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Early warning system for the European Insurance Sector

Author

Listed:
  • Lorenzo Danieli
  • Petr Jakubik

    (EIOPA)

Abstract

This article proposes an Early Warning System model composed of macro-financial and company-specific indicators that could help to anticipate a potential market distress in the European insurance sector. A distress is defined as periods in which insurance companies’ equity prices crash and CDS spreads spike simultaneously. The model is estimated using a sample of 43 insurance companies that are listed. Based on a panel binomial logit specification, empirical evidence shows that economic overheating that could be manifested by high economic growth and inflation as well as high interest rates have negative impact on insurance sector stability. At the company level, increasing operating expenses increase the likelihood of distress occurrence.

Suggested Citation

  • Lorenzo Danieli & Petr Jakubik, 2018. "Early warning system for the European Insurance Sector," EIOPA Financial Stability Report - Thematic Articles 13, EIOPA, Risks and Financial Stability Department.
  • Handle: RePEc:eio:thafsr:13
    as

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    File URL: https://www.eiopa.europa.eu/sites/default/files/financial_stability/eiopa_fsr_december_2018_thematic_article.pdf
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    References listed on IDEAS

    as
    1. Carmen M. Reinhart & Graciela L. Kaminsky, 1999. "The Twin Crises: The Causes of Banking and Balance-of-Payments Problems," American Economic Review, American Economic Association, vol. 89(3), pages 473-500, June.
    2. Carmen M. Reinhart & Graciela L. Kaminsky, 1999. "The Twin Crises: The Causes of Banking and Balance-of-Payments Problems," American Economic Review, American Economic Association, vol. 89(3), pages 473-500, June.
    3. Billio, Monica & Getmansky, Mila & Lo, Andrew W. & Pelizzon, Loriana, 2012. "Econometric measures of connectedness and systemic risk in the finance and insurance sectors," Journal of Financial Economics, Elsevier, vol. 104(3), pages 535-559.
    4. Alessi, Lucia & Detken, Carsten, 2011. "Quasi real time early warning indicators for costly asset price boom/bust cycles: A role for global liquidity," European Journal of Political Economy, Elsevier, vol. 27(3), pages 520-533, September.
    5. Fulvio Corsi, 2009. "A Simple Approximate Long-Memory Model of Realized Volatility," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 7(2), pages 174-196, Spring.
    6. Hua Chen & J. David Cummins & Krupa S. Viswanathan & Mary A. Weiss, 2014. "Systemic Risk and the Interconnectedness Between Banks and Insurers: An Econometric Analysis," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 81(3), pages 623-652, September.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    early warning system; insurance sector; financial distress;
    All these keywords.

    JEL classification:

    • G01 - Financial Economics - - General - - - Financial Crises
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G22 - Financial Economics - - Financial Institutions and Services - - - Insurance; Insurance Companies; Actuarial Studies
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy

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