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The Use of Dynamic Financial Analysis to Determine Whether an Optimal Growth Rate Exists for a Property‐Liability Insurer

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  • Stephen P. D'Arcy
  • Richard W. Gorvett

Abstract

Prior research on the aging phenomenon has demonstrated that new business for property‐liability (P‐L) insurers generates high loss ratios that gradually decline as a book of business goes through successive renewal cycles. Although the experience on new business is initially unprofitable, the renewal book of business eventually becomes profitable over time. Within this context, insurers need to manage their exposure growth in order to maximize long run profitability. Dynamic financial analysis (DFA), a relatively new tool for P‐L insurers, utilizes Monte Carlo simulation to generate the overall financial results for an insurer under a large number of scenarios. This article uses a publicly available DFA model—along with the estimated market value of an insurer, based on 1990–2001 data for stock P‐L insurers and underlying financial variables—to determine optimal growth rates of a P‐L insurer based on mean–variance analysis, stochastic dominance, and constraints on leverage.

Suggested Citation

  • Stephen P. D'Arcy & Richard W. Gorvett, 2004. "The Use of Dynamic Financial Analysis to Determine Whether an Optimal Growth Rate Exists for a Property‐Liability Insurer," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 71(4), pages 583-615, December.
    • Handle: RePEc:bla:jrinsu:v:71:y:2004:i:4:p:583-615
    DOI: 10.1111/j.0022-4367.2004.00105.x
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    Cited by:

    1. Pradhan, Rudra P. & Arvin, Mak B. & Norman, Neville R., 2015. "Insurance development and the finance-growth nexus: Evidence from 34 OECD countries," Journal of Multinational Financial Management, Elsevier, vol. 31(C), pages 1-22.
    2. Elisabeth Paté‐Cornell & Léa A. Deleris, 2009. "Failure Risks in the Insurance Industry: A Quantitative Systems Analysis," Risk Management and Insurance Review, American Risk and Insurance Association, vol. 12(2), pages 199-212, September.
    3. Pradhan, Rudra P. & Arvin, B. Mak & Norman, Neville R. & Nair, Mahendhiran & Hall, John H., 2016. "Insurance penetration and economic growth nexus: Cross-country evidence from ASEAN," Research in International Business and Finance, Elsevier, vol. 36(C), pages 447-458.
    4. Yu, Tzu-Yi & Tsai, Chenghsien & Huang, Hsiao-Tzu, 2010. "Applying simulation optimization to the asset allocation of a property-casualty insurer," European Journal of Operational Research, Elsevier, vol. 207(1), pages 499-507, November.
    5. Dorothea Diers & Martin Eling & Christian Kraus & Andreas Reuß, 2012. "Market-consistent embedded value in non-life insurance: how to measure it and why," Journal of Risk Finance, Emerald Group Publishing, vol. 13(4), pages 320-346, August.
    6. Martin Eling & Thomas Parnitzke, 2007. "Dynamic Financial Analysis: Classification, Conception, and Implementation," Risk Management and Insurance Review, American Risk and Insurance Association, vol. 10(1), pages 33-50, March.
    7. Byeongyong Paul Choi, 2010. "The U.S. Property and Liability Insurance Industry: Firm Growth, Size, and Age," Risk Management and Insurance Review, American Risk and Insurance Association, vol. 13(2), pages 207-224, September.
    8. Milton Nektarios, 2010. "A Growth Theory for the Insurance Industry," Risk Management and Insurance Review, American Risk and Insurance Association, vol. 13(1), pages 45-60, March.
    9. Martin Eling & Ruo Jia & Philipp Schaper, 2022. "The magic triangle: growth, profitability and safety in the insurance industry," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 47(2), pages 321-348, April.

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