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Probability of default in collateralized credit operations

Author

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  • Divino, Jose Angelo
  • Rocha, Líneke Clementino Sleegers

Abstract

The goal of this paper is to identify the major determinants of the probability of default in a mortgage credit operation, which is backed by collateral. We use an exclusive data set with 268,036 loan contracts and apply logistic regression and Cox proportional hazards model in the estimation. The discriminatory power of the estimated models is analyzed by several accuracy indicators. The inclusion of time-dependent macroeconomic variables in addition to covariates representing characteristics of the contract and individuals improved the overall performance. Logistic regression showed a higher discriminatory power than Cox proportional hazards model according to all accuracy indicators. It is worth mentioning the negative relationship between the probability of default and the economy base interest rate. Decreases in the base interest rate lead banks to lose revenue from treasury operations and expand credit operations to compensate the loss. This strategy brings individuals with a higher probability of default to the financial market.

Suggested Citation

  • Divino, Jose Angelo & Rocha, Líneke Clementino Sleegers, 2013. "Probability of default in collateralized credit operations," The North American Journal of Economics and Finance, Elsevier, vol. 25(C), pages 276-292.
  • Handle: RePEc:eee:ecofin:v:25:y:2013:i:c:p:276-292
    DOI: 10.1016/j.najef.2012.06.015
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    References listed on IDEAS

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    1. Jose Angelo Divino & Edna Souza Lima & Jaime Orrillo, 2013. "Interest rates and default in unsecured loan markets," Quantitative Finance, Taylor & Francis Journals, vol. 13(12), pages 1925-1934, December.
    2. Veronica Balzarotti & Michael Falkenheim & Andrew Powell, 2002. "On the Use of Portfolio Risk Models and Capital Requirements in Emerging Markets: The Case of Argentina," World Bank Economic Review, World Bank Group, vol. 16(2), pages 197-212, August.
    3. Gary Whalen, 1991. "A proportional hazards model of bank failure: an examination of its usefulness as an early warning tool," Economic Review, Federal Reserve Bank of Cleveland, issue Q I, pages 21-31.
    4. Pradeep Dubey & John Geanakoplos & Martin Shubik, 2005. "Default and Punishment in General Equilibrium," Econometrica, Econometric Society, vol. 73(1), pages 1-37, January.
    5. anonymous, 2008. "Monetary policy report to the Congress," Web Site 34, Board of Governors of the Federal Reserve System (U.S.).
    6. John Geanakoplos, 2009. "The Leverage Cycle," Cowles Foundation Discussion Papers 1715R, Cowles Foundation for Research in Economics, Yale University, revised Jan 2010.
    7. anonymous, 2008. "Monetary policy report to the Congress," Web Site 16, Board of Governors of the Federal Reserve System (U.S.).
    8. Dirk Tasche, 2006. "Validation of internal rating systems and PD estimates," Papers physics/0606071, arXiv.org.
    9. John Geanakoplos, 2010. "The Leverage Cycle," NBER Chapters,in: NBER Macroeconomics Annual 2009, Volume 24, pages 1-65 National Bureau of Economic Research, Inc.
    10. Jonathan Crook & Tony Bellotti, 2010. "Time varying and dynamic models for default risk in consumer loans," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 173(2), pages 283-305.
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    Cited by:

    1. Hammoudeh, Shawkat & McAleer, Michael, 2013. "Risk management and financial derivatives: An overview," The North American Journal of Economics and Finance, Elsevier, vol. 25(C), pages 109-115.
    2. Ortas, E. & Salvador, M. & Moneva, J.M., 2015. "Improved beta modeling and forecasting: An unobserved component approach with conditional heteroscedastic disturbances," The North American Journal of Economics and Finance, Elsevier, vol. 31(C), pages 27-51.

    More about this item

    Keywords

    Probability of default; Logistic regression; Survival analysis; Collateral; Accuracy indicators;

    JEL classification:

    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation

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