A new method to predict economic capital for the credit risk of a lending portfolio

Viani Biatat Djeundje and Jonathan Crook

Journal of the Operational Research Society, 2025, vol. 76, issue 7, 1432-1448

Abstract: We propose a method to compute the Value at Risk for a loan portfolio that involves predicting outstanding balance and repayment amounts, but not the probability of default directly. By modelling the behaviour of the borrowers in terms of repayments, balance and a default condition, we model the default occurrence more accurately than if we model the occurrence of default directly. We find that whilst including random effects increases the predictive accuracy of individual account performances, relatively simple structures give the most accurate predictions. We also find that, in terms of value at risk relative to expected loss, more complex random effects predict lower values at risk at more distant duration times whilst the omission of random effects leads to increasing values at risk over time. We compare the predicted amount of capital a bank should hold under our approach and under a standard approach and find that under certain circumstances, our approach indicates that considerably more capital should be held.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/01605682.2024.2437568 (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:tjorxx:v:76:y:2025:i:7:p:1432-1448

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/tjor20

DOI: 10.1080/01605682.2024.2437568

Access Statistics for this article

Journal of the Operational Research Society is currently edited by Tom Archibald

More articles in Journal of the Operational Research Society from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().