Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/97381
Authors: 
Han, Heejoon
Linton, Oliver
Oka, Tatsushi
Whang, Yoon-Jae
Year of Publication: 
2014
Series/Report no.: 
cemmap working paper, Centre for Microdata Methods and Practice CWP06/14
Abstract: 
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 the stationary bootstrap procedure; we show the consistency of this bootstrap. Also, we consider the self-normalized approach, which is shown to be asymptotically pivotal 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, Goldman Sachs and AIG. This article has supplementary materials online.
Subjects: 
Quantile
Correlogram
Dependence
Predictability
Systemic risk
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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