Computational Economics

, Volume 53, Issue 1, pp 433–455 | Cite as

Stress Testing for Retail Mortgages Based on Probability Analysis

  • Chang Liu
  • Raja Nassar


One big problem with stress testing used by banks, regulators, and international financial organization is that the test does not predict occurrence probabilities of certain pre-specified stress scenarios and their consequent loss to be expected, which is, however, the real purpose of stress testing in the first place. As a result, institutes lack information sufficient enough for preserving appropriate resources to hedge risks prompted by these scenarios. In this study we use real life retail mortgages from a Chinese commercial bank and propose a stress testing approach based on probability analysis of different scenarios. This method would provide not only the amount of expected loss, but also that of the loan distributed over the loan classification states: Standard, Special Mention, Substandard, Doubtful, Loss, and Paid-off. Consequently, the bank management would have useful information when making credit operation policy decisions. In addition, the models and algorithms, providing practical risk management tools for banks and regulators, could be implemented on other commercial credit products as well.


Stress testing Non-stationary Markov chains Copula Simulation Retail mortgages 



This study was funded by China Natural Science Fundation (Grant No: 71473204). The authors thank Prof. Dongtao Lin of Sichuan University for copyediting this manuscript.


  1. A survey of stress tests and current practice at major financial institutions. (2001). Bank for International Settlements.
  2. Bank for International Settlements. (2005). Stress testing at major financial institutions: survey results and practice. Bank for International Settlements.
  3. Berkowitz, J. (2000). A coherent framework for stress-testing. SSRN Electronic Journal. Scholar
  4. Blaschke, W., Jones, M. T., Majnoni, G., & Martinez Peria, M. S. (2001). Stress testing of financial systems: An overview of issues, methodologies, and FSAP experiences. Imf Working Papers, 01(1/88), 1–56.Google Scholar
  5. Breuer, T. (2007). Overcoming dimensional dependence of worst case scenarios and maximum loss. Journal of Risk, 11(1), 79–92.CrossRefGoogle Scholar
  6. Breuer, T., Jandacka, M., Rheinberger, K., & Summer, M. (2008). Macroeconomic stress and worst case analysis of loan portfolios (SSRN Scholarly Paper No. ID 1149952). Rochester, NY: Social Science Research Network.
  7. Breuer, T., Jandacka, M., Rheinberger, K., Summer, M., & others. (2009). How to find plausible, severe, and useful stress scenarios. Österr. Nationalbank.Google Scholar
  8. Breuer, T., & Pistovcak, F. (2004). Identifying worst case scenarios of security portfolios with quasi-random search algorithms. A Research Project carried out from March 2001 to November 2003.Google Scholar
  9. Demarta, S., & Mcneil, A. J. (2010). The t copula and related copulas. International Statistical Review, 73(1), 111–129. Scholar
  10. Genest, C., & Mackay, R. J. (1986). Copules archimédiennes et families de lois bidimensionnelles dont les marges sont données. Canadian Journal of Statistics, 14(2), 145–159. Scholar
  11. Hamilton, J. D. (1989). A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica, 57(2), 357–384.CrossRefGoogle Scholar
  12. Hoggarth, G., Logan, A., & Zicchino, L. (2005). Macro stress tests of UK banks. In BIS Papers chapters (Vol. 22, pp. 392–408). Bank for International Settlements.
  13. Huang, X., Zhou, H., & Zhu, H. (2009). a framework for assessing the systemic risk of major financial institutions (SSRN Scholarly Paper No. ID 1335023). Rochester, NY: Social Science Research Network.
  14. Principles for sound stress testing practices and supervision. (2009). Bank for International Settlements.
  15. Ruban, O. A., Melas, D., & Inc., M. (2010). Stress testing in the investment process (August 2010) (SSRN Scholarly Paper No. ID 1708243). Rochester, NY: Social Science Research Network.
  16. Schuermann, T. (2016). Stress testing in wartime and in peacetime (SSRN Scholarly Paper No. ID 2735895). Rochester, NY: Social Science Research Network.
  17. Smith, L. D., Sanchez, S. M., & Lawrence, E. C. (1996). A comprehensive model for managing credit risk on home mortgage portfolios. Decision Sciences, 27(2), 291–317. Scholar
  18. Sorge, M. (2004). Stress-testing financial systems: An overview of current methodologies. Social Science Electronic Publishing, 68(3), 18–21. Scholar
  19. Varotto, S. (2011). Stress testing credit risk: The great depression scenario (SSRN Scholarly Paper No. ID 1570137). Rochester, NY: Social Science Research Network. Accessed 24 April 2018.

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Southwestern University of Finance and EconomicsChengduChina
  2. 2.Louisiana Tech UniversityRustonUSA

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