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2016-11-23Diskussionpapier DOI: 10.18452/18426
Network Quantile Autoregression
dc.contributor.authorZhu, Xuening
dc.contributor.authorWang, Weining
dc.contributor.authorWang, Hangsheng
dc.contributor.authorHärdle, Wolfgang K.
dc.date.accessioned2017-10-02T09:29:02Z
dc.date.available2017-10-02T09:29:02Z
dc.date.issued2016-11-23
dc.identifier.urihttp://edoc.hu-berlin.de/18452/19103
dc.description.abstractIt is a challenging task to understand the complex dependency structures in an ultra-high dimensional network, especially when one concentrates on the tail dependency. To tackle this problem, we consider a network quantile autoregres- sion model (NQAR) to characterize the dynamic quantile behavior in a complex system. In particular, we relate responses to its connected nodes and node specific characteristics in a quantile autoregression process. A minimum contrast estimation approach for the NQAR model is introduced, and the asymptotic properties are studied. Finally, we demonstrate the usage of our model by in- vestigating the financial contagions in the Chinese stock market accounting for shared ownership of companies.eng
dc.language.isoeng
dc.publisherHumboldt-Universität zu Berlin
dc.subjectSocial Networkeng
dc.subjectQuantile Regressioneng
dc.subjectAutoregressioneng
dc.subjectSystemic Riskeng
dc.subjectFinancial Contagioneng
dc.subjectShared Ownershipeng
dc.subject.ddc330 Wirtschaft
dc.titleNetwork Quantile Autoregression
dc.typeworkingPaper
dc.identifier.urnurn:nbn:de:kobv:11-110-18452/19103-2
dc.identifier.doihttp://dx.doi.org/10.18452/18426
local.edoc.container-titleSonderforschungsbereich 649: Ökonomisches Risiko
local.edoc.pages56
local.edoc.type-nameDiskussionpapier
local.edoc.institutionWirtschaftswissenschaftliche Fakultät
local.edoc.container-typeseries
local.edoc.container-type-nameSchriftenreihe
local.edoc.container-volume2016
local.edoc.container-issue50
local.edoc.container-erstkatid2195055-6

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