IDEAS home Printed from https://ideas.repec.org/p/boh/wpaper/06_2023.html

European Insurance Market Analysis via a Joint Functional Clustering Method

Author

Listed:
  • Stavros Athanasiadis

Abstract

The enlargement of the European Union (EU) to include Central and South-Eastern European countries in 2004 and 2007 launched an integration process that unifies the economies and financial markets of member states and enables the convergence of these two areas. This study focuses on analyzing the development and similarity of the European insurance sector after the EU enlargement. We study 34 European insurance markets from 2004 until 2021 based on a certain set of indicators that characterize insurance markets, such as Insurance Density, Insurance Penetration and Gross Written Premiums to name a few. With a functional clustering method applied to such indicators, we try to reveal whether there are similarities between the individual countries that could explain the European insurance market homogeneity and convergence via the EU integration process. The proposed method has also a practical importance since it provides visualization of the clustering results through the construction of global envelopes. This study supports the works of EU policy makers that have a major impact on the ability of further integration of the European insurance market.Keywords: European insurance markets,

Suggested Citation

  • Stavros Athanasiadis, 2023. "European Insurance Market Analysis via a Joint Functional Clustering Method," Economics Working Papers 2023-06, University of South Bohemia in Ceske Budejovice, Faculty of Economics, revised 30 Nov 2023.
    • Handle: RePEc:boh:wpaper:06_2023
    DOI: 10.32725/ewp.2023.006
    as

