Predicting Insurance Insolvency Using Generalized Qualitative Response Models

  • James B. McDonald
Part of the Huebner International Series on Risk, Insurance and Economic Security book series (HSRI, volume 16)


The general problem of corporate or business failure is very important and can generate significant losses to creditors and stockholders. Models of predicting business failure have been studied by numerous authors including Aharony (1980), Altman (1968), Beaver (1967), Dambolena and Khoury (1980), Deakin (1972), and Zavgren (1983). The problem of predicting insolvency of insurance companies has become an important issue for the National Association of Insurance Commissioners as well as state and federal legislators. More than 130 property and casualty insurance companies have failed during the last ten years. Daenzer (1984) indicates that the total statutory underwriting loss for the period 1979 to 1983 is $33.7 billion. In an interesting article entitled “Is ‘A-Plus’ Really a Passing Grade?” Denenberg (1967) examined Best’s ratings for size and financial strength for the six years preceding insolvency and found that the ratings were useful in predicting solvency status. These ratings attempt to summarize many factors, such as quality of underwriting, management, adequacy of reserves, investment quality and a financial rating. This has provided a useful, but not a perfect, guideline of an insurance companies financial health.


Logit Model Probit Model Financial Distress Financial Ratio Account Research 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. Aharony, J., C.P. Jones and I. Swary. (1980). “An Analysis of Risk and Return Characteristics of Corporate Bankruptcy Using Capital Market Data.” Journal of Finance 35:1001–1016.CrossRefGoogle Scholar
  2. Altman, E.I. (1968). “Financial Ratios, Discriminant Analysis, and the Prediction of Corporate Bankruptcy.” Journal of Finance 23:589–609.CrossRefGoogle Scholar
  3. Ambrose, J.M. and J.A. Seward. (1988). “Best’s Ratings, Financial Ratios and Prior Probabilities in Insolvency Prediction.” The Journal of Risk and Insurance 55:229–244.CrossRefGoogle Scholar
  4. Amemiya, T. (1981). “Qualitative Response Models: A Survey.” Journal of Economic Literature 19:1483–1536.Google Scholar
  5. BarNiv, Ran and Robert A. Hershbarger. (1987). “Classifying Financial Distress in the Life Insurance Industry.” Mimeographed Manuscript. Mississippi State University (September).Google Scholar
  6. BarNiv, R. and M.L. Smith. (1987). “Underwriting, Investment and Solvency.” Journal of Insurance Regulation 409–428.Google Scholar
  7. Beaver, W.H. (1967). “Financial Ratios as Predictors of Failure,” Empirical Research in Accounting: Selected Studies. Journal of Accounting Research, Supplement to Vol. 5:71–102.Google Scholar
  8. Berkson, J. (1949). “Application of the Logistic Function to Bio-assay”, Journal of the American Statistical Association 39:357–365.CrossRefGoogle Scholar
  9. Best’s Insurance Reports—Property and Casualty Edition. Oldwick, NJ: A.M. Best (various issues, annual).Google Scholar
  10. Bliss, C.I. (1935). “The Calculation of the Dosage-Mortality Curve.” Annals of Applied Biology 22:135–67.Google Scholar
  11. Bookstaber, R. and J.B. McDonald. (1987). “A General Distribution for Describing Security Price Returns.” The Journal of Business 60:401–424.CrossRefGoogle Scholar
  12. Bloom, Thomas S. (1988). “Passing the Test: Regulators Need New Way to Measure Strength of Insurers.” Business Insurance March:27–28.Google Scholar
  13. Booth, P.J. (1983). “Decomposition Measures and the Prediction of Financial Failure.” Journal of Business Finance and Accounting 10(1):67–82.CrossRefGoogle Scholar
  14. Cooley, W.W. and P.R. Lohnes. (1962). Multivariate Procedures for the Behavioral Sciences. New York: Wiley.Google Scholar
  15. Daenzer, B.J. (1984). “Insurers Solvency: Determining Which Companies Can Survive the Storm.” Risk Management July:22–30.Google Scholar
