Disentangling global equity market instability: a network analysis
During a financial crisis, the capital markets network frequently exhibits a high correlation between returns. We developed a network analysis framework based on daily returns from 42 countries to determine systemic stability. Our network is built using the conditional probability of co-movement of...
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| creator | Kamtue, Supanat Luangaram, Pongsak Woramongkhon, Sirawit |
| description | During a financial crisis, the capital markets network frequently exhibits a
high correlation between returns. We developed a network analysis framework
based on daily returns from 42 countries to determine systemic stability. Our
network is built using the conditional probability of co-movement of returns,
and it identifies nodes, network complexity, and edge as potential sources of
fragility. We also introduce the concept of measuring flows from one return to
another. Then, we use 120-day rolling data to capture the financial system's
behavior and create a financial stability indicator. We discover that the
contributions of nodes and network complexity to changes in system stability
frequently cancel each other out. Edge change may be a determinant of systemic
stability. Furthermore, the total flows in the network are highly correlated
with the volatility. It main advantage is the tractability and potential
sources of volatility can be determined. |
| doi_str_mv | 10.48550/arxiv.2111.14227 |
| format | Article |
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high correlation between returns. We developed a network analysis framework
based on daily returns from 42 countries to determine systemic stability. Our
network is built using the conditional probability of co-movement of returns,
and it identifies nodes, network complexity, and edge as potential sources of
fragility. We also introduce the concept of measuring flows from one return to
another. Then, we use 120-day rolling data to capture the financial system's
behavior and create a financial stability indicator. We discover that the
contributions of nodes and network complexity to changes in system stability
frequently cancel each other out. Edge change may be a determinant of systemic
stability. Furthermore, the total flows in the network are highly correlated
with the volatility. It main advantage is the tractability and potential
sources of volatility can be determined.</description><identifier>DOI: 10.48550/arxiv.2111.14227</identifier><language>eng</language><subject>Mathematics - Dynamical Systems</subject><creationdate>2021-11</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</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/2111.14227$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2111.14227$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Kamtue, Supanat</creatorcontrib><creatorcontrib>Luangaram, Pongsak</creatorcontrib><creatorcontrib>Woramongkhon, Sirawit</creatorcontrib><title>Disentangling global equity market instability: a network analysis</title><description>During a financial crisis, the capital markets network frequently exhibits a
high correlation between returns. We developed a network analysis framework
based on daily returns from 42 countries to determine systemic stability. Our
network is built using the conditional probability of co-movement of returns,
and it identifies nodes, network complexity, and edge as potential sources of
fragility. We also introduce the concept of measuring flows from one return to
another. Then, we use 120-day rolling data to capture the financial system's
behavior and create a financial stability indicator. We discover that the
contributions of nodes and network complexity to changes in system stability
frequently cancel each other out. Edge change may be a determinant of systemic
stability. Furthermore, the total flows in the network are highly correlated
with the volatility. It main advantage is the tractability and potential
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high correlation between returns. We developed a network analysis framework
based on daily returns from 42 countries to determine systemic stability. Our
network is built using the conditional probability of co-movement of returns,
and it identifies nodes, network complexity, and edge as potential sources of
fragility. We also introduce the concept of measuring flows from one return to
another. Then, we use 120-day rolling data to capture the financial system's
behavior and create a financial stability indicator. We discover that the
contributions of nodes and network complexity to changes in system stability
frequently cancel each other out. Edge change may be a determinant of systemic
stability. Furthermore, the total flows in the network are highly correlated
with the volatility. It main advantage is the tractability and potential
sources of volatility can be determined.</abstract><doi>10.48550/arxiv.2111.14227</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Mathematics - Dynamical Systems |
| title | Disentangling global equity market instability: a network analysis |
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