Advertisement

Hazard-Specific Supply Reactions in the Aftermath of Natural Disasters

  • Vijay Aseervatham
  • Patricia Born
  • Dominik LohmaierEmail author
  • Andreas Richter
Article

Abstract

Prior studies on the effects of catastrophes on insurance markets have either focused on one specific type of hazard or pooled several natural disasters. We argue that insurers evaluate disaster risk with respect to not only the frequency and severity of disasters but also the disaster type. We analyse U.S. property insurers’ supply decisions between 1992 and 2012 and find that insurers’ responses with respect to the reduction of business volume and exit decisions differ across hazards, even after controlling for damage size. The negative effects of catastrophes on supply decisions are more pronounced after extreme hurricane years compared with tornado years. We argue that supply distortions in the aftermath of unprecedented catastrophes are driven primarily by correlated losses besides the damage size of the event. Our results show that the predictability of catastrophe losses poses less-severe threats to insurers. Thus, we propose that insurers and regulators should focus primarily on measures that encourage diversification.

Keywords

catastrophic risks insurance supply property/casualty insurance 

JEL Classification

D8 G22 

Notes

Acknowledgements

The authors thank the participants at the Annual Meeting of the German Insurance Science Association, 2014; the Annual Meeting of the American Risk and Insurance Association, 2014; the CEAR/MRIC Behavioral Insurance Workshop, 2014; the 16th Joint Seminar of the European Association of Law and Economics (EALE) and The Geneva Association, 2015; and in particular, James Carson, Randy Dumm, Michael Hanselmann, Robert Hoyt, Johannes Jaspersen, Stefan Neuß and Martin Spindler for valuable comments. Any remaining errors are our own.

Supplementary material

41288_2016_4_MOESM1_ESM.docx (84 kb)
Supplementary material 1 (DOCX 84 kb)

