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Can Regulation Affect the Solvency of Insurers? New Evidence from European Insurers

Author

Listed:
  • Evaggelia Siopi

    (Democritus University of Thrace)

  • Thomas Poufinas

    (Democritus University of Thrace)

  • James Ming Chen

    (Michigan State University)

  • Charalampos Agiropoulos

    (University of Piraeus)

Abstract

Successive crises in the early twenty-first century prompted regulators around the world to ask financial institutions to implement a series of regulations. These measures aimed to increase transparency, improve consumer and investor protection, restructure financial capital, stabilize insurance and pension markets, and improve solvency. The Solvency II framework introduced in the European Union applied these principles to insurance companies. This study attempts to predict the solvency of an insurer within a set of European insurers. The dataset consists of 29 insurance groups that operate across the European Union with a country of origin within the European Union for the period 2016 to 2020. The variables were constructed from annual financial statements retrieved from (Thomson Reuters) DataStream. The solvency capital requirement ratios were obtained manually from the solvency financial condition reports of each group. Regularized linear regression applying a ℓ1/ least-absolute-shrinkage-and-selection-operator penalty showed that the reinvestment rate, cash and equivalents, long term investment, and losses-benefits-and-adjustments expenses have the greatest predictive impact on the solvency of insurers. The contribution of this paper lies in the identification of determinants that allow insurance companies to maintain strong solvency capital requirement ratios so that they can maintain internal operations with minimal interruption.

Suggested Citation

  • Evaggelia Siopi & Thomas Poufinas & James Ming Chen & Charalampos Agiropoulos, 2023. "Can Regulation Affect the Solvency of Insurers? New Evidence from European Insurers," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 29(1), pages 15-30, May.
    • Handle: RePEc:kap:iaecre:v:29:y:2023:i:1:d:10.1007_s11294-023-09867-w
    DOI: 10.1007/s11294-023-09867-w
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    References listed on IDEAS

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    1. James Ming Chen, 2021. "An Introduction to Machine Learning for Panel Data," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 27(1), pages 1-16, February.
    2. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," Review of Finance, European Finance Association, vol. 33(5), pages 2223-2273.
    3. repec:eme:jrfpps:v:14:y:2013:i:2:p:303-314 is not listed on IDEAS
    4. Danielsson, Jon & James, Kevin R. & Valenzuela, Marcela & Zer, Ilknur, 2016. "Model risk of risk models," Journal of Financial Stability, Elsevier, vol. 23(C), pages 79-91.
    5. Martin Leo & Suneel Sharma & K. Maddulety, 2019. "Machine Learning in Banking Risk Management: A Literature Review," Risks, MDPI, vol. 7(1), pages 1-22, March.
    6. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2223-2273.
    7. Mathias Bärtl & Simone Krummaker, 2020. "Prediction of Claims in Export Credit Finance: A Comparison of Four Machine Learning Techniques," Risks, MDPI, vol. 8(1), pages 1-27, March.
    8. Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
    9. T. Joji Rao & Krishan K. Pandey, 2013. "A study on factors influencing claims in general insurance business in India," Journal of Risk Finance, Emerald Group Publishing Limited, vol. 14(3), pages 303-314, May.
    10. T. Joji Rao & Krishan K. Pandey, 2013. "A study on factors influencing claims in general insurance business in India," Journal of Risk Finance, Emerald Group Publishing, vol. 14(3), pages 303-314, May.
    11. Loisel, Stéphane & Piette, Pierrick & Tsai, Cheng-Hsien Jason, 2021. "Applying Economic Measures To Lapse Risk Management With Machine Learning Approaches," ASTIN Bulletin, Cambridge University Press, vol. 51(3), pages 839-871, September.
    12. Kexing Ding & Baruch Lev & Xuan Peng & Ting Sun & Miklos A. Vasarhelyi, 2020. "Machine learning improves accounting estimates: evidence from insurance payments," Review of Accounting Studies, Springer, vol. 25(3), pages 1098-1134, September.
    13. Filippo Curti & Ibrahim Ergen & Minh Le & Marco Migueis & Rob T. Stewart, 2016. "Benchmarking Operational Risk Models," Finance and Economics Discussion Series 2016-070, Board of Governors of the Federal Reserve System (U.S.).
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