Loading…
Network quantile autoregression
The complex tail dependency structure in a dynamic network with a large number of nodes is an important object to study. We propose a network quantile autoregression model (NQAR), which characterizes the dynamic quantile behavior. Our NQAR model consists of a system of equations, of which we relate...
Saved in:
| Published in: | Journal of econometrics 2019-09, Vol.212 (1), p.345-358 |
|---|---|
| Main Authors: | , , , |
| Format: | Article |
| Language: | English |
| Subjects: | |
| Citations: | Items that this one cites Items that cite this one |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| cited_by | cdi_FETCH-LOGICAL-c449t-4a5f8a3377e30d492743ec6818adb8ebba0d8faa2e2f7288cb12b288c6bedd293 |
|---|---|
| cites | cdi_FETCH-LOGICAL-c449t-4a5f8a3377e30d492743ec6818adb8ebba0d8faa2e2f7288cb12b288c6bedd293 |
| container_end_page | 358 |
| container_issue | 1 |
| container_start_page | 345 |
| container_title | Journal of econometrics |
| container_volume | 212 |
| creator | Zhu, Xuening Wang, Weining Wang, Hansheng Härdle, Wolfgang Karl |
| description | The complex tail dependency structure in a dynamic network with a large number of nodes is an important object to study. We propose a network quantile autoregression model (NQAR), which characterizes the dynamic quantile behavior. Our NQAR model consists of a system of equations, of which we relate a response to its connected nodes and node specific characteristics in a quantile autoregression process. We show the estimation of the NQAR model and the asymptotic properties with assumptions on the network structure. For this propose we develop a network Bahadur representation that gives us direct insight into the parameter asymptotics. Moreover, innovative tail-event driven impulse functions are defined. Finally, we demonstrate the usage of our model by investigating the financial contagions in the Chinese stock market accounting for shared ownership of companies. We find higher network dependency when the market is exposed to a higher volatility level.
▪www.quantlet.de |
| doi_str_mv | 10.1016/j.jeconom.2019.04.034 |
| format | article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2301460606</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S0304407619300892</els_id><sourcerecordid>2301460606</sourcerecordid><originalsourceid>FETCH-LOGICAL-c449t-4a5f8a3377e30d492743ec6818adb8ebba0d8faa2e2f7288cb12b288c6bedd293</originalsourceid><addsrcrecordid>eNqFkMtKxDAUhoMoWEcfQRxw3XpyaZuuRAZHhUE3ug5pciqpM81M0iq-vS2dvZzFv_kvnI-QawoZBVrctVmLxnd-lzGgVQYiAy5OSEJlydJCVvkpSYCDSAWUxTm5iLEFgFxInpCbV-x_fPhaHgbd9W6LSz30PuBnwBid7y7JWaO3Ea-OuiAf68f31XO6eXt6WT1sUiNE1adC543UnJclcrCiYqXgaApJpba1xLrWYGWjNUPWlExKU1NWT1rUaC2r-ILczr374A8Dxl61fgjdOKkYByoKGG905bPLBB9jwEbtg9vp8KsoqImFatWRhZpYKBBqZDHm7uccji98OwwqGoedQesCml5Z7_5p-ANq-GoT</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2301460606</pqid></control><display><type>article</type><title>Network quantile autoregression</title><source>International Bibliography of the Social Sciences (IBSS)</source><source>Backfile Package - Economics, Econometrics and Finance (Legacy) [YET]</source><source>ScienceDirect Journals</source><source>Backfile Package - Mathematics (Legacy) [YMT]</source><creator>Zhu, Xuening ; Wang, Weining ; Wang, Hansheng ; Härdle, Wolfgang Karl</creator><creatorcontrib>Zhu, Xuening ; Wang, Weining ; Wang, Hansheng ; Härdle, Wolfgang Karl</creatorcontrib><description>The complex tail dependency structure in a dynamic network with a large number of nodes is an important object to study. We propose a network quantile autoregression model (NQAR), which characterizes the dynamic quantile behavior. Our NQAR model consists of a system of equations, of which we relate a response to its connected nodes and node specific characteristics in a quantile autoregression process. We show the estimation of the NQAR model and the asymptotic properties with assumptions on the network structure. For this propose we develop a network Bahadur representation that gives us direct insight into the parameter asymptotics. Moreover, innovative tail-event driven impulse functions are defined. Finally, we demonstrate the usage of our model by investigating the financial contagions in the Chinese stock market accounting for shared ownership of companies. We find higher network dependency when the market is exposed to a higher volatility level.