References

  1. Aiuppa, T.A., Carney, R.J. and Krueger, T. M. (1993) ‘An examination of insurance stock prices following the 1989 Loma Prieta Earthquake’, Journal of Insurance Issues 16(1): 1–14.Google Scholar
  2. Angbazo, L.A. and Narayanan, R. (1996) ‘Catastrophic shocks in the property-liability insurance industry: Evidence on regulatory and contagion effects’, The Journal of Risk and Insurance 63(4): 619–637.CrossRefGoogle Scholar
  3. Arellano, M. and Bond, S. (1991) ‘Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations’, The Review of Economic Studies 58(2): 277–297.CrossRefGoogle Scholar
  4. Arellano, M. and Bover, O. (1995) ‘Another look at the instrumental variable estimation of error-components models’, Journal of Econometrics 68(1): 29–51.CrossRefGoogle Scholar
  5. Aseervatham, V., Born, P. and Richter, A. (2013) Demand reactions in the aftermath of catastrophes and the need for behavioral approaches, Munich Risk and Insurance Center Working Paper No. 13.Google Scholar
  6. Blundell, R. and Bond, S. (1998) ‘Initial conditions and moment restrictions in dynamic panel data models’, Journal of Econometrics 87(1): 115–143.CrossRefGoogle Scholar
  7. Born, P. and Klimaszewski-Blettner, B. (2009) Catastrophes and performance in property insurance: A comparison of personal and commercial lines, Independent Policy Report, Oakland, CA: Independent Institute.Google Scholar
  8. Born, P. and Klimaszewski-Blettner, B. (2013) ‘Should I stay or should I go? The impact of natural disasters and regulation on US property insurers’ supply decisions’, The Journal of Risk and Insurance 80(1): 1–36.CrossRefGoogle Scholar
  9. Born, P. and Viscusi, W.K. (2006) ‘The catastrophic effects of natural disasters on insurance markets’, Journal of Risk and Uncertainty 33(1–2): 55–72.CrossRefGoogle Scholar
  10. Browne, M.J. and Hoyt, R.E. (2000) ‘The demand for flood insurance: Empirical evidence’, The Journal of Risk and Insurance 20(3): 291–306.Google Scholar
  11. Cabantous, L. (2007) ‘Ambiguity aversion in the field of insurance: Insurers’ attitude to imprecise and conflicting probability estimates’, Theory and Decision 62(3): 219–240.CrossRefGoogle Scholar
  12. Cabantous, L., Hilton, D., Kunreuther, H. and Michel-Kerjan, E. (2011) ‘Is imprecise knowledge better than conflicting expertise? Evidence from insurers’ decisions in the United States’, Journal of Risk and Uncertainty 42(3): 211–232.CrossRefGoogle Scholar
  13. Cagle, J.B. and Harrington, S. (1995) ‘Insurance supply with capacity constraints and endogenous insolvency risk’, Journal of Risk and Uncertainty 11(3): 219–232.CrossRefGoogle Scholar
  14. Chen, H. and Sun, T. (2012) Model uncertainty, ambiguity aversion and implications for catastrophe insurance market, working paper, Temple University.Google Scholar
  15. Cole, C.R. and McCullough, K.A. (2006) ‘A reexamination of the corporate demand for reinsurance’, The Journal of Risk and Insurance 73(1): 169–192.CrossRefGoogle Scholar
  16. Cummins, D. and Lewis, C.M. (2003) ‘Catastrophic events, parameter uncertainty and the breakdown of implicit long-term contracting: The case of terrorism insurance’, Journal of Risk and Uncertainty 26(2–3): 153–178.CrossRefGoogle Scholar
  17. Cummins, D. and Sommer, D.W. (1996) ‘Capital and risk in property-liability insurance markets’, Journal of Banking & Finance 20(6): 1069–1092.CrossRefGoogle Scholar
  18. Deryugina, T. (2011) The dynamic effects of hurricanes in the US: The role of non-disaster transfer payments, working paper, Massachusetts Institute of Technology Center for Environmental Policy Research.Google Scholar
  19. Froot, K. A. and O’Connell, P. G. (1999) ‘The pricing of US catastrophe reinsurance’, in The Financing of Catastrophe Risk. Chicago: University of Chicago Press, pp. 195–232.Google Scholar
  20. Gallagher, J. (2014) ‘Learning about an infrequent event: Evidence from flood insurance take-up in the United States’, American Economic Journal: Applied Economics 6(3): 206–233.Google Scholar
  21. Grace, M. and Klein, R. W. (2006) After the storms: Property insurance markets in Florida, working paper, Georgia State University.Google Scholar
  22. Grace, M. and Klein, R.W. (2007) ‘Hurricane risk and property insurance markets’, working paper, Georgia State University.Google Scholar
  23. Grace, M. and Klein, R.W. (2009) ‘The perfect storm: Hurricanes, insurance, and regulation’, Risk Management and Insurance Review 12(1): 81–124.CrossRefGoogle Scholar
  24. Grace, M., Klein, R.W. and Kleindorfer, P.R. (2004) ‘Homeowners insurance with bundled catastrophe coverage’, The Journal of Risk and Insurance 71(3): 351–379.CrossRefGoogle Scholar
  25. Grace, M., Klein, R.W. and Liu, Z. (2005) ‘Increased hurricane risk and insurance market responses’, Journal of Insurance Regulation 24(2): 3–32.Google Scholar
  26. Gron, A. (1990) Property-Casualty Insurance Cycles, Capacity Constraints, and Empirical Results, Cambridge, MA: Massachusetts Institute of Technology.Google Scholar
  27. Gron, A. (1994) ‘Capacity constraints and cycles in property-casualty insurance markets’, The RAND Journal of Economics 25(1): 110–127.CrossRefGoogle Scholar
  28. Hagendorff, B., Hagendorff, J. and Keasey, K. (2015) ‘The impact of mega-catastrophes on insurers: An exposure-based analysis of the U.S. homeowners’ insurance market’, Risk Analysis 35(1): 157–173.CrossRefGoogle Scholar
  29. Hansen, L.P. (1982) ‘Large sample properties of generalized method of moments estimators’, Econometrica: Journal of the Econometric Society 50(4): 1029–1054.Google Scholar
