European Insurance Market Analysis: A Multivariate Clustering approach

Kód výsledku v IS VaVaI

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Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

European Insurance Market Analysis: A Multivariate Clustering approach

Popis výsledku v původním jazyce

Clustering has been proved to extract valuable information resided in complex and massive data sets. Motivated by this evidence, this paper is aimed to provide a multivariate clustering of European insurance market in terms of the insurance penetration curves of European countries. Yet, at the same time, this clustering is provided through two different cases, where the first case considers only the magnitude (size) of these curves, and the second considers only their shape. In this paper, two partitional clustering methods are utilized, k-means and Gaussian mixture model, with two distance measures, the Euclidean and Mahalanobis distance, respectively. Both clustering methods form clusters within a sample of 34 European countries observed between 2004 and 2016; that is before, during and post-financial and sovereign debt crises. The clustering solutions also reveal the extent to which the employed clustering methods and distance measures are being able to capture the distinctive properties of the original curves as depicted during the period under examination.

Název v anglickém jazyce

European Insurance Market Analysis: A Multivariate Clustering approach

Popis výsledku anglicky

Clustering has been proved to extract valuable information resided in complex and massive data sets. Motivated by this evidence, this paper is aimed to provide a multivariate clustering of European insurance market in terms of the insurance penetration curves of European countries. Yet, at the same time, this clustering is provided through two different cases, where the first case considers only the magnitude (size) of these curves, and the second considers only their shape. In this paper, two partitional clustering methods are utilized, k-means and Gaussian mixture model, with two distance measures, the Euclidean and Mahalanobis distance, respectively. Both clustering methods form clusters within a sample of 34 European countries observed between 2004 and 2016; that is before, during and post-financial and sovereign debt crises. The clustering solutions also reveal the extent to which the employed clustering methods and distance measures are being able to capture the distinctive properties of the original curves as depicted during the period under examination.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

50204 - Business and management

Projekt

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Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2018

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the 12th International Scientific Conference INPROFORUM. Innovations, Enterprises, Regions and Management

ISBN

978-80-7394-726-2

ISSN

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e-ISSN

neuvedeno

Počet stran výsledku

7

Strana od-do

328-334

Název nakladatele

Jihočeská univerzita v Českých Budějovicích, Ekonomická fakulta

Místo vydání

České Budějovice

Místo konání akce

České Budějovice

Datum konání akce

1. 11. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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