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Change point detection in dynamic Gaussian graphical models: the impact of COVID-19 pandemic on the US stock market

Franzolini, Beatrice; Beskos, Alexandros; Iorio, Maria De; Koziell, Warrick Poklewski; Grzeszkiewicz, Karolina; (2022) Change point detection in dynamic Gaussian graphical models: the impact of COVID-19 pandemic on the US stock market. arXiv: Ithaca, NY, USA. Green open access

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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 US industry stock portfolios, over a time horizon that covers the COVID-19 pandemic. The selected stocks give a comprehensive picture of the US 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.

Type: Working / discussion paper
Title: Change point detection in dynamic Gaussian graphical models: the impact of COVID-19 pandemic on the US stock market
Open access status: An open access version is available from UCL Discovery
DOI: 10.48550/arXiv.2208.00952
Publisher version: http://arxiv.org/abs/2208.00952v2
Language: English
Additional information: This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: stat.ME, stat.ME, q-fin.ST, stat.AP
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI: https://discovery.ucl.ac.uk/id/eprint/10173024
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