▪www.quantlet.de</description><identifier>ISSN: 0304-4076</identifier><identifier>EISSN: 1872-6895</identifier><identifier>DOI: 10.1016/j.jeconom.2019.04.034</identifier><language>eng</language><publisher>Amsterdam: Elsevier B.V</publisher><subject>Autoregression ; Dependency ; Estimating techniques ; Financial contagion ; Ownership ; Quantile regression ; Regression analysis ; Securities markets ; Shared ownership ; Social network ; Systemic risk ; Volatility</subject><ispartof>Journal of econometrics, 2019-09, Vol.212 (1), p.345-358</ispartof><rights>2019</rights><rights>Copyright Elsevier Sequoia S.A. Sep 2019</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c449t-4a5f8a3377e30d492743ec6818adb8ebba0d8faa2e2f7288cb12b288c6bedd293</citedby><cites>FETCH-LOGICAL-c449t-4a5f8a3377e30d492743ec6818adb8ebba0d8faa2e2f7288cb12b288c6bedd293</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S0304407619300892$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3460,3564,27924,27925,33223,45992,46003</link.rule.ids></links><search><creatorcontrib>Zhu, Xuening</creatorcontrib><creatorcontrib>Wang, Weining</creatorcontrib><creatorcontrib>Wang, Hansheng</creatorcontrib><creatorcontrib>Härdle, Wolfgang Karl</creatorcontrib><title>Network quantile autoregression</title><title>Journal of econometrics</title><description>The complex tail dependency structure in a dynamic network with a large number of nodes is an important object to study. We propose a network quantile autoregression model (NQAR), which characterizes the dynamic quantile behavior. Our NQAR model consists of a system of equations, of which we relate a response to its connected nodes and node specific characteristics in a quantile autoregression process. We show the estimation of the NQAR model and the asymptotic properties with assumptions on the network structure. For this propose we develop a network Bahadur representation that gives us direct insight into the parameter asymptotics. Moreover, innovative tail-event driven impulse functions are defined. Finally, we demonstrate the usage of our model by investigating the financial contagions in the Chinese stock market accounting for shared ownership of companies. We find higher network dependency when the market is exposed to a higher volatility level.
▪www.quantlet.de</description><subject>Autoregression</subject><subject>Dependency</subject><subject>Estimating techniques</subject><subject>Financial contagion</subject><subject>Ownership</subject><subject>Quantile regression</subject><subject>Regression analysis</subject><subject>Securities markets</subject><subject>Shared ownership</subject><subject>Social network</subject><subject>Systemic risk</subject><subject>Volatility</subject><issn>0304-4076</issn><issn>1872-6895</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>8BJ</sourceid><recordid>eNqFkMtKxDAUhoMoWEcfQRxw3XpyaZuuRAZHhUE3ug5pciqpM81M0iq-vS2dvZzFv_kvnI-QawoZBVrctVmLxnd-lzGgVQYiAy5OSEJlydJCVvkpSYCDSAWUxTm5iLEFgFxInpCbV-x_fPhaHgbd9W6LSz30PuBnwBid7y7JWaO3Ea-OuiAf68f31XO6eXt6WT1sUiNE1adC543UnJclcrCiYqXgaApJpba1xLrWYGWjNUPWlExKU1NWT1rUaC2r-ILczr374A8Dxl61fgjdOKkYByoKGG905bPLBB9jwEbtg9vp8KsoqImFatWRhZpYKBBqZDHm7uccji98OwwqGoedQesCml5Z7_5p-ANq-GoT</recordid><startdate>20190901</startdate><enddate>20190901</enddate><creator>Zhu, Xuening</creator><creator>Wang, Weining</creator><creator>Wang, Hansheng</creator><creator>Härdle, Wolfgang Karl</creator><general>Elsevier B.V</general><general>Elsevier Sequoia S.A</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8BJ</scope><scope>FQK</scope><scope>JBE</scope></search><sort><creationdate>20190901</creationdate><title>Network quantile autoregression</title><author>Zhu, Xuening ; Wang, Weining ; Wang, Hansheng ; Härdle, Wolfgang