A GARCH-based method for clustering of financial time series: International stock markets evidence
AbstractIn 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 InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 2074.
Date of creation: 2007
Date of revision:
Cluster analysis; GARCH; International stock markets; Volatility;
Find related papers by JEL classification:
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
- G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
This paper has been announced in the following NEP Reports:
- NEP-ALL-2007-03-17 (All new papers)
- NEP-ECM-2007-03-17 (Econometrics)
- NEP-ETS-2007-03-17 (Econometric Time Series)
- NEP-RMG-2007-03-17 (Risk Management)
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