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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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    References listed on IDEAS

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    1. André K. Anundsen & Karsten Gerdrup & Frank Hansen & Kasper Kragh‐Sørensen, 2016. "Bubbles and Crises: The Role of House Prices and Credit," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(7), pages 1291-1311, November.
    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. Drehmann, Mathias & Juselius, Mikael, 2014. "Evaluating early warning indicators of banking crises: Satisfying policy requirements," International Journal of Forecasting, Elsevier, vol. 30(3), pages 759-780.
    4. 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.
    5. Bertrand Candelon & Elena-Ivona Dumitrescu & Christophe Hurlin, 2012. "How to Evaluate an Early-Warning System: Toward a Unified Statistical Framework for Assessing Financial Crises Forecasting Methods," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 60(1), pages 75-113, April.
    6. 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.
    7. Mr. Fabio Comelli, 2014. "Comparing the Performance of Logit and Probit Early Warning Systems for Currency Crises in Emerging Market Economies," IMF Working Papers 2014/065, International Monetary Fund.
    8. Fabio Comelli, 2014. "Comparing Parametric and Non-parametric Early Warning Systems for Currency Crises in Emerging Market Economies," Review of International Economics, Wiley Blackwell, vol. 22(4), pages 700-721, September.
    9. 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.
    10. Òscar Jordà & Moritz Schularick & Alan M Taylor, 2011. "Financial Crises, Credit Booms, and External Imbalances: 140 Years of Lessons," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 59(2), pages 340-378, June.
    11. Fulvio Corsi, 2009. "A Simple Approximate Long-Memory Model of Realized Volatility," Journal of Financial Econometrics, Oxford University Press, vol. 7(2), pages 174-196, Spring.
    12. Martin, Daniel, 1977. "Early warning of bank failure : A logit regression approach," Journal of Banking & Finance, Elsevier, vol. 1(3), pages 249-276, November.
    13. 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.
    14. Davis, E. Philip & Karim, Dilruba, 2008. "Comparing early warning systems for banking crises," Journal of Financial Stability, Elsevier, vol. 4(2), pages 89-120, June.
    15. Girardi, Giulio & Hanley, Kathleen W. & Nikolova, Stanislava & Pelizzon, Loriana & Sherman, Mila Getmansky, 2021. "Portfolio similarity and asset liquidation in the insurance industry," Journal of Financial Economics, Elsevier, vol. 142(1), pages 69-96.
    16. Claudio Borio & Mathias Drehmann, 2009. "Assessing the risk of banking crises - revisited," BIS Quarterly Review, Bank for International Settlements, March.
    17. Sevim, Cuneyt & Oztekin, Asil & Bali, Ozkan & Gumus, Serkan & Guresen, Erkam, 2014. "Developing an early warning system to predict currency crises," European Journal of Operational Research, Elsevier, vol. 237(3), pages 1095-1104.
    18. Cristina Dorofti & Petr Jakubik, 2015. "Insurance Sector Profitability and the Macroeconomic Environment," EIOPA Financial Stability Report - Thematic Articles 4, EIOPA, Risks and Financial Stability Department.
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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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