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Analysing the Determinants of Credit Risk for General Insurance Firms in the UK

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  • Guglielmo Maria Caporale
  • Mario Cerrato
  • Xuan Zhang

Abstract

This paper estimates a reduced-form model to assess the credit risk of General Insurance (GI) non-life firms in the UK. Compared to earlier studies, it uses a much larger sample including 30 years of data for 515 firms, and also considers a much wider set of possible determinants of credit risk. The empirical results suggest that macroeconomic and firm-specific factors both play important roles. Other key findings are the following: credit risk varies across firms depending on their business lines; there is default clustering in the GIindustry; different reinsurance levels also affect the credit risk of insurance firms. The implications of these findings for regulators of GI firms under the coming Solvency II are discussed.

Suggested Citation

  • Guglielmo Maria Caporale & Mario Cerrato & Xuan Zhang, 2016. "Analysing the Determinants of Credit Risk for General Insurance Firms in the UK," Discussion Papers of DIW Berlin 1591, DIW Berlin, German Institute for Economic Research.
    • Handle: RePEc:diw:diwwpp:dp1591
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    Cited by:

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    2. David Mhlanga, 2021. "Financial Inclusion in Emerging Economies: The Application of Machine Learning and Artificial Intelligence in Credit Risk Assessment," IJFS, MDPI, vol. 9(3), pages 1-16, July.

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    More about this item

    Keywords

    Insolvent; Doubly Stochastic; Insurance; Reinsurance;
    All these keywords.

    JEL classification:

    • G22 - Financial Economics - - Financial Institutions and Services - - - Insurance; Insurance Companies; Actuarial Studies
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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