[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-32103":3,"doc-seo-32103":28},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"file_id":15,"file_url":16,"file_type":17,"file_size":18,"view_count":19,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"table_of_contents":24,"faqs":25,"seo_title":13,"seo_description":14,"update_tm":26,"read_time":27},32103,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","Quantile Connectedness Modeling Tail Behavior in the Topology of Financial Networks","Quantile Connectedness develops an estimation technique for vector autoregressions using quantile regression, combined with a factor structure to eliminate cross-sectional residual correlation. The equation-by-equation setup enables estimation via standard quantile regression toolboxes. Applied to credit risk spillovers across 18 sovereigns and their financial sectors from January 2006 to February 2012, the model finds weak median propagation from idiosyncratic shocks, but strong spillovers in both tails. Rolling estimates also show time-varying tail dependence, which mean-based conditional methods miss.","cbCaijH9fTAbSRw9","https://ap.wps.com/l/cbCaijH9fTAbSRw9","pdf",6907228,6,1,48,"English","en","# Introduction\n## Network topology and systemic risk\n## Quantile-based framework and motivation\n## Relation to Diebold–Yilmaz connectedness measures","[{\"question\":\"What problem does Quantile Connectedness address in financial network analysis?\",\"answer\":\"It studies whether network topology changes with the size of shocks, since large systemic shocks may not propagate like average shocks assumed by conditional mean estimators.\"},{\"question\":\"How does the proposed method estimate quantile vector autoregressions?\",\"answer\":\"It uses quantile regression with a factor structure to remove cross-section correlation in residuals, allowing estimation equation-by-equation with existing quantile regression toolboxes.\"},{\"question\":\"What do the results show about credit risk spillovers in the median versus the tails?\",\"answer\":\"Idiosyncratic credit risk shocks propagate weakly at the median, while powerful spillovers appear in both tails.\"},{\"question\":\"Why is rolling sample analysis important in this study?\",\"answer\":\"It reveals time-varying tail dependence, highlighting dynamic features of credit risk transmission that conditional mean models obscure.\"}]",1780866177,121,{"code":4,"msg":29,"data":30},"ok",{"site_id":31,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":90,"head_meta":92,"extra_data":94,"updated_unix":26},105,"quantile-connectedness-modeling-tail-behavior-in-the-topology-of-financial-networks","",{"@graph":35,"@context":89},[36,53,68],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/quantile-connectedness-modeling-tail-behavior-in-the-topology-of-financial-networks/32103/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-06-13","2026-06-07",true,{"@type":65,"interactionType":66,"userInteractionCount":19},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81,85],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does Quantile Connectedness address in financial network analysis?","Question",{"text":75,"@type":76},"It studies whether network topology changes with the size of shocks, since large systemic shocks may not propagate like average shocks assumed by conditional mean estimators.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed method estimate quantile vector autoregressions?",{"text":80,"@type":76},"It uses quantile regression with a factor structure to remove cross-section correlation in residuals, allowing estimation equation-by-equation with existing quantile regression toolboxes.",{"name":82,"@type":73,"acceptedAnswer":83},"What do the results show about credit risk spillovers in the median versus the tails?",{"text":84,"@type":76},"Idiosyncratic credit risk shocks propagate weakly at the median, while powerful spillovers appear in both tails.",{"name":86,"@type":73,"acceptedAnswer":87},"Why is rolling sample analysis important in this study?",{"text":88,"@type":76},"It reveals time-varying tail dependence, highlighting dynamic features of credit risk transmission that conditional mean models obscure.","https://schema.org",{"og:url":51,"og:type":91,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":93,"canonical":51},"index,follow",{"doc_id":7,"site_id":31}]