  30. Harrington, S.E. and Niehaus, G. (2001) ‘Government insurance, tax policy, and the affordability and availability of catastrophe insurance’, Journal of Insurance Regulation 19(4): 591–612.Google Scholar
  31. Henson, B. (2013) ‘Long-range tornado prediction: Is it feasible?’, from http://www2.ucar.edu/atmosnews/opinion/9996/long-range-tornado-prediction-it-feasible, retrieved 16 March 2015.
  32. Ibragimov, R., Jaffee, D. and Walden, J. (2009) ‘Non-diversification traps in catastrophe insurance markets’, Review of Financial Studies 22(3): 959–993.CrossRefGoogle Scholar
  33. Insurance Information Institute (2016) ‘Homeowners and Renters Insurance’, from http://www.iii.org/fact-statistic/homeowners-and-renters-insurance, retrieved 10 April 2016.
  34. Kleffner, A. E. and Doherty, N.A. (1996) ‘Costly risk bearing and the supply of catastrophic insurance’, The Journal of Risk and Insurance 63(4): 657–671.CrossRefGoogle Scholar
  35. Klein, R. (2008) Catastrophe risk and the regulation of property insurance, working paper, Georgia State University.Google Scholar
  36. Klein, R. and Kleindorfer, P. (1999) The supply of catastrophe insurance under regulatory constraints, Wharton Center for Financial Institutions Working Paper No. 99-25.Google Scholar
  37. Klein, R.W. (2013) ‘Insurance market regulation: Catastrophe risk, competition, and systemic risk’, in G. Dionne (ed) Handbook of Insurance. New York: Springer, pp. 909–939.CrossRefGoogle Scholar
  38. Kunreuther, H., Meszaros, J., Hogarth, R.M. and Spranca, M. (1995) ‘Ambiguity and underwriter decision processes’, Journal of Economic Behavior & Organization 26(3): 337–352.CrossRefGoogle Scholar
  39. Kunreuther, H. and Michel-Kerjan, E.O. (2009) At War with the Weather: Managing Large-Scale Risks in a New Era of Catastrophes. Cambridge, MA: MIT Press.CrossRefGoogle Scholar
  40. Lamb, R.P. (1995) ‘An exposure-based analysis of property-liability insurer stock values around Hurricane Andrew’, The Journal of Risk and Insurance 62(1): 111–123.CrossRefGoogle Scholar
  41. Medders, L.A., Nyce, C.M. and Karl, J.B. (2014) ‘Market implications of public policy interventions: The case of Florida’s property insurance market’, Risk Management and Insurance Review 17(2): 183–214.CrossRefGoogle Scholar
  42. National Hurricane Center (2014) ‘Hurricane season dates’, from http://www.nhc.noaa.gov/, retrieved 16 March 2015
  43. National Severe Storms Laboratory (2014) Severe weather 101', from https://www.nssl.noaa.gov/education/svrwx101/tornadoes/faq/, retrieved 16 March 2015.
  44. Nickell, S. (1981) ‘Biases in dynamic models with fixed effects’, Econometrica: Journal of the Econometric Society 6: 1417–1426.Google Scholar
  45. Ragin, M.A. and Halek, M. (2015) ‘Market expectations following catastrophes: An examination of insurance broker returns’, The Journal of Risk and Insurance. doi: 10.1111/jori.12069.
  46. Raykov, R.S. (2015) ‘Catastrophe insurance equilibrium with correlated claims’, Theory and Decision 78(1): 89–115.CrossRefGoogle Scholar
  47. Shelor, R.M., Anderson, D.C. and Cross, M.L. (1992) ‘Gaining from loss: Property-liability insurer stock values in the aftermath of the 1989 California earthquake’, The Journal of Risk and Insurance 59(3): 476–488.CrossRefGoogle Scholar
  48. Skogh, G. (1999) ‘Risk-sharing institutions for unpredictable losses’, Journal of Institutional and Theoretical Economics 155(3): 505–515.Google Scholar
  49. Skogh, G. and Wu, H. (2005) ‘The diversification theorem restated: Risk-pooling without assignment of probabilities’, Journal of Risk and Uncertainty 31(1): 35–51.CrossRefGoogle Scholar
  50. Swiss Re. (2014) ‘Natural catastrophes and man-made disasters in 2013’, SIGMA 1/2014.Google Scholar
  51. Swiss Re. (2015) ‘Natural catastrophes and man-made disasters in 2014: Convective and winter storms generate most losses’, SIGMA 2/2015: 14–20.Google Scholar
  52. Takao, A., Yoshizawa, T., Hsu, S. and Yamasaki, T. (2013) ‘The effect of the Great East Japan earthquake on the stock prices of non-life insurance companies’, The Geneva Papers on Risk and Insurance—Issues and Practice 38(3): 449–468.CrossRefGoogle Scholar
  53. Thomann, C. and von der Schulenburg, J.-M. (2006) Supply and demand for terrorism insurance: Lessons from Germany, Hannover Economic Papers  dp-340, Universität Hannover.Google Scholar
  54. Windmeijer, F. (2005) ‘A finite sample correction for the variance of linear efficient two-step GMM estimators’, Journal of Econometrics 126(1): 25–51.CrossRefGoogle Scholar
  55. Winter, R.A. (1994) ‘The dynamics of competitive insurance markets’, Journal of Financial Intermediation 3(4): 379–415.CrossRefGoogle Scholar
  56. Yamasaki, T. (2015) ‘Do typhoons cause turbulence in property-liability insurers’ stock prices?’, The Geneva Papers on Risk and Insurance—Issues and Practice 41(3): 432–454.Google Scholar
  57. Zhang, L. and Nielson, N. (2015) ‘Solvency analysis and prediction in property-casualty insurance: Incorporating economic and market predictors’, The Journal of Risk and Insurance 82(1): 97–124.CrossRefGoogle Scholar

Copyright information

© The International Association for the Study of Insurance Economics 2016

Authors and Affiliations

  • Vijay Aseervatham
    • 1
  • Patricia Born
    • 2
  • Dominik Lohmaier
    • 1
    Email author
  • Andreas Richter
    • 1
  1. 1.Munich Risk and Insurance Center, Munich School of ManagementLudwig-Maximilians-Universität MunichMunichGermany
  2. 2.Department of Risk Management/Insurance, Real Estate and Legal Studies, College of BusinessFlorida State UniversityTallahasseeUSA

Personalised recommendations