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Identifying Threshold Effects in Credit Risk Stress Testing

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  • Armando Méndez Morales
  • Jose Giancarlo Gasha
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    Abstract

    Using data from Argentina, Australia, Colombia, El Salvador, Peru, and the United States, we identify three types of threshold effects when assessing the impact of economic activity on nonperforming loans (NPLs). For advanced financial systems showing low NPLs, there is an embedded self-correcting adjustment when NPLs exceed a minimum threshold. For financial systems in emerging markets in Latin America showing higher NPLs, there is instead a magnifying effect once NPLs cross a (higher) threshold. GDP growth apparently affects NPLs only below a certain threshold, which is consistent with observed lower elasticity of credit risk to changes in economic activity in boom periods.

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    Bibliographic Info

    Paper provided by International Monetary Fund in its series IMF Working Papers with number 04/150.

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    Length: 17
    Date of creation: 01 Aug 2004
    Date of revision:
    Handle: RePEc:imf:imfwpa:04/150

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    Related research

    Keywords: Credit risk; Stress testing; gdp growth; probability; business cycle; normal distribution; growth rates; gdp growth rates; functional form; growth rate; real gdp; markov process; probability distribution; statistics; econometrics; gdp growth rate; time series; standard errors; equation; heteroscedasticity; random walk; standard deviation; standard error; probability function; probabilities; quality control; cointegration; predictions; standard deviations; statistic; goodness of fit; stochastic process; gnp;

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    1. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-84, March.
    2. Potter, Simon M, 1995. "A Nonlinear Approach to US GNP," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 10(2), pages 109-25, April-Jun.
    3. Mehmet Caner & Bruce E. Hansen, 2001. "Threshold Autoregression with a Unit Root," Econometrica, Econometric Society, vol. 69(6), pages 1555-1596, November.
    4. Maria Soledad Martinez Peria & Giovanni Majnoni & Matthew T. Jones & Winfrid Blaschke, 2001. "Stress Testing of Financial Systems," IMF Working Papers 01/88, International Monetary Fund.
    5. Hansen, Bruce E. & Seo, Byeongseon, 2002. "Testing for two-regime threshold cointegration in vector error-correction models," Journal of Econometrics, Elsevier, vol. 110(2), pages 293-318, October.
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    Cited by:
    1. Marcucci, Juri & Quagliariello, Mario, 2009. "Asymmetric effects of the business cycle on bank credit risk," Journal of Banking & Finance, Elsevier, vol. 33(9), pages 1624-1635, September.
    2. Grigori Fainstein & Igor Novikov, 2011. "The role of macroeconomic determinants in credit risk measurement in transition country: Estonian example," International Journal of Transitions and Innovation Systems, Inderscience Enterprises Ltd, vol. 1(2), pages 117-137.
    3. Grigori Fainstein & Igor Novikov, 2011. "The Comparative Analysis of Credit Risk Determinants In the Banking Sector of the Baltic States," Review of Economics & Finance, Better Advances Press, Canada, vol. 1, pages 20-45, June.
    4. Tom Pak-wing Fong & Chun-shan Wong, 2008. "Stress Testing Banks' Credit Risk Using Mixture Vector Autoregressive Models," Working Papers 0813, Hong Kong Monetary Authority.

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