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Solvency Prediction for Property-Liability Insurance Companies: Evidence from the Financial Crisis

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Abstract

The financial crisis of 2008 generated sizeable losses in the financial sector around the world. Because regulators are used for predicting insurers’ financial strength in order to detect financially distressed firms as early as possible, we question how reliably regulators can forecast financial strength, especially during a financial crisis. We use the company-level data of German property-liability insurers from 2004 to 2011 to examine factors that affect the insurer’s regulatory solvency ratio. Furthermore, we develop a prediction model to classify the insurers regarding their financial strength. We show that, in particular, the lagged solvency ratio can be used to predict the future regulatory solvency ratio irrespective of the economic conditions. Thus, our results imply that German regulators are able to detect insurers in financial distress early enough to take appropriate actions to protect policyholders’ interests. Our results do not support the adoption of tighter regulations or higher capital requirements.

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Notes

  1. Acharya et al. (2011).

  2. Harrington (2009).

  3. Munch and Smallwood (1982).

  4. Klein (1995).

  5. See for example Grace et al. (1998); Cummins et al. (1999); Carson and Hoyt (1995); Browne and Hoyt (1995); Cheng and Weiss (2012); Chen and Wong (2004); Sharpe and Stadnik (2007); Kleffner and Lee (2009); Berry-Stölzle et al. (2010).

  6. Browne and Hoyt (1995); Cheng and Weiss (2012).

  7. Altman (1968).

  8. Trieschmann and Pinches (1973).

  9. See for example Grace et al. (1993, 1998); Browne and Hoyt (1995); Cummins et al. (1999); Cheng and Weiss (2012).

  10. Kramer (1996).

  11. Chen and Wong (2004).

  12. Sharpe and Stadnik (2007).

  13. Berry-Stölzle et al. (2010).

  14. The insurance supervision is mainly codified in the Versicherungsaufsichtsgesetz and the Gesetz über die Bundesanstalt für Finanzdienstleistungsaufsicht. See Berry-Stölzle and Born (2012) for a more detailed discussion of the German insurance market and its regulatory environment.

  15. See Directive 2002/13/EC of the European Parliament and Council Directive 73/239/EEC. According to German law, insurers’ equity contains the sum of paid-in capital stock, additional paid-in capital, retained earnings, profit-sharing rights outstanding and subordinate debt minus expenditure for the start-up or the expansion of business operation, goodwill of the company, and deferred taxes, and minus the net loss for the year if applicable.

  16. Only large holding companies like Allianz Group are traded, but not the single affiliates.

  17. See para. 81b Section 1 VAG (insurance regulatory law). For example, the BaFin can require a solvency plan that includes measures to either increase the current solvency (e.g. by increasing the company’s equity) or to decrease the required solvency (e.g. by increasing reinsurance).

  18. BaFin (2011).

  19. However, the BaFin possesses internal information in addition to the solvency ratio in order to assess the insurers’ financial condition.

  20. We analysed companies whose solvency ratios fell below 100 per cent in a given year individually. The unreported results show that, in all cases except one, the solvency ratio exceeded 100 per cent in the following year again and did not fall below 100 per cent again during our examination period, providing evidence for the effectiveness of the German approach to capital requirements in initiating regulatory action before insurers become insolvent. In one case (Mondial Assistance), the ratio kept fluctuating for several years. However, the company was part of the Allianz group and is still in business.

  21. With respect to the multicollinearity of the variables used in our analysis, the unreported results indicate low variance inflation factors (1.8 on average), with a maximum of 3.25 between two variables in the year 2011. Hence, we assume that multicollinearity aspects can be ignored in our analysis.

  22. Following Berry-Stölzle et al. (2010), we suspect the standard errors to be heteroskedastic. The results of a Breusch-Pagan-Test indicate a substantial amount of heteroscedasticity in our model. Therefore, we use robust standard errors for our estimation.

  23. Hair et al. (2006); Berry-Stölzle et al. (2010).

  24. The use of lagged variables to detect factors that influence insurers’ solvency proved to be successful in several related studies, as for example Barrese (1990), who finds the two-year reserve development as strong predictor of insurer distress.

  25. Cummins and Nini (2002).

  26. Sommer (1996).

  27. Harrington and Nelson (1986).

  28. For example, U.S. General Accounting Office (1989); Kim et al. (1995); Lee and Urrutia (1996).

  29. For example, during the Asian Financial Crisis, see Chen and Wong (2004).

  30. Cummins et al. (1999).

  31. Cummins et al. (1995).

  32. BarNiv and McDonalds (1992).

  33. Lee and Urrutia (1996).

  34. Eck (1982).

  35. Sommer (1996); Berry-Stölzle et al. (2010).

  36. We follow Berry-Stölzle et al. (2010) and define the Herfindahl index as Σa i 2/(Σa i )2, where a i represents the gross premiums earned in business line i. The calculation uses premium data reported in the insurance companies’ annual reports for the following lines of business: Personal Accident, Liability, Auto Liability, Other Auto, Fire, Homeowners Personal Property, Residential and Commercial Building Damage, Transportation and Aircraft, Legal Expenses, Credit and Collateral and Others.

  37. Lamm-Tennant and Starks (1993).

  38. Public insurers are founded as non-profit organisations under public law to serve a certain region or administrative district; they can be owned by public institutions like cities, counties, states, other public insurers or municipal savings banks which are non-profit organisations under public law as well.

  39. Previous literature on solvency prediction includes a vast amount of additional factors that have been tested regarding their impact on insurer solvency. For example, Grace et al. (1998) and Cummins et al. (1999) include larger set of variables, including regulatory RBC and FAST ratios. However, given the data availability provided by the German insurance regulation, we cannot include these kinds of factor in our analysis. Furthermore, we used different ratios for our control variables in additional regression analyses. The unreported results show no significant differences to the results presented in this paper. In addition, several factors that might affect insurer solvency like falsified financial statements or mismanagement could not been included in our model due to a lack of data.

  40. Thus, our methodology is similar to the U.S. RBC Authorized Control Levels that represent different levels of insurer capital holdings (200, 150, 100, 70 per cent).

  41. Lower AIC and BIC values indicate a better fit of the model. See Akaike (1974) and Schwarz (1978).

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Acknowledgements

The authors thank H.R. Schradin, D. Farny, T.R. Berry-Stölzle and the participants and discussants of the Research Seminar at University of Cologne, of the Research Seminar at University of Georgia, of the Roundtable on Insurance Regulation and Governance at St. John’s University, of the Western Risk and Insurance Association Annual Meeting 2013, of the Jahrestagung des Deutscher Vereins für Versicherungswissenschaft e.V. 2013 and two anonymous referees for helpful comments and suggestions on the paper. Furthermore, Sabine Wende gratefully acknowledges support from Köln Alumni e.V.

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  1. Department of Risk Management and Insurance and Cologne Graduate School in Management, Economics and Social Sciences, University of Cologne, Albertus-Magnus-Platz, Cologne, 50923, Germany

    Jannes Rauch

  2. Faculty of Management, Economics and Social Sciences, University of Cologne, Albertus-Magnus-Platz, Cologne, 50923, Germany

    Sabine Wende

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  1. Jannes Rauch
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Rauch, J., Wende, S. Solvency Prediction for Property-Liability Insurance Companies: Evidence from the Financial Crisis. Geneva Pap Risk Insur Issues Pract 40, 47–65 (2015). https://doi.org/10.1057/gpp.2014.16

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