CUPAL, Martin, Oleg DEEV and Dagmar LINNERTOVÁ. Network structures of the European stock market. In Ramík, J. and Stavárek, D. Proceedings of the 30th International Conference Mathematical Methods in Economics. Karviná: Silesian University, School of Business Administration, 2012, p. 79-84. ISBN 978-80-7248-779-0.
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Basic information
Original name Network structures of the European stock market
Authors CUPAL, Martin (203 Czech Republic, belonging to the institution), Oleg DEEV (643 Russian Federation, guarantor, belonging to the institution) and Dagmar LINNERTOVÁ (203 Czech Republic, belonging to the institution).
Edition Karviná, Proceedings of the 30th International Conference Mathematical Methods in Economics, p. 79-84, 6 pp. 2012.
Publisher Silesian University, School of Business Administration
Other information
Original language English
Type of outcome Proceedings paper
Field of Study 50200 5.2 Economics and Business
Country of publisher Czech Republic
Confidentiality degree is not subject to a state or trade secret
Publication form printed version "print"
WWW URL
RIV identification code RIV/00216224:14560/12:00060882
Organization unit Faculty of Economics and Administration
ISBN 978-80-7248-779-0
UT WoS 000316715900014
Keywords in English stock markets; cross-correlation networks; network topology
Tags International impact, Reviewed
Changed by Changed by: Ing. Dagmar Vágnerová Linnertová, Ph.D., učo 76289. Changed: 7/4/2014 13:11.
Abstract
The paper examines changing topological characteristics of correlation-based network of European stock markets on both national and supranational levels. First, the problem of how to correctly build a representative correlation-based procedure and choose a specific filtering procedure for identifying the strongest links is addressed. Then, network structures are investigated on several datasets, for which the data of different time intervals and varying frequency are assembled. On a national level, core stem of stock markets of highly developed countries is found to be stable over time with French market playing the central role. On the supranational level, stocks are clustered based on their economic sector, rather than country’s origin. Network modeling of a stock market proves to be highly useful and powerful tool, since network formulation could give much insight and understanding on mutual dependence of stocks’ behavior by simply examining graphic representation of the market.
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