On the accuracy of alternative approaches for calibrating bank stress test models

Paul Kupiec

Journal of Financial Stability, 2018, vol. 38, issue C, 132-146

Abstract: Multi-year forecasts of bank performance under stressful economic conditions determine large institution regulatory capital requirements and yet the accuracy of these forecasts is undocumented. I compare the accuracies of alternative stress test model forecasts using the financial crisis as the stress scenario. Models include specifications that mimic the Federal Reserve CLASS model and alternatives that use Lasso, the AIC and an abridged set of explanatory variables. A simple single-equation Lasso model has, by far, the best forecast accuracy. Large differences in model forecast accuracy are undetectable from estimation sample statistics. These findings highlight the need for new methods for validating bank stress test models.

Keywords: Bank stress tests; Lasso (search for similar items in EconPapers)
JEL-codes: C52 C53 G28 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S157230891730195X
Full text for ScienceDirect subscribers only

Related works:
Working Paper: On the accuracy of alternative approaches for calibrating bank stress test models (2018)
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:eee:finsta:v:38:y:2018:i:c:p:132-146

DOI: 10.1016/j.jfs.2018.08.001

Access Statistics for this article

Journal of Financial Stability is currently edited by I. Hasan, W. C. Hunter and G. G. Kaufman

More articles in Journal of Financial Stability from Elsevier
Bibliographic data for series maintained by Catherine Liu ().