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Detection of multiple change-points in multivariate time series

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Abstract

We consider the multiple change-point problem for multivariate time series, including strongly dependent processes, with an unknown number of change-points. We assume that the covariance structure of the series changes abruptly at some unknown common change-point times. The proposed adaptive method is able to detect changes in multivariate i.i.d., weakly and strongly dependent series. This adaptive method outperforms the Schwarz criteria, mainly for the case of weakly dependent data. We consider applications to multivariate series of daily stock indices returns and series generated by an artificial financial market.

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Authors and Affiliations

  1. Laboratoire de Mathématiques, Université René Descartes et Université Paris-Sud, France

    M. Lavielle

  2. Statistique Appliqué et MOdélisation Stochastique, CES, Université Paris 1 Panthéon-Sorbonne, France

    G. Teyssière

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  1. M. Lavielle
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  2. G. Teyssière
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__________

Translated from Lietuvos Matematikos Rinkinys, Vol. 46, No. 3, pp. 351–376, July–September, 2006.

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Lavielle, M., Teyssière, G. Detection of multiple change-points in multivariate time series. Lith Math J 46, 287–306 (2006). https://doi.org/10.1007/s10986-006-0028-9

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  • DOI: https://doi.org/10.1007/s10986-006-0028-9

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