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A moving-window bayesian network model for assessing systemic risk in financial markets
Systemic risk refers to the uncertainty that arises due to the breakdown of a financial system. The concept of “too connected to fail” suggests that network connectedness plays an important role in measuring systemic risk. In this paper, we first recover a time series of Bayesian networks for stock...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9858016/ https://www.ncbi.nlm.nih.gov/pubmed/36662719 http://dx.doi.org/10.1371/journal.pone.0279888 |
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author | Chan, Lupe S. H. Chu, Amanda M. Y. So, Mike K. P. |
author_facet | Chan, Lupe S. H. Chu, Amanda M. Y. So, Mike K. P. |
author_sort | Chan, Lupe S. H. |
collection | PubMed |
description | Systemic risk refers to the uncertainty that arises due to the breakdown of a financial system. The concept of “too connected to fail” suggests that network connectedness plays an important role in measuring systemic risk. In this paper, we first recover a time series of Bayesian networks for stock returns, which allow the direction of links among stock returns to be formed with Markov properties in directed graphs. We rank the stocks in the time series of Bayesian networks based on the topological orders of the stocks in the learned Bayesian networks and develop an order distance, a new measure with which to assess the changes in the topological orders of the stocks. In an empirical study using stock data from the Hang Seng Index in Hong Kong and the Dow Jones Industrial Average, we use the order distance to predict the extreme absolute return, which is a proxy of extreme market risks, or a signal of systemic risks, using the LASSO regression model. Our results indicate that the network statistics of the time series of Bayesian networks and the order distance substantially improve the predictability of extreme absolute returns and provide insights into the assessment of systemic risk. |
format | Online Article Text |
id | pubmed-9858016 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-98580162023-01-21 A moving-window bayesian network model for assessing systemic risk in financial markets Chan, Lupe S. H. Chu, Amanda M. Y. So, Mike K. P. PLoS One Research Article Systemic risk refers to the uncertainty that arises due to the breakdown of a financial system. The concept of “too connected to fail” suggests that network connectedness plays an important role in measuring systemic risk. In this paper, we first recover a time series of Bayesian networks for stock returns, which allow the direction of links among stock returns to be formed with Markov properties in directed graphs. We rank the stocks in the time series of Bayesian networks based on the topological orders of the stocks in the learned Bayesian networks and develop an order distance, a new measure with which to assess the changes in the topological orders of the stocks. In an empirical study using stock data from the Hang Seng Index in Hong Kong and the Dow Jones Industrial Average, we use the order distance to predict the extreme absolute return, which is a proxy of extreme market risks, or a signal of systemic risks, using the LASSO regression model. Our results indicate that the network statistics of the time series of Bayesian networks and the order distance substantially improve the predictability of extreme absolute returns and provide insights into the assessment of systemic risk. Public Library of Science 2023-01-20 /pmc/articles/PMC9858016/ /pubmed/36662719 http://dx.doi.org/10.1371/journal.pone.0279888 Text en © 2023 Chan et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Chan, Lupe S. H. Chu, Amanda M. Y. So, Mike K. P. A moving-window bayesian network model for assessing systemic risk in financial markets |
title | A moving-window bayesian network model for assessing systemic risk in financial markets |
title_full | A moving-window bayesian network model for assessing systemic risk in financial markets |
title_fullStr | A moving-window bayesian network model for assessing systemic risk in financial markets |
title_full_unstemmed | A moving-window bayesian network model for assessing systemic risk in financial markets |
title_short | A moving-window bayesian network model for assessing systemic risk in financial markets |
title_sort | moving-window bayesian network model for assessing systemic risk in financial markets |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9858016/ https://www.ncbi.nlm.nih.gov/pubmed/36662719 http://dx.doi.org/10.1371/journal.pone.0279888 |
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