Publication:
The cross-quantilogram: Measuring quantile dependence and testing directional predictability between time series

dc.contributor.author Linton, Oliver
dc.contributor.author.lku.lk Han, Heejoon
dc.contributor.author.lku.lk Oka, Tatsushi
dc.contributor.author.lku.lk Whang, Yoon-Jae
dc.contributor.other Systemic Physiological and Ecotoxicological Research (SPHERE)
dc.date.accessioned 2019-04-26T08:57:25Z
dc.date.available 2019-04-26T08:57:25Z
dc.date.issued 30/03/16
dc.description This paper proposes the cross-quantilogram to measure the quantile dependence between two time series. We apply it to test the hypothesis that one time series has no directional predictability to another time series. We establish the asymptotic distribution of the cross-quantilogram and the corresponding test statistic. The limiting distributions depend on nuisance parameters. To construct consistent confidence intervals we employ a stationary bootstrap procedure; we establish consistency of this bootstrap. Also, we consider a self-normalized approach, which yields an asymptotically pivotal statistic under the null hypothesis of no predictability. We provide simulation studies and two empirical applications. First, we use the cross-quantilogram to detect predictability from stock variance to excess stock return. Compared to existing tools used in the literature of stock return predictability, our method provides a more complete relationship between a predictor and stock return. Second, we investigate the systemic risk of individual financial institutions, such as JP Morgan Chase, Morgan Stanley and AIG.
dc.identifier.uri https://demo.dspace.org/handle/atmire/471
dc.language en
dc.publisher Elsevier
dc.title The cross-quantilogram: Measuring quantile dependence and testing directional predictability between time series
dspace.entity.type Publication
relation.isAuthorOfPublication 5f12fcc9-4b54-4f60-ac0f-445636c16257
relation.isOrgUnitOfPublication b30c55a2-f5de-4cf5-af45-ce97522c236d
relation.isProjectOfPublication 76a6476d-7ee6-475b-b4fb-978db1587764
relation.isProjectOfPublication 5c163a7d-63dd-4533-b74a-28455f732adb
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