    Download full text from publisher

    File URL: http://ewp.ef.jcu.cz/doi/10.32725/ewp.2023.006.html
    Download Restriction: free of charge
    File URL: http://ewp.ef.jcu.cz/doi/10.32725/ewp.2023.006.pdf
    Download Restriction: free of charge
    File URL: https://libkey.io/10.32725/ewp.2023.006?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Christian Hennig & Tim F. Liao, 2013. "How to find an appropriate clustering for mixed-type variables with application to socio-economic stratification," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 62(3), pages 309-369, May.
    2. Zhangyao Zhu & Na Liu & Wei Wang, 2021. "Early Warning of Financial Risk Based on K-Means Clustering Algorithm," Complexity, Hindawi, vol. 2021, pages 1-12, March.
    3. in ’t Veld, Jan, 2019. "The economic benefits of the EU Single Market in goods and services," Journal of Policy Modeling, Elsevier, vol. 41(5), pages 803-818.
    4. Alan Kirman, 2006. "Heterogeneity in Economics," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 1(1), pages 89-117, May.
    5. W. Jean Kwon & Leigh Wolfrom, 2016. "Analytical tools for the insurance market and macro-prudential surveillance," OECD Journal: Financial Market Trends, OECD Publishing, vol. 2016(1), pages 1-47.
    6. Dai, Wenlin & Mrkvička, Tomáš & Sun, Ying & Genton, Marc G., 2020. "Functional outlier detection and taxonomy by sequential transformations," Computational Statistics & Data Analysis, Elsevier, vol. 149(C).
    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. Pietro Coretto & Christian Hennig, 2016. "Robust Improper Maximum Likelihood: Tuning, Computation, and a Comparison With Other Methods for Robust Gaussian Clustering," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(516), pages 1648-1659, October.
    2. Dayal, Eshwar & Khuntia, Lisa & Lakshay, Lakshay & Buldeo Rai, Heleen & Pani, Agnivesh, 2025. "Behavioral intention to use parcel lockers in the last mile and underlying linkages with travel modal choice," Research in Transportation Economics, Elsevier, vol. 111(C).
    3. Beckman, Jayson & Burfisher, Mary & Mitchell, Lorraine & Arita, Shawn, 2021. "Hidden obstacles to trade: The case of the EU’s Ban on beef hormones," Journal of Policy Modeling, Elsevier, vol. 43(6), pages 1332-1343.
    4. Alan Kirman & François Laisney & Paul Pezanis-Christou, 2023. "Relaxing the symmetry assumption in participation games: a specification test for cluster-heterogeneity," Experimental Economics, Springer;Economic Science Association, vol. 26(4), pages 850-878, September.
    5. Yeh, Chia-Hsuan & Yang, Chun-Yi, 2010. "Examining the effectiveness of price limits in an artificial stock market," Journal of Economic Dynamics and Control, Elsevier, vol. 34(10), pages 2089-2108, October.
    6. Fritz Breuss, 2020. "25 Years of Austria's EU Membership. Quantifying the Economic Benefits With a DSGE Model," WIFO Working Papers 603, WIFO.
    7. Serena Brianzoni & Roy Cerqueti & Elisabetta Michetti, 2010. "A Dynamic Stochastic Model of Asset Pricing with Heterogeneous Beliefs," Computational Economics, Springer;Society for Computational Economics, vol. 35(2), pages 165-188, February.
    8. Hyuk Rhee & Nurlan Turdaliev, 2015. "Central bank policy instrument forecasts," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 10(2), pages 221-245, October.
    9. Morgan, Grant B. & Hodge, Kari J. & Baggett, Aaron R., 2016. "Latent profile analysis with nonnormal mixtures: A Monte Carlo examination of model selection using fit indices," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 146-161.
    10. Elvira Pelle & Roberta Pappadà, 2021. "A clustering procedure for mixed-type data to explore ego network typologies: an application to elderly people living alone in Italy," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(5), pages 1507-1533, December.
    11. Jack Blundell, 2020. "Clusters in UK Self-Employment," CEP Occasional Papers 048, Centre for Economic Performance, LSE.
    12. Wittmann, Nadine, 2014. "Regulating gasoline retail markets: The case of Germany," Economics Discussion Papers 2014-17, Kiel Institute for the World Economy.
    13. Ojo, Oluwasegun Taiwo & Fernández Anta, Antonio & Genton, Marc G. & Lillo Rodríguez, Rosa Elvira, 2022. "Multivariate Functional Outlier Detection using the FastMUOD Indices," DES - Working Papers. Statistics and Econometrics. WS 35665, Universidad Carlos III de Madrid. Departamento de Estadística.
    14. Brittney Goodrich & Jisang Yu & Monte Vandeveer, 2020. "Participation patterns of the rainfall index insurance for pasture, rangeland and forage programme," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 45(1), pages 29-51, January.
    15. Zixian Liu & Guansan Du & Shuai Zhou & Haifeng Lu & Han Ji, 2022. "Analysis of Internet Financial Risks Based on Deep Learning and BP Neural Network," Computational Economics, Springer;Society for Computational Economics, vol. 59(4), pages 1481-1499, April.
    16. Kirman, Alan P. & Laisney, François & Pezanis-Christou, Paul, 2018. "Exploration vs exploitation, impulse balance equilibrium, and a specification test for the El Farol bar problem," ZEW Discussion Papers 18-038, ZEW - Leibniz Centre for European Economic Research.
    17. Roberto Mari & Roberto Rocci & Stefano Antonio Gattone, 2020. "Scale-constrained approaches for maximum likelihood estimation and model selection of clusterwise linear regression models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 29(1), pages 49-78, March.
    18. Jean-Michel Grandmont, 2017. "Behavioral Heterogeneity: Pareto Distributions of Homothetic Preference Scales and Aggregate Expenditures Income Elasticities," Discussion Paper Series DP2017-31, Research Institute for Economics & Business Administration, Kobe University.
    19. Fernández, D. & Arnold, R. & Pledger, S., 2016. "Mixture-based clustering for the ordered stereotype model," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 46-75.
    20. Layal Christine Lettry, 2023. "Clustering the Swiss Pension Register," FSES Working Papers 529, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • F15 - International Economics - - Trade - - - Economic Integration
    • F36 - International Economics - - International Finance - - - Financial Aspects of Economic Integration
    • G22 - Financial Economics - - Financial Institutions and Services - - - Insurance; Insurance Companies; Actuarial Studies

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:boh:wpaper:06_2023. 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: Markéta Matějíčková (email available below). General contact details of provider: https://edirc.repec.org/data/efjcucz.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.