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Macro stress testing of credit risk focused on the tails

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  • Schechtman, Ricardo
  • Gaglianone, Wagner Piazza

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 adverse 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 (1997a,b) 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.

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Bibliographic Info

Article provided by Elsevier in its journal Journal of Financial Stability.

Volume (Year): 8 (2012)
Issue (Month): 3 ()
Pages: 174-192

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Handle: RePEc:eee:finsta:v:8:y:2012:i:3:p:174-192

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Web page: http://www.elsevier.com/locate/jfstabil

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Keywords: Macro stress test; Credit risk; Financial system; Quantile regression;

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Cited by:
  1. Miguel Ángel Morales Mosquera & Wilmar Cabrera & Laura Capera & Dairo Estrada, . "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, 2012. "Macroprudential stress testing of credit risk : a practical approach for policy makers," Policy Research Working Paper Series 5936, The World Bank.
  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. 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.
  5. Ornelas, Jose Renato Haas & Barbachan, José 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, School of Public and Business Administration, Getulio Vargas Foundation (Brazil).
  6. Francisco B. Covas & Ben Rump & Egon Zakrajsek, 2013. "Stress-testing U.S. bank holding companies: a dynamic panel quantile regression approach," Finance and Economics Discussion Series 2013-55, Board of Governors of the Federal Reserve System (U.S.).
  7. 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.
  8. Luiz A. Pereira da Silva & Adriana Soares Sales & Wagner Piazza Gaglianone, 2012. "Financial Stability in Brazil," Working Papers Series 289, Central Bank of Brazil, Research Department.
  9. Mamatzakis, E & Koutsomanoli-Filippaki, Anastasia & Pasiouras, Fotios, 2012. "A quantile regression approach to bank efficiency measurement," MPRA Paper 51879, University Library of Munich, Germany.

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