  16. Dambolena, I.G. and S.J. Khoury. (1980). “Ratio Stability and Corporate Failure,” Journal of Finance 35:1017–1026.CrossRefGoogle Scholar
  17. Deakin, E. (1972) “A Discriminant Analysis of Predictors of Business Failure.” Journal of Accounting Research 10(1):167–179.CrossRefGoogle Scholar
  18. Denenberg, H. (1967). “Is ‘A-Plus’ Really a Passing Grade?” Journal of Risk and Insurance 34(3):371–384.CrossRefGoogle Scholar
  19. Finney, P.J. (1952). Probit Analysis, Cambridge: Cambridge University Press.Google Scholar
  20. Goldberger, A. (1964). Econometric Theory, New York: Wiley.Google Scholar
  21. Harmelink, P.J. (1974). “Prediction of Best’s General Policyholder Ratings.” Journal of Risk and Insurance 41(4):621–632.CrossRefGoogle Scholar
  22. Harrington, S.E. and J.M. Nelson. (1986). “A Regression-Based Methodology for Solvency Surveillance in the Property-Liability Insurance Industry.” The Journal of Risk and Insurance 53(4):583–605.CrossRefGoogle Scholar
  23. Judge, G.G., W.E. Griffiths, R.C. Hill, H. Lutkepohl and T. Lee. (1985). The Theory and Practice of Econometrics. New York: Wiley.Google Scholar
  24. Kahane, Y. (1978). “Solidity, Leverage and Regulation of Insurance Companies.” The Geneva Papers on Risk and Insurance December:3–19.Google Scholar
  25. Kahane, Y., C.S. Tapiero and L. Jacque. (1986). “Concepts and Trends in the STudy of Insurer’s Solvency.” Paper presented at the International Conference on Insurance Solvency, Wharton School, (June).Google Scholar
  26. Manski, C.F. and S.R. Lerman. (1977). “The Estimation of Choice Based Samples.” Econometrica 45:1977–1988.CrossRefGoogle Scholar
  27. McDonald, J. (1984). “Some Generalized Functions for the Size Distribution of Income,” Econometrica 52:647–663.CrossRefGoogle Scholar
  28. McDonald, J. and D.G. Clarke. (1992). “Generalized Logit Models with an Application to Consumer Credit Behavior.” Presented at the International Symposium on Forecasting, Amsterdam (1988). Journal of Economic and Business 44:47–62.CrossRefGoogle Scholar
  29. McFadden, D. (1974). “Conditional Logit Analysis of Qualitative Choice Behavior.” In Frontiers in Econometrics P. Zaremka (ed.). New York: Academic Press.Google Scholar
  30. Ohlson, J.A. (1980). “Financial Ratios and the Probabilistic Prediction of Bankruptcy.” Journal of Accounting Research 18:109–131.CrossRefGoogle Scholar
  31. Pinches, G.E. and J.S. Trieschmann. (1977). “Discriminant Analysis, Classification Results and Financially Distressed P-L Insurers.” The Journal of Risk and Insurance 44:289–298.CrossRefGoogle Scholar
  32. Pinches, G.E. and J.S. Trieschmann. (1974). “The Efficiency of Alternative Models for Solvency Surveillance in the Insurance Industry.” Journal of Risk and Insurance 41(4):563–577.CrossRefGoogle Scholar
  33. Pinches, G.E. and J.S. Trieschmann. (1973). “A Multivariate Model for Predicting Financially Distressed Property-Liability Insurers.” Journal of Risk and Insurance 40(3):327–338.CrossRefGoogle Scholar
  34. Shaked, L. (1985). “Measuring Prospective Probabilities of Insolvency: An Application to the Life Insurance Industry.” The Journal of Risk and Insurance 52:59–80.CrossRefGoogle Scholar
  35. Thornton, J.H. and J.W. Meador. (1977). “Comments on the Validity of the NAIC Early Warning System for Predicting Failures Among P-L Insurance Companies.” CPCU Annuals September:191–211.Google Scholar
  36. Trieschmann, J.S. and G.E. Pinches. “A Multivariate Model for Predicting Financially Distressed P1-L Insurers.” The Journal of Risk and Insurance 40:327–338.Google Scholar
  37. Zavgren, C.V. (1983). “The Prediction of Corporate Failure: The State of the Art.” Journal of Accounting Literature 2:1–38.Google Scholar
  38. Zemijewski, M.E. (1984). “Methodological Issues Related to the Estimation of Financial Distress Prediction Models.” Journal of Accounting Research 22:59–82.CrossRefGoogle Scholar

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© Kluwer Academic Publishers 1993

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  • James B. McDonald

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