Clustering financial time series with variance ratio statistics

João Bastos and Jorge Caiado

No 904, CEMAPRE Working Papers from Centre for Applied Mathematics and Economics (CEMAPRE), School of Economics and Management (ISEG), Technical University of Lisbon

Abstract: This study introduces a new distance measure for clustering financial time series based on variance ratio test statistics. The proposed metric attempts to assess the level of interdependence of time series from the point of view of return predictability. Simulation results show that this metric aggregates better time series according to their serial dependence structure than a metric based on the sample autocorrelations. An empirical application of this approach to international stock market returns is presented. The results suggest that this metric discriminates reasonably well stock markets according to size and level of development. Furthermore, despite the substantial evolution of individual variance ratio statistics, the clustering pattern remains fairly stable across different time periods.

Keywords: Time series; Cluster analysis; Multidimensional scaling; Variance ratio test; International stock market (search for similar items in EconPapers)
JEL-codes: G14 G15 (search for similar items in EconPapers)
Pages: 28 pages
Date: 2009-09
New Economics Papers: this item is included in nep-fmk
References: View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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Journal Article: Clustering financial time series with variance ratio statistics (2014)
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