Karl</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c449t-4a5f8a3377e30d492743ec6818adb8ebba0d8faa2e2f7288cb12b288c6bedd293</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Autoregression</topic><topic>Dependency</topic><topic>Estimating techniques</topic><topic>Financial contagion</topic><topic>Ownership</topic><topic>Quantile regression</topic><topic>Regression analysis</topic><topic>Securities markets</topic><topic>Shared ownership</topic><topic>Social network</topic><topic>Systemic risk</topic><topic>Volatility</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhu, Xuening</creatorcontrib><creatorcontrib>Wang, Weining</creatorcontrib><creatorcontrib>Wang, Hansheng</creatorcontrib><creatorcontrib>Härdle, Wolfgang Karl</creatorcontrib><collection>CrossRef</collection><collection>International Bibliography of the Social Sciences (IBSS)</collection><collection>International Bibliography of the Social Sciences</collection><collection>International Bibliography of the Social Sciences</collection><jtitle>Journal of econometrics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhu, Xuening</au><au>Wang, Weining</au><au>Wang, Hansheng</au><au>Härdle, Wolfgang Karl</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Network quantile autoregression</atitle><jtitle>Journal of econometrics</jtitle><date>2019-09-01</date><risdate>2019</risdate><volume>212</volume><issue>1</issue><spage>345</spage><epage>358</epage><pages>345-358</pages><issn>0304-4076</issn><eissn>1872-6895</eissn><abstract>The complex tail dependency structure in a dynamic network with a large number of nodes is an important object to study. We propose a network quantile autoregression model (NQAR), which characterizes the dynamic quantile behavior. Our NQAR model consists of a system of equations, of which we relate a response to its connected nodes and node specific characteristics in a quantile autoregression process. We show the estimation of the NQAR model and the asymptotic properties with assumptions on the network structure. For this propose we develop a network Bahadur representation that gives us direct insight into the parameter asymptotics. Moreover, innovative tail-event driven impulse functions are defined. Finally, we demonstrate the usage of our model by investigating the financial contagions in the Chinese stock market accounting for shared ownership of companies. We find higher network dependency when the market is exposed to a higher volatility level.
▪www.quantlet.de</abstract><cop>Amsterdam</cop><pub>Elsevier B.V</pub><doi>10.1016/j.jeconom.2019.04.034</doi><tpages>14</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0304-4076 |
| ispartof | Journal of econometrics, 2019-09, Vol.212 (1), p.345-358 |
| issn | 0304-4076 1872-6895 |
| language | eng |
| recordid | cdi_proquest_journals_2301460606 |
| source | International Bibliography of the Social Sciences (IBSS); Backfile Package - Economics, Econometrics and Finance (Legacy) [YET]; ScienceDirect Journals; Backfile Package - Mathematics (Legacy) [YMT] |
| subjects | Autoregression Dependency Estimating techniques Financial contagion Ownership Quantile regression Regression analysis Securities markets Shared ownership Social network Systemic risk Volatility |
| title | Network quantile autoregression |
| url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-08T02%3A35%3A54IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Network%20quantile%20autoregression&rft.jtitle=Journal%20of%20econometrics&rft.au=Zhu,%20Xuening&rft.date=2019-09-01&rft.volume=212&rft.issue=1&rft.spage=345&rft.epage=358&rft.pages=345-358&rft.issn=0304-4076&rft.eissn=1872-6895&rft_id=info:doi/10.1016/j.jeconom.2019.04.034&rft_dat=%3Cproquest_cross%3E2301460606%3C/proquest_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c449t-4a5f8a3377e30d492743ec6818adb8ebba0d8faa2e2f7288cb12b288c6bedd293%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2301460606&rft_id=info:pmid/&rfr_iscdi=true |