Article accepted for publication in ISI international journal, equivalent to FIISEG category B: Authors: Jorge Caiado (ISEG) and João Bastos (ISEG):
BASTOS, J. A. and CAIADO, J. (2012). Clustering financial time series with variance ratio statistics, Quantitative Finance, Taylor & Francis.
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Jorge Caiado (PhD) is Assistant Professor at the Instituto Superior de Economia e Gestão (ISEG) and researcher at the Center for Applied Mathematics for Economic Forecasting and Decision (CEMAPRE). |
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João Bastos (PhD) is an Invited Assistant Professor at the Instituto Superior de Economia e Gestão (ISEG) and a researcher at the Center for Applied Mathematics for Forecasting and Economic Decision (CEMAPRE). |
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.
