Uniform change point tests in high dimension

TL;DR

This paper develops asymptotic theory for high-dimensional change point tests based on CUSUM statistics, enabling simultaneous confidence bands and change point localization in complex dependent data.

Contribution

It introduces a flexible, nonparametric framework for high-dimensional change point detection that handles large d, small n scenarios and includes dependent models like ARMA and GARCH.

Findings

Asymptotic distribution for maximum CUSUM statistics is derived.

Method successfully applied to S&P 500 data for change point analysis.

Framework accommodates various dependence structures and nonlinear models.

Abstract

Consider $d$ dependent change point tests, each based on a CUSUM-statistic. We provide an asymptotic theory that allows us to deal with the maximum over all test statistics as both the sample size $n$ and $d$ tend to infinity. We achieve this either by a consistent bootstrap or an appropriate limit distribution. This allows for the construction of simultaneous confidence bands for dependent change point tests, and explicitly allows us to determine the location of the change both in time and coordinates in high-dimensional time series. If the underlying data has sample size greater or equal $n$ for each test, our conditions explicitly allow for the large $d$ small $n$ situation, that is, where $n/d\to0$. The setup for the high-dimensional time series is based on a general weak dependence concept. The conditions are very flexible and include many popular multivariate linear and nonlinear models from the literature, such as ARMA, GARCH and related models. The construction of the tests is completely nonparametric, difficulties associated with parametric model selection, model fitting and parameter estimation are avoided. Among other things, the limit distribution for $\max_{1\leq h\leq d}\sup_{0\leq t\leq1}\vert \mathcal{W}_{t,h}-t\mathcal{W}_{1,h}\vert$ is established, where $\{\mathcal{W}_{t,h}\}_{1\leq h\leq d}$ denotes a sequence of dependent Brownian motions. As an application, we analyze all S&P 500 companies over a period of one year.