Using Joint Random Partition Models for Flexible Change Point Analysis in Multivariate Processes

TL;DR

This paper introduces a joint partition model for multivariate change point detection that leverages shared information across processes, improving detection accuracy and providing new insights in financial data analysis.

Contribution

It develops a novel joint model for change point analysis in multivariate processes that accounts for shared change point information and demonstrates computational strategies and practical applications.

Findings

Improved change point detection performance in simulations.

Application to Latin American financial data reveals correlated change points.

Method uncovers meaningful economic insights.

Abstract

Change point analyses are concerned with identifying positions of an ordered stochastic process that undergo abrupt local changes of some underlying distribution. When multiple processes are observed, it is often the case that information regarding the change point positions is shared across the different processes. This work describes a method that takes advantage of this type of information. Since the number and position of change points can be described through a partition with contiguous clusters, our approach develops a joint model for these types of partitions. We describe computational strategies associated with our approach and illustrate improved performance in detecting change points through a small simulation study. We then apply our method to a financial data set of emerging markets in Latin America and highlight interesting insights discovered due to the correlation between change point locations among these economies.