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Probability of Default Model to Estimate Ex Ante Credit Risk

Author

Listed:
  • Anna Burova

    (Bank of Russia)

  • Henry Penikas

    (Bank of Russia, Higher School of Economics, Lebedev Physics Institute)

  • Svetlana Popova

    (Bank of Russia)

Abstract

A genuine measure of ex ante credit risk links borrower’s financial position with the odds of default. Comprehension of a borrower’s financial position is proxied by the derivatives of its filled financial statements, i.e. financial ratios. We identify statistically significant relationships between shortlisted financial ratios and subsequent default events and develop a probability of default (PD) model that assesses the likelihood of a borrower going into delinquency at a one year horizon. We compare the PD model constructed against alternative measures of ex ante credit risk that are widely used in related literature on bank risk taking, i.e. credit quality groups (prudential reserve ratios) assigned to creditors by banks and the credit spreads in interest rates. We find that the PD model predicts default events more accurately at a horizon of one year compared to prudential reserve rates. We conclude that the measure of ex ante credit risk developed is feasible for estimating risk-taking behaviour by banks and analysing shifts in portfolio composition.

Suggested Citation

  • Anna Burova & Henry Penikas & Svetlana Popova, 2021. "Probability of Default Model to Estimate Ex Ante Credit Risk," Russian Journal of Money and Finance, Bank of Russia, vol. 80(3), pages 49-72, September.
  • Handle: RePEc:bkr:journl:v:80:y:2021:i:3:p:49-72
    DOI: 10.31477/rjmf.202103.49
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    1. Mite Miteski & Ana Mitreska & Mihajlo Vaskov, 2019. "The risk-taking channel of monetary policy in Macedonia: evidence from credit registry data," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Are post-crisis statistical initiatives completed?, volume 49, Bank for International Settlements.
    2. Bruno, Valentina & Shin, Hyun Song, 2015. "Capital flows and the risk-taking channel of monetary policy," Journal of Monetary Economics, Elsevier, vol. 71(C), pages 119-132.
    3. Andrikopoulos, Panagiotis & Khorasgani, Amir, 2018. "Predicting unlisted SMEs' default: Incorporating market information on accounting-based models for improved accuracy," The British Accounting Review, Elsevier, vol. 50(5), pages 559-573.
    4. Edward I. Altman, 1968. "The Prediction Of Corporate Bankruptcy: A Discriminant Analysis," Journal of Finance, American Finance Association, vol. 23(1), pages 193-194, March.
    5. B. Luppi & M. Marzo & E. Scorcu, 2007. "A credit risk model for Italian SMEs," Working Papers 600, Dipartimento Scienze Economiche, Universita' di Bologna.
    6. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure - Reply," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 123-127.
    7. Denis Shibitov & Mariam Mamedli, 2019. "The finer points of model comparison in machine learning: forecasting based on russian banks’ data," Bank of Russia Working Paper Series wps43, Bank of Russia.
    8. Anna Burova, 2022. "Measuring the Debt Service Ratio in Russia: A Micro-Level Data Approach," Russian Journal of Money and Finance, Bank of Russia, vol. 81(3), pages 72-88, September.
    9. Stephen G. Cecchetti & Lianfa Li, 2008. "Do Capital Adequacy Requirements Matter For Monetary Policy?," Economic Inquiry, Western Economic Association International, vol. 46(4), pages 643-659, October.
    10. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 71-111.
    11. Dean Fantazzini & Silvia Figini, 2009. "Random Survival Forests Models for SME Credit Risk Measurement," Methodology and Computing in Applied Probability, Springer, vol. 11(1), pages 29-45, March.
    12. Zhivaikina, A. & Peresetsky, A., 2017. "Russian Bank Credit Ratings and Bank License Withdrawal 2012-2016," Journal of the New Economic Association, New Economic Association, vol. 36(4), pages 49-80.
    13. Paligorova, Teodora & Santos, João A.C., 2017. "Monetary policy and bank risk-taking: Evidence from the corporate loan market," Journal of Financial Intermediation, Elsevier, vol. 30(C), pages 35-49.
    14. Pompe, Paul P.M. & Bilderbeek, Jan, 2005. "The prediction of bankruptcy of small- and medium-sized industrial firms," Journal of Business Venturing, Elsevier, vol. 20(6), pages 847-868, November.
    15. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    16. A. Colin Cameron & Pravin K. Trivedi, 2010. "Microeconometrics Using Stata, Revised Edition," Stata Press books, StataCorp LP, number musr, March.
    17. Ioannidou, Vasso P. & Penas, María Fabiana, 2010. "Deposit insurance and bank risk-taking: Evidence from internal loan ratings," Journal of Financial Intermediation, Elsevier, vol. 19(1), pages 95-115, January.
    18. Agata Lozinskaia & Andreas Merikas & Anna Merika & Henry Penikas, 2017. "Determinants of the probability of default: the case of the internationally listed shipping corporations," Maritime Policy & Management, Taylor & Francis Journals, vol. 44(7), pages 837-858, October.
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    More about this item

    Keywords

    ex ante probability of default; corporate credit; credit registry; probability of default model; credit quality groups; credit spreads;
    All these keywords.

    JEL classification:

    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • E51 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Money Supply; Credit; Money Multipliers
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy
    • E58 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Central Banks and Their Policies
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation

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