Recursive Segmentation Procedure Based on the Akaike Information Criterion Test

Abstract

This study proposes a recursive segmentation procedure for multivariate time series based on Akaike information criterion. The Akaike information criterion, between independently identically distributed multivariate Gaussian samples and two successive segments drawn from different multivariate Gaussian distributions, is used as a discriminator to segment multivariate time series. The bootstrap method is employed in order to evaluate the statistical significance level. The proposed method is performed for an artificial multi-dimensional time series consisting of two segments with different statistics. The log-return time series of currency exchange rates for 30 currency pairs for the period from January 4, 2001 to December 30, 2011 are also divided into 11 segments with the proposed method. This method confirms that some segments correspond to historical events recorded as critical situations.