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Country effects in CEE3 stock market networks: a preliminary study

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  • Výrost, Tomáš

Abstract

The stock markets in the Czech Republic, Poland and Hungary (CEE3) are studied in the context of stock market networks. A total of 17 shares are followed during the period of 1998 – 2012. The daily returns are used for calculation of rolling correlations of various window lengths. The resulting correlation matrices are then used to construct network models. Minimum spanning trees (MST) are used as a form of abstraction in the graph structure, and their evolution is studied over time. The main objective of the paper is to test whether the individual assets cluster in the MSTs by the country to which they belong or whether the origin is of lesser importance, leading to cross-country links within the MSTs. The latter might hint at increasing integration within CEE3 stock markets. We find that at the beginning of the series, the MSTs exhibited very strong country clustering, which changed in the later 2000s. The country effects do not seem to be synchronized between all markets.

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Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 43481.

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Date of creation: 29 Dec 2012
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Handle: RePEc:pra:mprapa:43481

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Keywords: stock market networks; minimum spanning trees; stock market integration;

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  1. R. Mantegna, 1999. "Hierarchical structure in financial markets," The European Physical Journal B - Condensed Matter and Complex Systems, Springer, vol. 11(1), pages 193-197, September.
  2. N. Vandewalle & F. Brisbois & X. Tordoir, 2001. "Non-random topology of stock markets," Quantitative Finance, Taylor & Francis Journals, vol. 1(3), pages 372-374.
  3. Lyócsa, Štefan & Výrost, Tomáš & Baumöhl, Eduard, 2012. "Stock market networks: The dynamic conditional correlation approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(16), pages 4147-4158.
  4. Tse, Chi K. & Liu, Jing & Lau, Francis C.M., 2010. "A network perspective of the stock market," Journal of Empirical Finance, Elsevier, vol. 17(4), pages 659-667, September.
  5. Lyócsa, Štefan & Baumöhl, Eduard, 2012. "Testing the covariance stationarity of CEE stocks," MPRA Paper 43432, University Library of Munich, Germany.
  6. J.-P. Onnela & K. Kaski & J. Kertész, 2004. "Clustering and information in correlation based financial networks," The European Physical Journal B - Condensed Matter and Complex Systems, Springer, vol. 38(2), pages 353-362, 03.
  7. Baumöhl, Eduard & Lyócsa, Štefan, 2012. "Constructing weekly returns based on daily stock market data: A puzzle for empirical research?," MPRA Paper 43431, University Library of Munich, Germany.
  8. Lee, Junghoon & Youn, Janghyuk & Chang, Woojin, 2012. "Intraday volatility and network topological properties in the Korean stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(4), pages 1354-1360.
  9. Tabak, Benjamin M. & Serra, Thiago R. & Cajueiro, Daniel O., 2010. "Topological properties of stock market networks: The case of Brazil," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(16), pages 3240-3249.
  10. Onnela, J.-P. & Chakraborti, A. & Kaski, K. & Kertész, J., 2003. "Dynamic asset trees and Black Monday," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 324(1), pages 247-252.
  11. Giovanni Bonanno & Fabrizio Lillo & Rosario N. Mantegna, 2000. "High-frequency Cross-correlation in a Set of Stocks," Papers cond-mat/0009350, arXiv.org, revised Nov 2000.
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