oai:DiVA.org:oru-14072DiVA: diva2:389401
Note

Mr Alam is also affiliated to Dalarna University, SE 781 88 Borlange, Sweden

Available from: 2011-01-19 Created: 2011-01-19 Last updated: 2016-11-21Bibliographically approved
In thesis
1. Feasible computation of generalized linear mixed models with application to credit risk modelling
Open this publication in new window or tab >>Feasible computation of generalized linear mixed models with application to credit risk modelling
2010 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis deals with developing and testing feasible computational procedures to facilitate the estimation of and carry out the prediction with the generalized linear mixed model (GLMM) with a scope of applying them to large data sets. The work of this thesis is motivated from an issue arising incredit risk modelling. We have access to a huge data set, consisting of about one million observations, on credit history obtained from two major Swedish banks. The principal research interest involved with the data analysis is to model the probability of credit defaults by incorporating the systematic dependencies among the default events. In order to model the dependent credit defaults we adopt the framework of GLMM which is apopular approach to model correlated binary data. However, existing computational procedures for GLMM did not offer us the flexibility to incorporate the desired correlation structure of defaults events.For the feasible estimation of the GLMM we propose two estimation techniques being the fixed effects (FE) approach and the two-step pseudolikelihood approach (2PL). The preciseness of the estimation techniques and their computational advantages are studied by Monte-Carlo simulations and by applying them to the credit risk modelling. Regarding the prediction issue, we show how to apply the likelihood principle to carryout prediction with GLMM. We also provide an R add-in package to facilitate the predictive inference for GLMM.

Place, publisher, year, edition, pages
Örebro: Örebro universitet, 2010. 29 p.
Series
Örebro Studies in Statistics, ISSN 1651-8608 ; 5
Keyword
Credit risk, cluster correlation, GLMM, large data, two-step pseudo likelihood estimation, defaults contagion, predictive likelihood
National Category
Social Sciences Probability Theory and Statistics
Research subject
Statistics
Identifiers
urn:nbn:se:oru:diva-12390 (URN)978-91-7668-771-0 (ISBN)
Public defence
2010-12-21, Hörsal M, Örebro universitet, Fakultetsgatan 1, 701 82 Örebro, 15:15 (English)
Opponent
Available from: 2010-11-04 Created: 2010-11-02 Last updated: 2011-04-21Bibliographically approved

Open Access in DiVA

fulltext(177 kB)148 downloads
File information
File name FULLTEXT01.pdfFile size 177 kBChecksum SHA-512
66de0deb4a85279451dac0ea948aa762ec5aecf51e98a72ecc82913a02b0154b400a6f9c74bec26f18d45d0c57ff19cbb56138ff6eda04506b3b002d6492c0d1
Type fulltextMimetype application/pdf

Other links

http://www.oru.se/PageFiles/13172/WP%202009/WP%203%2009.pdf

Search in DiVA

By author/editor
Alam, Md. Moudud
By organisation
Swedish Business School at Örebro University
Social SciencesProbability Theory and Statistics

Search outside of DiVA

GoogleGoogle Scholar
Total: 148 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Total: 126 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf