The Cross-Quantilogram: Measuring Quantile Dependence and Testing Directional Predictability between Time Series

By Heejoon Han, Oliver B. Linton, Tatsushi Oka, Yoon-Jae Whang. 2014

502 citations recorded by the source corpus.

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.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/25fcf236d9a9e2e5d0dcd197ec510ebedd95fcaa

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Citations in the corpus

Frequency connectedness and cross-quantile dependence between green bond and green equity markets (2021, methodology): “To control for the effect of uncertainties within the cross-quantilogram framework, we follow Han et al. (2016) and compute partial cross-quantilograms (PCQ) between green bond and green equity.”
Searching for safe-haven assets during the COVID-19 pandemic (2020, unknown): “Under the null hypothesis, the asymptotic limit ofWn(s) is provided by Hoga and Wied (2017) that, Q. Ji, et al. International Review of Financial Analysis 71 (2020) 101526”
Quantile coherency: A general measure for dependence between cyclical economic variables (2015, unknown): “For the computation we used the estimator and stationary bootstrap procedure defined in Han et al. (2016). More precisely, we used the implementation that is available in the R package quantilogram; cf.”
Quantile spectral analysis for locally stationary time series (2014, methodology): “Pioneering contributions in that direction are Hong (1999) and Li (2008), who coined the names of Laplace spectrum and Laplace periodogram.”
Quantile Spectral Processes: Asymptotic Analysis and Inference (2014, unknown): “As shown by Dette et al. [12], the copula spectral densities provide a complete description of the pairwise copulas of a time series.”