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

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  • Caiado, Jorge
  • Crato, Nuno

Abstract

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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Bibliographic Info

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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Keywords: Cluster analysis; GARCH; International stock markets; Volatility;

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References

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  1. Theodore Syriopoulos, 2004. "International portfolio diversification to Central European stock markets," Applied Financial Economics, Taylor & Francis Journals, vol. 14(17), pages 1253-1268.
  2. 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.
  3. Tim Bollerslev, 1986. "Generalized autoregressive conditional heteroskedasticity," EERI Research Paper Series EERI RP 1986/01, Economics and Econometrics Research Institute (EERI), Brussels.
  4. Tak-Kee Hui, 2005. "Portfolio diversification: a factor analysis approach," Applied Financial Economics, Taylor & Francis Journals, vol. 15(12), pages 821-834.
  5. 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.
  6. 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.
  7. 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.
  8. 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.
  9. 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.
  10. 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.
  11. 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.
  12. Ammer, John & Mei, Jianping, 1996. " Measuring International Economic Linkages with Stock Market Data," Journal of Finance, American Finance Association, vol. 51(5), pages 1743-63, December.
  13. 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.
  14. Karolyi, G Andrew & Stulz, Rene M, 1996. " Why Do Markets Move Together? An Investigation of U.S.-Japan Stock Return Comovements," Journal of Finance, American Finance Association, vol. 51(3), pages 951-86, July.
  15. Caiado, Jorge & Crato, Nuno & Peña, Daniel, 2007. "Comparison of time series with unequal length," MPRA Paper 6605, University Library of Munich, Germany.
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Citations

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Cited by:
  1. Luca De Angelis, 2013. "Latent class models for financial data analysis: some statistical developments," Statistical Methods and Applications, Springer, vol. 22(2), pages 227-242, June.
  2. F. Lisi & E. Otranto, 2008. "Clustering Mutual Funds by Return and Risk Levels," Working Paper CRENoS 200813, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
  3. D’Urso, Pierpaolo & Cappelli, Carmela & Di Lallo, Dario & Massari, Riccardo, 2013. "Clustering of financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(9), pages 2114-2129.
  4. Anna CZAPKIEWICZ & Pawel MAJDOSZ, 2014. "Grouping Stock Markets with Time-Varying Copula-GARCH Model," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 64(2), pages 144-159, March.

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