Artigo aceite para publicação em revista internacional do ISI, equivalente à categoria B do FIISEG: Autores: Jorge Caiado (ISEG) e João Bastos (ISEG):
BASTOS, J. A. e CAIADO, J. (2012). Clustering financial time series with variance ratio statistics, Quantitative Finance, Taylor & Francis.
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Jorge Caiado (PhD) é Professor Auxiliar do Instituto Superior de Economia e Gestão (ISEG) e investigador do Centro de Matemática Aplicada à Previsão e Decisão Económica (CEMAPRE). |
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João Bastos (PhD) é Professor Auxiliar Convidado do Instituto Superior de Economia e Gestão (ISEG) e investigador do Centro de Matemática Aplicada à Previsão e Decisão Económica (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.
