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Spatial dimension of the credit risk: spatial filter approach
Jönköping University, Jönköping International Business School, JIBS, Center for Family Enterprise and Ownership (CeFEO). Jönköping University, Jönköping International Business School, JIBS, Economics.ORCID iD: 0000-0001-5776-9396
Jönköping University, Jönköping International Business School, JIBS, Economics.
2017 (English)Conference paper, Published paper (Refereed)
Abstract [en]

In this research it was shown that, in general, spatial filter enhance the fit and moderately improve the prediction of the logit credit risk model. It was observed that the fit and prediction results depend on the created weight matrix when using spatial filtering. With the increase of the neighbor links, the prediction by the spatial model increase and slightly outperform the base model. Detected positive autocorrelation indicate the existence of clusters of defaults within geographical area, which could confirm the need for use of spatial filter or other spatial techniques. Also, existence of positive spatial pattern in the credit risk assessment could be taken in consideration by the national banking regulators (central banks) and appropriately treated in the regulation, so that estimated credit risk parameters reflect the true risk condition of the companies and their microeconomic surrounding.

Place, publisher, year, edition, pages
2017.
Keywords [en]
credit risk, probability of default, binary choice models, logit, spatial filter
National Category
Economics
Identifiers
URN: urn:nbn:se:hj:diva-36682OAI: oai:DiVA.org:hj-36682DiVA, id: diva2:1121505
Conference
6th Malmö Real Estate Research Conference, May 18th - 19th, 2017, Malmö, Sweden
Available from: 2017-07-11 Created: 2017-07-11 Last updated: 2018-01-15Bibliographically approved

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Language
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  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
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  • text
  • asciidoc
  • rtf