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A GARCH-based method for clustering of financial time series: International stock markets evidence

  • Caiado, Jorge
  • Crato, Nuno

In this paper, we introduce a volatility-based method for clustering analysis of financial time series. Using the generalized autoregressive conditional heteroskedasticity (GARCH) models we estimate the distances between the stock return volatilities. The proposed method uses the volatility behavior of the time series and solves the problem of different lengths. As an illustrative example, we investigate the similarities among major international stock markets using daily return series with different sample sizes from 1966 to 2006. From cluster analysis, most European markets countries, United States and Canada appear close together, and most Asian/Pacific markets and the South/Middle American markets appear in a distinct cluster. After the terrorist attack on September 11, 2001, the European stock markets have become more homogenous, and North American markets, Japan and Australia seem to come closer.

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File URL: http://mpra.ub.uni-muenchen.de/2074/1/MPRA_paper_2074.pdf
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Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 2074.

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Date of creation: 2007
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Handle: RePEc:pra:mprapa:2074
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  1. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
  2. G. Bonanno & F. Lillo & R. N. Mantegna, 2001. "High-frequency cross-correlation in a set of stocks," Quantitative Finance, Taylor & Francis Journals, vol. 1(1), pages 96-104.
  3. Ball, Clifford A. & Torous, Walter N., 2000. "Stochastic correlation across international stock markets," Journal of Empirical Finance, Elsevier, vol. 7(3-4), pages 373-388, November.
  4. Ball, Clifford A. & Torous, Walter N., 2000. "Stochastic Correlation Across International Stock Markets," University of California at Los Angeles, Anderson Graduate School of Management qt6vn9q79w, Anderson Graduate School of Management, UCLA.
  5. 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.
  6. John Ammer & Jianping Mei, 1993. "Measuring international economic linkages with stock market data," International Finance Discussion Papers 449, Board of Governors of the Federal Reserve System (U.S.).
  7. Caiado, Jorge & Crato, Nuno & Peña, Daniel, 2007. "Comparison of time series with unequal length," MPRA Paper 6605, University Library of Munich, Germany.
  8. Theodore Syriopoulos, 2004. "International portfolio diversification to Central European stock markets," Applied Financial Economics, Taylor & Francis Journals, vol. 14(17), pages 1253-1268.
  9. Bollerslev, Tim & Chou, Ray Y. & Kroner, Kenneth F., 1992. "ARCH modeling in finance : A review of the theory and empirical evidence," Journal of Econometrics, Elsevier, vol. 52(1-2), pages 5-59.
  10. Ramchand, Latha & Susmel, Raul, 1998. "Volatility and cross correlation across major stock markets," Journal of Empirical Finance, Elsevier, vol. 5(4), pages 397-416, October.
  11. G. Andrew Karoly & Rene Stulz, . "Why do Markets Move Together? An Investigation of U.S.-Japan Stock Return Comovements," Research in Financial Economics 9603, Ohio State University.
  12. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
  13. Bessler, David A. & Yang, Jian, 2003. "The structure of interdependence in international stock markets," Journal of International Money and Finance, Elsevier, vol. 22(2), pages 261-287, April.
  14. Tak-Kee Hui, 2005. "Portfolio diversification: a factor analysis approach," Applied Financial Economics, Taylor & Francis Journals, vol. 15(12), pages 821-834.
  15. A. Tahai & Robert Rutledge & Khondkar Karim, 2004. "An examination of financial integration for the group of seven (G7) industrialized countries using an I( ) cointegration model," Applied Financial Economics, Taylor & Francis Journals, vol. 14(5), pages 327-335.
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