[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-31643":3,"doc-seo-31643":27},{"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":4,"is_deleted":4,"is_public":19,"is_downloadable":19,"audit_status":19,"page_count":20,"language":21,"language_code":22,"table_of_contents":23,"faqs":24,"seo_title":13,"seo_description":14,"update_tm":25,"read_time":26},31643,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","Forecasting and Stress Testing with Quantile Vector Autoregression","Forecasting and stress testing are addressed through a structural quantile vector autoregression (QVAR) framework. Unlike standard VAR models, QVAR traces interactions among endogenous variables at any chosen quantile, enabling quantile forecasts through a recursive factorization of the joint distribution. Structural quantile impulse response functions are derived to generalize VAR identification. The model is estimated with real and financial data for the euro area, revealing quantile-dependent dynamic properties and time-varying vulnerability ahead of Lehman’s default.","cbCaijLX4aZpGnbq","https://ap.wps.com/l/cbCaijLX4aZpGnbq","pdf",8491324,1,20,"English","en","# Introduction\n## Quantile VAR motivation and asymmetries\n# Structural Quantile VAR framework\n## Links to VAR and identification\n## Structural quantile impulse response functions\n## Multistep quantile forecasting\n# Empirical application\n## Euro area estimation and tail-risk implications","[{\"question\":\"How does a quantile VAR model differ from a standard VAR model for forecasting?\",\"answer\":\"Standard VAR models capture interactions with constant coefficients and often rely on Gaussian assumptions. The structural quantile VAR (QVAR) instead traces interactions at selected quantiles, producing quantile forecasts that reflect tail behavior via a recursive factorization of the joint distribution.\"},{\"question\":\"Why can’t quantile forecasts be obtained from reduced-form estimation in QVAR?\",\"answer\":\"In QVAR, the quantile of the sum of random variables is not generally equal to the sum of their quantiles. Therefore, reduced-form estimation does not deliver the required quantile structure, while recursive estimation aligns with the necessary joint-to-conditional decomposition for forecasting.\"},{\"question\":\"How is QVAR used for stress testing and what does the euro-area application show?\",\"answer\":\"QVAR provides a framework for macro stress testing by forecasting tail behavior under large financial and real shocks. Empirical results indicate that euro-area vulnerability to financial shocks varies over time, with months before Lehman’s default standing out as especially critical.\"}]",1779829317,50,{"code":4,"msg":28,"data":29},"ok",{"site_id":30,"language":22,"slug":31,"title":13,"keywords":32,"description":14,"schema_data":33,"social_meta":84,"head_meta":86,"extra_data":88,"updated_unix":25},105,"forecasting-and-stress-testing-with-quantile-vector-autoregression","",{"@graph":34,"@context":83},[35,52,66],{"@type":36,"itemListElement":37},"BreadcrumbList",[38,42,46,49],{"item":39,"name":40,"@type":41,"position":19},"https://docshare.wps.com","Home","ListItem",{"item":43,"name":44,"@type":41,"position":45},"https://docshare.wps.com/document/","Document",2,{"item":47,"name":12,"@type":41,"position":48},"https://docshare.wps.com/document/research-report/",3,{"item":50,"name":13,"@type":41,"position":51},"https://docshare.wps.com/document/forecasting-and-stress-testing-with-quantile-vector-autoregression/31643/",4,{"url":50,"name":13,"@type":53,"author":54,"headline":13,"publisher":56,"fileFormat":59,"description":14,"dateModified":60,"datePublished":60,"encodingFormat":59,"isAccessibleForFree":61,"interactionStatistic":62},"DigitalDocument",{"name":9,"@type":55},"Person",{"url":39,"name":57,"@type":58},"DocShare","Organization","application/pdf","2026-05-26",true,{"@type":63,"interactionType":64,"userInteractionCount":4},"InteractionCounter",{"@type":65},"ViewAction",{"@type":67,"mainEntity":68},"FAQPage",[69,75,79],{"name":70,"@type":71,"acceptedAnswer":72},"How does a quantile VAR model differ from a standard VAR model for forecasting?","Question",{"text":73,"@type":74},"Standard VAR models capture interactions with constant coefficients and often rely on Gaussian assumptions. The structural quantile VAR (QVAR) instead traces interactions at selected quantiles, producing quantile forecasts that reflect tail behavior via a recursive factorization of the joint distribution.","Answer",{"name":76,"@type":71,"acceptedAnswer":77},"Why can’t quantile forecasts be obtained from reduced-form estimation in QVAR?",{"text":78,"@type":74},"In QVAR, the quantile of the sum of random variables is not generally equal to the sum of their quantiles. Therefore, reduced-form estimation does not deliver the required quantile structure, while recursive estimation aligns with the necessary joint-to-conditional decomposition for forecasting.",{"name":80,"@type":71,"acceptedAnswer":81},"How is QVAR used for stress testing and what does the euro-area application show?",{"text":82,"@type":74},"QVAR provides a framework for macro stress testing by forecasting tail behavior under large financial and real shocks. Empirical results indicate that euro-area vulnerability to financial shocks varies over time, with months before Lehman’s default standing out as especially critical.","https://schema.org",{"og:url":50,"og:type":85,"og:title":13,"og:site_name":57,"og:description":14},"article",{"robots":87,"canonical":50},"index,follow",{"doc_id":7,"site_id":30}]