Research Repository

Change point analysis of covariance functions: A weighted cumulative sum approach

Horváth, Lajos and Rice, Gregory and Zhao, Yuqian (2022) 'Change point analysis of covariance functions: A weighted cumulative sum approach.' Journal of Multivariate Analysis, 189. p. 104877. ISSN 0047-259X

[img] Text
funvolChange.pdf - Accepted Version
Restricted to Repository staff only until 2 November 2022.
Available under License Creative Commons Attribution Non-commercial No Derivatives.

Download (2MB) | Request a copy

Abstract

We develop and study change point detection and estimation procedures for the covariance kernel of functional data based on the norms of a generally weighted process of partial sample estimates. It is shown under mild weak dependence and moment conditions on the data that in the absence of a change point a detector based on integrating such a process over the partial sample parameter is asymptotically distributed as the norm of a Gaussian process, which furnishes a consistent change point detection procedure. We further derive consistency and local asymptotic results for this detector in the presence of a change in the covariance function. The corresponding change point estimator based on such a process is also shown to be rate optimal for estimating an existing change point, and further is asymptotically distributed as the argument maximum of a Gaussian process under a local asymptotic framework. We study the detector and change point estimator in a small simulation study to detect changes in the covariance of functional autoregressive and generalized conditionally heteroscedastic processes, which demonstrate that the use of the weighted CUSUM statistics in this context generally improves performance over existing methods. These new statistics are demonstrated in an application to detecting changes in the volatility of high resolution intraday asset price curves derived from oil futures prices.

Item Type: Article
Uncontrolled Keywords: Approximation of partial sums of functions; Bernoulli shift; Change point detection; Functional data
Divisions: Faculty of Social Sciences
Faculty of Social Sciences > Essex Business School
Faculty of Social Sciences > Essex Business School > Essex Finance Centre
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 02 Nov 2021 11:11
Last Modified: 22 Feb 2022 10:18
URI: http://repository.essex.ac.uk/id/eprint/31414

Actions (login required)

View Item View Item