IDEAS home Printed from https://ideas.repec.org/a/sav/journl/v70y2022i1p3-21.html
   My bibliography  Save this article

Early Warning System for the European Insurance Sector

Author

Listed:
  • Lorenzo Danieli

    (European Securities and Markets Authority)

  • Petr Jakubik

    (European Insurance and Occupational Pensions Authority)

Abstract

This study 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 36 insurance companies that are listed. Based on a fixed-effects panel binomial logit specification, empirical evidence shows that economic overheating that could be manifested by high economic growth, inflation and interest rates have negative impact on insurance sector stability. At the company level, a drop in return on assets and price-to-book value or raising operating expenses increase the likelihood of distress occurrence.

Suggested Citation

  • Lorenzo Danieli & Petr Jakubik, 2022. "Early Warning System for the European Insurance Sector," Journal of Economics / Ekonomicky casopis, Institute of Economic Research, Slovak Academy of Sciences, vol. 70(1), pages 3-21, January.
  • Handle: RePEc:sav:journl:v:70:y:2022:i:1:p:3-21
    as

    Download full text from publisher

    File URL: https://www.sav.sk/journals/uploads/0310180501%2022%20Danieli-Jakubik%20+%20SR.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. 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.
    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. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. Martin, Daniel, 1977. "Early warning of bank failure : A logit regression approach," Journal of Banking & Finance, Elsevier, vol. 1(3), pages 249-276, November.
    16. Claudio Borio & Mathias Drehmann, 2009. "Assessing the risk of banking crises - revisited," BIS Quarterly Review, Bank for International Settlements, March.
    17. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Antunes, António & Bonfim, Diana & Monteiro, Nuno & Rodrigues, Paulo M.M., 2018. "Forecasting banking crises with dynamic panel probit models," International Journal of Forecasting, Elsevier, vol. 34(2), pages 249-275.
    2. Xianglong Liu, 2023. "Towards Better Banking Crisis Prediction: Could an Automatic Variable Selection Process Improve the Performance?," The Economic Record, The Economic Society of Australia, vol. 99(325), pages 288-312, June.
    3. Kalatie, Simo & Laakkonen, Helinä & Tölö, Eero, 2015. "Indicators used in setting the countercyclical capital buffer," Research Discussion Papers 8/2015, Bank of Finland.
    4. Kalatie, Simo & Laakkonen, Helinä & Tölö, Eero, 2015. "Indicators used in setting the countercyclical capital buffer," Bank of Finland Research Discussion Papers 8/2015, Bank of Finland.
    5. Helene Olsen & Harald Wieslander, 2020. "The Impact of Monetary Policy on Leading Variables for Financial Stability in Norway," Working Papers No 02/2020, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    6. repec:zbw:bofrdp:2015_008 is not listed on IDEAS
    7. Savas Papadopoulos & Pantelis Stavroulias & Thomas Sager & Etti Baranoff, 2017. "A ternary-state early warning system for the European Union," Working Papers 222, Bank of Greece.
    8. Tölö, Eero, 2020. "Predicting systemic financial crises with recurrent neural networks," Journal of Financial Stability, Elsevier, vol. 49(C).
    9. Alessi, Lucia & Detken, Carsten, 2018. "Identifying excessive credit growth and leverage," Journal of Financial Stability, Elsevier, vol. 35(C), pages 215-225.
    10. Lang, Jan Hannes & Izzo, Cosimo & Fahr, Stephan & Ruzicka, Josef, 2019. "Anticipating the bust: a new cyclical systemic risk indicator to assess the likelihood and severity of financial crises," Occasional Paper Series 219, European Central Bank.
    11. Lo Duca, Marco & Koban, Anne & Basten, Marisa & Bengtsson, Elias & Klaus, Benjamin & Kusmierczyk, Piotr & Lang, Jan Hannes & Detken, Carsten & Peltonen, Tuomas, 2017. "A new database for financial crises in European countries," ESRB Occasional Paper Series 13, European Systemic Risk Board.
    12. Mahir Binici & Aytül Ganioglu, 2021. "Net external position, financial development, and banking crisis," Empirical Economics, Springer, vol. 61(3), pages 1225-1251, September.
    13. Piotr Bańbuła & Marcin Pietrzak, 2021. "Early Warning Models of Banking Crises: VIX and High Profits," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 13(4), pages 381-403, December.
    14. Oet, Mikhail V. & Gramlich, Dieter & Sarlin, Peter, 2016. "Evaluating measures of adverse financial conditions," Journal of Financial Stability, Elsevier, vol. 27(C), pages 234-249.
    15. Tihana Škrinjarić, 2023. "Credit-to-GDP Gap Estimates in Real Time: A Stable Indicator for Macroprudential Policy Making in Croatia," Comparative Economic Studies, Palgrave Macmillan;Association for Comparative Economic Studies, vol. 65(3), pages 582-614, September.
    16. Kauko, Karlo, 2014. "How to foresee banking crises? A survey of the empirical literature," Economic Systems, Elsevier, vol. 38(3), pages 289-308.
    17. repec:zbw:bofrdp:2019_014 is not listed on IDEAS
    18. Tihana Skrinjaric, 2023. "Leading indicators of financial stress in Croatia: a regime switching approach," Public Sector Economics, Institute of Public Finance, vol. 47(2), pages 205-232.
    19. Tölö, Eero, 2019. "Predicting systemic financial crises with recurrent neural networks," Research Discussion Papers 14/2019, Bank of Finland.
    20. Ward, Felix, 2014. "Spotting the Danger Zone - Forecasting Financial Crises with Classification Tree Ensembles and Many Predictors," Bonn Econ Discussion Papers 01/2014, University of Bonn, Bonn Graduate School of Economics (BGSE).
    21. Wang, Peiwan & Zong, Lu, 2023. "Does machine learning help private sectors to alarm crises? Evidence from China’s currency market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 611(C).
    22. Roy, Saktinil, 2022. "What drives the systemic banking crises in advanced economies?," Global Finance Journal, Elsevier, vol. 54(C).

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sav:journl:v:70:y:2022:i:1:p:3-21. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Peter Renac (email available below). General contact details of provider: https://edirc.repec.org/data/eusavsk.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.