Learning probability of default and stress testing

Luca Nocciola and Samuele Scaglioni

No 3277, Working Paper Series from European Central Bank

Abstract: We analyze the Probability of Default (PD) of non-financial corporations in Europe using Random Forests (RF) and assess implications for stress testing the banking sector. To this end, we exploit data on firms’ financial statements (Orbis) and banks’ credit registry (Anacredit). We show that RF displays stronger risk sensitivity than logistic regression in stress testing, shedding new light on the non-linear effect of scenario severity on PD. Moreover, we show how RF-based PD can be used in a network of banks and firms to stress test the banking sector through loan exposures as a key transmission channel of adverse scenarios. A granular inspection of banks’ riskiness indices derived from this network sheds light also on RF’s superior ability in capturing non-linearity thanks to its capability in identifying “tail banks”. Our work is relevant for central banks and banking supervisors alike. JEL Classification: C53, C55, C58, G17, G21

Keywords: banking supervision; corporate exposures; credit risk; financial stability; random forests (search for similar items in EconPapers)
Date: 2026-08
New Economics Papers: this item is included in nep-eur
Note: 2600378
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.ecb.europa.eu//pub/pdf/scpwps/ecb.wp3277~46bd788158.en.pdf (application/pdf)

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:ecb:ecbwps:20263277

Access Statistics for this paper

More papers in Working Paper Series from European Central Bank 60640 Frankfurt am Main, Germany. Contact information at EDIRC.
Bibliographic data for series maintained by Official Publications ().