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

Heejoon Han, Oliver Linton, Tatsushi Oka and Yoon-Jae Whang

No CWP06/14, CeMMAP working papers from Centre for Microdata Methods and Practice, Institute for Fiscal Studies

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 confi?dence 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 fi?nancial institutions, such as JP Morgan Chase, Goldman Sachs and AIG. This article has supplementary materials online.

Date: 2014-02-20
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-rmg
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Citations: View citations in EconPapers (6)

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Related works:
Journal Article: The cross-quantilogram: Measuring quantile dependence and testing directional predictability between time series (2016)
Working Paper: The cross-quantilogram: measuring quantile dependence and testing directional predictability between time series (2014)
Working Paper: The Cross-Quantilogram: Measuring Quantile Dependence and Testing Directional Predictability between Time Series (2014)
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