Clustering financial time series with variance ratio statistics
AbstractThis 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.
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Bibliographic InfoPaper provided by Centre for Applied Mathematics and Economics (CEMAPRE), School of Economics and Management (ISEG), Technical University of Lisbon in its series CEMAPRE Working Papers with number 0904.
Length: 28 pages
Date of creation: Sep 2009
Date of revision:
Time series; Cluster analysis; Multidimensional scaling; Variance ratio test; International stock market;
Find related papers by JEL classification:
- G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
- G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
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