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Learning probability of default and stress testing

Author

Listed:
  • Nocciola, Luca
  • Scaglioni, Samuele

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

Suggested Citation

  • Nocciola, Luca & Scaglioni, Samuele, 2026. "Learning probability of default and stress testing," Working Paper Series 3277, European Central Bank.
  • Handle: RePEc:ecb:ecbwps:20263277
    Note: 2600378
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    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

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