Causal Hierarchy in the Financial Market Network -- Uncovered by the Helmholtz-Hodge-Kodaira Decomposition
Granger causality can uncover the cause and effect relationships in financial networks. However, such networks can be convoluted and difficult to interpret, but the Helmholtz-Hodge-Kodaira decomposition can split them into a rotational and gradient component which reveals the hierarchy of Granger ca...
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creator | Wand, Tobias Kamps, Oliver Iyetomi, Hiroshi |
description | Granger causality can uncover the cause and effect relationships in financial
networks. However, such networks can be convoluted and difficult to interpret,
but the Helmholtz-Hodge-Kodaira decomposition can split them into a rotational
and gradient component which reveals the hierarchy of Granger causality flow.
Using Kenneth French's business sector return time series, it is revealed that
during the Covid crisis, precious metals and pharmaceutical products are causal
drivers of the financial network. Moreover, the estimated Granger causality
network shows a high connectivity during crisis which means that the research
presented here can be especially useful to better understand crises in the
market by revealing the dominant drivers of the crisis dynamics. |
doi_str_mv | 10.48550/arxiv.2408.12839 |
format | Article |
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networks. However, such networks can be convoluted and difficult to interpret,
but the Helmholtz-Hodge-Kodaira decomposition can split them into a rotational
and gradient component which reveals the hierarchy of Granger causality flow.
Using Kenneth French's business sector return time series, it is revealed that
during the Covid crisis, precious metals and pharmaceutical products are causal
drivers of the financial network. Moreover, the estimated Granger causality
network shows a high connectivity during crisis which means that the research
presented here can be especially useful to better understand crises in the
market by revealing the dominant drivers of the crisis dynamics.</description><identifier>DOI: 10.48550/arxiv.2408.12839</identifier><language>eng</language><subject>Physics - Data Analysis, Statistics and Probability ; Quantitative Finance - Statistical Finance ; Statistics - Applications</subject><creationdate>2024-08</creationdate><rights>http://creativecommons.org/licenses/by/4.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,776,881</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2408.12839$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2408.12839$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Wand, Tobias</creatorcontrib><creatorcontrib>Kamps, Oliver</creatorcontrib><creatorcontrib>Iyetomi, Hiroshi</creatorcontrib><title>Causal Hierarchy in the Financial Market Network -- Uncovered by the Helmholtz-Hodge-Kodaira Decomposition</title><description>Granger causality can uncover the cause and effect relationships in financial
networks. However, such networks can be convoluted and difficult to interpret,
but the Helmholtz-Hodge-Kodaira decomposition can split them into a rotational
and gradient component which reveals the hierarchy of Granger causality flow.
Using Kenneth French's business sector return time series, it is revealed that
during the Covid crisis, precious metals and pharmaceutical products are causal
drivers of the financial network. Moreover, the estimated Granger causality
network shows a high connectivity during crisis which means that the research
presented here can be especially useful to better understand crises in the
market by revealing the dominant drivers of the crisis dynamics.</description><subject>Physics - Data Analysis, Statistics and Probability</subject><subject>Quantitative Finance - Statistical Finance</subject><subject>Statistics - Applications</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNqFzrsOgkAQheFtLIz6AFbOCyyiaIK1SkiMVlqTEUYZhV0yrBd8epXYW53i_MWn1HDie7NwPvfHKE--e9OZH3qTaRgsuuqyxFuNBcRMgpLmDbABlxNEbNCk_Lm2KFdysCP3sHIFreFgUnsnoQyOTRvHVJS5LdxLxzY7k97YDFkQVpTasrI1O7amrzonLGoa_LanRtF6v4x1q0oq4RKlSb66pNUF_4s359VFoQ</recordid><startdate>20240823</startdate><enddate>20240823</enddate><creator>Wand, Tobias</creator><creator>Kamps, Oliver</creator><creator>Iyetomi, Hiroshi</creator><scope>EPD</scope><scope>GOX</scope></search><sort><creationdate>20240823</creationdate><title>Causal Hierarchy in the Financial Market Network -- Uncovered by the Helmholtz-Hodge-Kodaira Decomposition</title><author>Wand, Tobias ; Kamps, Oliver ; Iyetomi, Hiroshi</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-arxiv_primary_2408_128393</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Physics - Data Analysis, Statistics and Probability</topic><topic>Quantitative Finance - Statistical Finance</topic><topic>Statistics - Applications</topic><toplevel>online_resources</toplevel><creatorcontrib>Wand, Tobias</creatorcontrib><creatorcontrib>Kamps, Oliver</creatorcontrib><creatorcontrib>Iyetomi, Hiroshi</creatorcontrib><collection>arXiv Statistics</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Wand, Tobias</au><au>Kamps, Oliver</au><au>Iyetomi, Hiroshi</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Causal Hierarchy in the Financial Market Network -- Uncovered by the Helmholtz-Hodge-Kodaira Decomposition</atitle><date>2024-08-23</date><risdate>2024</risdate><abstract>Granger causality can uncover the cause and effect relationships in financial
networks. However, such networks can be convoluted and difficult to interpret,
but the Helmholtz-Hodge-Kodaira decomposition can split them into a rotational
and gradient component which reveals the hierarchy of Granger causality flow.
Using Kenneth French's business sector return time series, it is revealed that
during the Covid crisis, precious metals and pharmaceutical products are causal
drivers of the financial network. Moreover, the estimated Granger causality
network shows a high connectivity during crisis which means that the research
presented here can be especially useful to better understand crises in the
market by revealing the dominant drivers of the crisis dynamics.</abstract><doi>10.48550/arxiv.2408.12839</doi><oa>free_for_read</oa></addata></record> |
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subjects | Physics - Data Analysis, Statistics and Probability Quantitative Finance - Statistical Finance Statistics - Applications |
title | Causal Hierarchy in the Financial Market Network -- Uncovered by the Helmholtz-Hodge-Kodaira Decomposition |
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