Review of Quantitative Finance and Accounting

, Volume 29, Issue 3, pp 241–266 | Cite as

Modeling exposure to losses on automobile leases

  • L. Douglas Smith
  • Baiqiang Jin
Original Paper


We present an integrated statistical model for assessing risk and projecting financial losses on automobile leases. The model employs nonstationary Markovian state transitions for active leases and hierarchical logistic and regression equations for different outcomes on termination. The model reveals that lower residual risks may partially offset higher credit risk for customers whose credit scores predict higher risk of default. It also reveals a risk profile that differs through time from other secured credits such as mortgages. A three-year follow-up of forecasts versus outcomes for 39,500 leasing contracts shows that the model predicted rates of repossession better than standard roll-rate models with stationary transition probabilities. It displayed similar accuracy in predicting unscheduled terminations and insurance settlements.


Credit risk Modeling automobile leases Risk management Nonstationary Markovian models 

JEL Classifications

C25 G2 G32 


  1. Altman, E. I. (1989). Measuring corporate bond mortality and performance. Journal of Finance, 44, 909–922.CrossRefGoogle Scholar
  2. Altman, E. I., Caouette, J. B., & Narayanan, P. (1998). Credit-risk measurement and management: the ironic challenge in the next decade. Financial Analysts Journal, 54(1), 7–11.CrossRefGoogle Scholar
  3. Asquith, P., Mullins, D. W. Jr., & Wolff, E. D. (1989). Original issue high yield bonds: aging analysis of defaults, exchanges, and calls. Journal of Finance, 44, 923–953.CrossRefGoogle Scholar
  4. Barth, J. R., & Yezer, A. M. J. (1983). Default risk on home mortgages: a further test of competing hypotheses. Journal of Risk and Insurance, 50(3), 500–505.CrossRefGoogle Scholar
  5. Betancourt, L. (1999). Using Markov chains to estimate losses from a portfolio of mortgages. Review of Quantitative Finance and Accounting, 12(3), 303–317.CrossRefGoogle Scholar
  6. Campbell, T. S., & Dietrich, J. K. (1983). The determinants of default on insured conventional residential mortgage loans. Journal of Finance, 38, 1569–1581.CrossRefGoogle Scholar
  7. Cunningham, D. F., & Capone, C. A., Jr. (1990). The relative termination experience of adjustable to fixed rate mortgages. Journal of Finance, 45, 1687–1703.CrossRefGoogle Scholar
  8. Curnow, G., Kochman, G., Meester, S., Sarkar, D., & Wilton, K. (1997). Automating credit and collections decisions at at&t capital corporation. Interfaces, 27(1), 29–52.Google Scholar
  9. Cyert, R. M., Davidson, H. J., & Thompson, G. L. (1962). Estimation of the allowance for doubtful accounts by Markov chains. Management Science, 8, 287–303.Google Scholar
  10. Docking, D. S., Hirschey, M., & Jones, E. (2000). Reaction of bank stock prices to loan-loss reserve announcements. Review of Quantitative Finance and Accounting, 15(3), 277–297.CrossRefGoogle Scholar
  11. Edmister, R. O., & Srivastava, S. C. (1993). Loan portfolio composition and management control of bank risk: An empirical investigation. Journal of Applied Business Research, 9(1), 119–126.Google Scholar
  12. FASB (1989). Accounting standards: Original pronouncements. July 1973–June 1, 1989, Norwalk, Connecticut: Financial Accounting Standards Board, (FASB), 1989.Google Scholar
  13. Gross, D. B., & Souleles, N. S. (2002). An empirical analysis of personal bankruptcy and delinquency. The Review of Financial Studies, 15(1), 319–347.CrossRefGoogle Scholar
  14. Kanagaretnam, K., Lobo, G. J., & Mathieu, R. (2003). Managerial incentives for income smoothing through loan loss provisions. Review of Quantitative Finance and Accounting, 20(1), 63–80.CrossRefGoogle Scholar
  15. Kang, P., & Zenios, S. (1992). Complete prepayment models for mortgage-backed securities. Management Science, 38, 1665–1685.CrossRefGoogle Scholar
  16. Lawrence, E. C., Smith, L. D., & Rhoades, M. (1992). An analysis of default risk in mobile home credit. Journal of Banking and Finance, 16, 299–312.CrossRefGoogle Scholar
  17. Lobo, G. J., & Yang, D. (2001). Bank managers heterogeneous decisions on discretionary loan loss provisions. Review of Quantitative Finance and Accounting, 16(3), 223–250.CrossRefGoogle Scholar
  18. Nissim, D. (2003). Reliability of banks’ fair value disclosure for loans. Review of Quantitative Finance and Accounting, 20(4), 355–384.CrossRefGoogle Scholar
  19. Rosenberg, E., & Gleit, A. (1994). Quantitative methods in credit management: A survey. Operations Research, 42(4), 589–613.Google Scholar
  20. Smith, L. D., & Lawrence, E. C. (1995). Forecasting losses on liquidating long-term loan portfolio. Journal of Banking and Finance, 19, 959–985.CrossRefGoogle Scholar
  21. Smith, L. D., Sanchez, S. M., & Lawrence, E. C. (1996). A comprehensive model for managing risk on home mortgage portfolios. Decision Sciences, 27(2), 291–317.Google Scholar
  22. Smith, L. D., Bilir, C., Huang, V. W., Hung, K. Y., & Kaplan, M. (2005). Citibank models credit risk on hybrid mortgages in Taiwan. Interfaces, 35(3), 215–229.CrossRefGoogle Scholar
  23. Von Furstenberg, G. M. (1970). The investment quality of home mortgages. Journal of Risk and Insurance, 37(3), 437–453.CrossRefGoogle Scholar
  24. Walter, J. (1991). Loan-Loss Reserves. Federal Reserve Bank of Richmond Economic Review July/August, 20–30.Google Scholar
  25. Zanakis, S. H., Mavrides, L. P., & Roussakis, E. N. (1986). Applications of management science in banking. Decision Sciences, 17, 114–128.CrossRefGoogle Scholar
  26. Zipkin, P. (1993). Mortgages and Markov chains: A simplified valuation model. Management Science, 39, 683–691.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  1. 1.Center for Business and Industrial Studies, College of Business AdministrationUniversity of Missouri-St. LouisSt. LouisUSA
  2. 2.Swiss Reinsurance CompanyBarringtonUSA

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