Franzolini, Beatrice;
Beskos, Alexandros;
De Iorio, Maria;
Koziell, Warrick Poklewski;
Grzeszkiewicz, Karolina;
(2024)
Change point detection in dynamic Gaussian graphical models: The impact of COVID-19 pandemic on the U.S. stock market.
The Annals of Applied Statistics
, 18
(1)
pp. 555-584.
10.1214/23-AOAS1801.
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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 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.
| Type: | Article |
|---|---|
| Title: | Change point detection in dynamic Gaussian graphical models: The impact of COVID-19 pandemic on the U.S. stock market |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1214/23-AOAS1801 |
| Publisher version: | https://doi.org/10.1214/23-AOAS1801 |
| 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: | Coronavirus pandemic, graphical models, industry portoflios, particle filter, precision matrix, stochastic volatility |
| 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-pp.ucl.ac.uk/id/eprint/10173023 |
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