Measuring nonlinear dependence in time‐series, a distance correlation approach

Zhou Zhou

119 citations0/0 files storedJournal of Time Series Analysis

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Financial Risk and Volatility Modeling Complex Systems and Time Series Analysis Time Series Analysis and Forecasting

Abstract

We extend the concept of distance correlation of Szekelyet 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.

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References

1986Generalized autoregressive conditional heteroskedasticity 1982Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation 1977Time Series Analysis: Forecasting and Control 2005Analysis of Financial Time Series 2000Nonlinear Time Series Models in Empirical Finance 2000Stable Paretian Models in Finance 1998The sample autocorrelations of heavy-tailed processes with applications to ARCH

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