Proposing an innovative real-time monitoring approach for the parameter stability of credit-default prediction model

Linzi Liu, Xin Lai, Xiaofang Cai, Wei (Wayne) Huang and Weimin Miao

Journal of the Operational Research Society, 2024, vol. 75, issue 11, 2115-2126

Abstract: In real-world applications, the default prediction process generally requires periodic recalibration of parameter estimates with up-to-date data. However, few studies have investigated sudden changes or unknown structural breaks in the regularly estimated parameters, which could lead to biases in the models and thus affect the prediction accuracy. The instability of estimated parameters has been shown to under- or overestimate prediction results, but their real-time monitoring remains unexplored. This paper aims to fill this research gap by monitoring the estimated parameter stability in default prediction models in real time using statistical process control methods. We innovatively transform this issue into a research problem of profile monitoring and examine the monitoring performance of three control charts using Monte Carlo simulation. Simulation experiments demonstrate that the self-starting multivariate exponentially weighted moving average control chart outperforms other charts in detecting speed and range. It can also predict credit crises, detect more minor changes in risk, and describe their changing trends in real time. Throughout this article, we employ a forward intensity approach, which has been put into long-term applications for corporate default predictions, to illustrate the profile monitoring process.

Date: 2024
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://hdl.handle.net/10.1080/01605682.2024.2305157 (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:75:y:2024:i:11:p:2115-2126

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

DOI: 10.1080/01605682.2024.2305157

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 ().