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Measuring Nonlinear Dependence in Time‐Series, a Distance Correlation Approach

20 Pages Posted: 21 Apr 2012  

Zhou Zhou

University of Toronto

Date Written: May 2012

Abstract

We extend the concept of distance correlation of Szekely et al. (2007) and propose the auto distance correlation function (ADCF) to measure the temporal dependence structure of time‐series. Unlike the classic measures of correlations such as the autocorrelation function, the proposed measure is zero if and only if the measured time‐series components are independent. In this article, we propose and theoretically verify a subsampling methodology for the inference of sample ADCF for dependent data. Our methodology provides a useful tool for exploring nonlinear dependence structures in time‐series.

Keywords: Autodistance correlation function, nonlinear time‐series, nonlinear dependence, subsampling

Suggested Citation

Zhou, Zhou, Measuring Nonlinear Dependence in Time‐Series, a Distance Correlation Approach (May 2012). Journal of Time Series Analysis, Vol. 33, Issue 3, pp. 438-457, 2012. Available at SSRN: https://ssrn.com/abstract=2042910 or http://dx.doi.org/10.1111/j.1467-9892.2011.00780.x

Zhou Zhou (Contact Author)

University of Toronto ( email )

Department of Statistical Sciences
Toronto, Ontario M5S 3G8
Canada

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