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Cyclical default and recovery in stress testing loan losses

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  • Jokivuolle, Esa
  • Virén, Matti

Abstract

We present a macro variable-based empirical model for corporate bank loans’ credit risk. The model captures the well-known positive relationship between probability of default (PD) and loss given default (LGD; i.e., the inverse of recovery) and their counter-cyclical movement with the business cycle. In the absence of proper micro data on LGD, we use a random-sampling method to estimate the annual average LGD. We specify a two equation model for PD and LGD which is estimated with Finnish time-series data from 1989 to 2008. We also use a system of time-series models for the exogenous macro variables to derive the main macroeconomic shocks which are then used in stress testing aggregate loan losses. We show that the endogenous LGD makes a considerable difference in stress tests compared to a constant LGD assumption.

Suggested Citation

  • Jokivuolle, Esa & Virén, Matti, 2013. "Cyclical default and recovery in stress testing loan losses," Journal of Financial Stability, Elsevier, vol. 9(1), pages 139-149.
    • Handle: RePEc:eee:finsta:v:9:y:2013:i:1:p:139-149
    DOI: 10.1016/j.jfs.2011.10.001
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    1. Jokivuolle, Esa & Pesola, Jarmo & Viren, Matti, 2015. "Why is credit-to-GDP a good measure for setting countercyclical capital buffers?," Journal of Financial Stability, Elsevier, vol. 18(C), pages 117-126.
    2. Dobromił Serwa, 2013. "Measuring Non-Performing Loans During (and After) Credit Booms," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 5(3), pages 163-183, September.
    3. Collins, Sean & Gallagher, Emily, 2016. "Assessing the credit risk of money market funds during the eurozone crisis," Journal of Financial Stability, Elsevier, vol. 25(C), pages 150-165.
    4. Pliszka, Kamil, 2021. "System-wide and banks' internal stress tests: Regulatory requirements and literature review," Discussion Papers 19/2021, Deutsche Bundesbank.
    5. Wang, Zheqi & Crook, Jonathan & Andreeva, Galina, 2020. "Reducing estimation risk using a Bayesian posterior distribution approach: Application to stress testing mortgage loan default," European Journal of Operational Research, Elsevier, vol. 287(2), pages 725-738.
    6. Nigmonov, Asror & Shams, Syed & Urbonas, Povilas, 2024. "Estimating probability of default via delinquencies? Evidence from European P2P lending market," Global Finance Journal, Elsevier, vol. 63(C).
    7. Busch, Ramona & Koziol, Philipp & Mitrovic, Marc, 2015. "Many a little makes a mickle: Macro portfolio stress test for small and medium-sized German banks," Discussion Papers 23/2015, Deutsche Bundesbank.
    8. Yao, Xiao & Crook, Jonathan & Andreeva, Galina, 2017. "Is it obligor or instrument that explains recovery rate: Evidence from US corporate bond," Journal of Financial Stability, Elsevier, vol. 28(C), pages 1-15.
    9. Memmel, Christoph & Gündüz, Yalin & Raupach, Peter, 2015. "The common drivers of default risk," Journal of Financial Stability, Elsevier, vol. 16(C), pages 232-247.
    10. Mora, Nada, 2015. "Creditor recovery: The macroeconomic dependence of industry equilibrium," Journal of Financial Stability, Elsevier, vol. 18(C), pages 172-186.
    11. Barbagli, Matteo & Vrins, Frédéric, 2023. "Accounting for PD-LGD dependency: A tractable extension to the Basel ASRF framework," Economic Modelling, Elsevier, vol. 125(C).
    12. Ferrari, Stijn & Van Roy, Patrick & Vespro, Cristina, 2021. "Sensitivity of credit risk stress test results: Modelling issues with an application to Belgium," Journal of Financial Stability, Elsevier, vol. 52(C).
    13. Busch, Ramona & Koziol, Philipp & Mitrovic, Marc, 2018. "Many a little makes a mickle: Stress testing small and medium-sized German banks," The Quarterly Review of Economics and Finance, Elsevier, vol. 68(C), pages 237-253.
    14. Abudy, Menachem Meni & Raviv, Alon, 2016. "How much can illiquidity affect corporate debt yield spread?," Journal of Financial Stability, Elsevier, vol. 25(C), pages 58-69.
    15. Fang, Yiwei & van Lelyveld, Iman, 2014. "Geographic diversification in banking," Journal of Financial Stability, Elsevier, vol. 15(C), pages 172-181.
    16. Konstantin Belyaev & Aelita Belyaeva & Tomas Konecny & Jakub Seidler & Martin Vojtek, 2012. "Macroeconomic Factors as Drivers of LGD Prediction: Empirical Evidence from the Czech Republic," Working Papers 2012/12, Czech National Bank, Research and Statistics Department.

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    Keywords

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    JEL classification:

    • G01 - Financial Economics - - General - - - Financial Crises
    • 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
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

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