March 2024 Change point detection in dynamic Gaussian graphical models: The impact of COVID-19 pandemic on the U.S. stock market
Beatrice Franzolini, Alexandros Beskos, Maria De Iorio, Warrick Poklewski Koziell, Karolina Grzeszkiewicz
Author Affiliations +
Ann. Appl. Stat. 18(1): 555-584 (March 2024). DOI: 10.1214/23-AOAS1801

Abstract

Reliable estimates of volatility and correlation are fundamental in economics and finance for understanding the impact of macroeconomics events on the market and guiding future investments and policies. Dependence across financial returns is likely to be subject to sudden structural changes, especially in correspondence with major global events, such as the COVID-19 pandemic. In this work we are interested in capturing abrupt changes over time in the conditional dependence across U.S. industry stock portfolios, over a time horizon that covers the COVID-19 pandemic. The selected stocks give a comprehensive picture of the U.S. stock market. To this end, we develop a Bayesian multivariate stochastic volatility model based on a time-varying sequence of graphs capturing the evolution of the dependence structure. The model builds on the Gaussian graphical models and the random change points literature. In particular, we treat the number, the position of change points, and the graphs as object of posterior inference, allowing for sparsity in graph recovery and change point detection. The high dimension of the parameter space poses complex computational challenges. However, the model admits a hidden Markov model formulation. This leads to the development of an efficient computational strategy, based on a combination of sequential Monte-Carlo and Markov chain Monte-Carlo techniques. Model and computational development are widely applicable, beyond the scope of the application of interest in this work.

Funding Statement

This work was supported by the Singapore Ministry of Education Academic Research Fund Tier 2 under Grant MOE2019-T2-2-100.
B. Franzolini is supported by PNRR - PE1 FAIR - CUP B43C22000800006.

Acknowledgments

The authors are grateful to the Editor, an Associate Editor, and five anonymous referees for insightful comments and suggestions, which led to a substantial improvement of the manuscript. Most of the paper was completed while B. Franzolini was a Research Fellow at the Singapore Institute for Clinical Sciences of the Agency for Science, Technology and Research, in Singapore, Republic of Singapore. M. De Iorio is also affiliated with the Department of Statistical Sciences of the Unviersity College of London and the Singapore Institute for Clinical Sciences of the Agency for Science, Technology and Research.

Citation

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Beatrice Franzolini. Alexandros Beskos. Maria De Iorio. Warrick Poklewski Koziell. Karolina Grzeszkiewicz. "Change point detection in dynamic Gaussian graphical models: The impact of COVID-19 pandemic on the U.S. stock market." Ann. Appl. Stat. 18 (1) 555 - 584, March 2024. https://doi.org/10.1214/23-AOAS1801

Information

Received: 1 August 2022; Revised: 1 July 2023; Published: March 2024
First available in Project Euclid: 31 January 2024

Digital Object Identifier: 10.1214/23-AOAS1801

Keywords: Coronavirus pandemic , graphical models , industry portoflios , particle filter , precision matrix , stochastic volatility

Rights: Copyright © 2024 Institute of Mathematical Statistics

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Vol.18 • No. 1 • March 2024
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