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Macro Stress Testing of Credit Risk Focused on the Tails

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  • Ricardo Schechtman
  • Wagner Piazza Gaglianone

Abstract

This paper investigates macro stress testing of system-wide credit risk with special focus on the tails of the credit risk distributions conditional on bad macroeconomic scenarios. These tails determine the ex-post solvency probabilities derived from the scenarios. This paper estimates the macro-credit risk link by the traditional Wilson (1997) model as well as by an alternative proposed quantile regression (QR) method (Koenker and Xiao, 2002), in which the relative importance of the macro variables can vary along the credit risk distribution, conceptually incorporating uncertainty in default correlations. Stress-testing exercises on the Brazilian household sector at the one-quarter horizon indicate that unemployment rate distress produces the most harmful effect, whereas distressed inflation and distressed interest rate show higher impacts at longer periods. Determining which of the two stress-testing approaches perceives the scenarios more severely depends on the type of comparison employed. The QR approach is revealed more conservative based on a suggested comparison of vertical distances between the tails of the conditional and unconditional credit risk cumulative distributions.

Suggested Citation

  • Ricardo Schechtman & Wagner Piazza Gaglianone, 2011. "Macro Stress Testing of Credit Risk Focused on the Tails," Working Papers Series 241, Central Bank of Brazil, Research Department.
  • Handle: RePEc:bcb:wpaper:241
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    Cited by:

    1. Miguel Ángel Morales Mosquera & Wilmar Cabrera & Laura Capera & Dairo Estrada, 2012. "Un Mapa de Riesgo de Crédito para el Sistema Financiero Colombiano," Temas de Estabilidad Financiera 068, Banco de la Republica de Colombia.
    2. Buncic, Daniel & Melecky, Martin, 2013. "Macroprudential stress testing of credit risk: A practical approach for policy makers," Journal of Financial Stability, Elsevier, vol. 9(3), pages 347-370.
    3. Tabak, Benjamin M. & Takami, Marcelo & Rocha, Jadson M.C. & Cajueiro, Daniel O. & Souza, Sergio R.S., 2014. "Directed clustering coefficient as a measure of systemic risk in complex banking networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 394(C), pages 211-216.
    4. Covas, Francisco B. & Rump, Ben & Zakrajšek, Egon, 2014. "Stress-testing US bank holding companies: A dynamic panel quantile regression approach," International Journal of Forecasting, Elsevier, vol. 30(3), pages 691-713.
    5. Moreno, Ramón, 2011. "La formulación de políticas desde una perspectiva macroprudencial en economías emergentes," Revista Estudios Económicos, Banco Central de Reserva del Perú, issue 22, pages 21-40.
    6. Siemsen, Thomas & Vilsmeier, Johannes, 2018. "On a quest for robustness: About model risk, randomness and discretion in credit risk stress tests," Discussion Papers 31/2018, Deutsche Bundesbank.
    7. Luiz Awazu Pereira da Silva & Adriana Soares Sales & Wagner Piazza Gaglianone, 2013. "Financial stability in Brazil," Chapters,in: Stability of the Financial System, chapter 4 Edward Elgar Publishing.
    8. repec:gam:jrisks:v:5:y:2017:i:3:p:38-:d:105140 is not listed on IDEAS
    9. Ekaterina Neretina & Cenkhan Sahin & Jakob de Haan, 2014. "Banking stress test effects on returns and risks," DNB Working Papers 419, Netherlands Central Bank, Research Department.
    10. Reserve Bank of India RBI, 2012. "Financial Stability Report Issue No. 5," Working Papers id:5123, eSocialSciences.
    11. Ornelas, José Renato Haas & Barbachan, José Santiago Fajardo & Farias, Aquiles Rocha de, 2012. "Estimating relative risk aversion, risk-neutral and real-world densities using brazilian real currency options," EBAPE Working Papers 1, FGV/EBAPE - Escola Brasileira de Administração Pública e de Empresas (Brazil).
    12. Ruja, Catalin, 2014. "Macro Stress-Testing Credit Risk in Romanian Banking System," MPRA Paper 58244, University Library of Munich, Germany.
    13. Mamatzakis, E & Koutsomanoli-Filippaki, Anastasia & Pasiouras, Fotios, 2012. "A quantile regression approach to bank efficiency measurement," MPRA Paper 51879, University Library of Munich, Germany.
    14. Wong, Alfred Y-T. & Fong, Tom Pak Wing, 2011. "Analysing interconnectivity among economies," Emerging Markets Review, Elsevier, vol. 12(4), pages 432-442.

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    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation

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