Quantile coherency: A general measure for dependence between cyclical economic variables

Jozef Baruník and Tobias Kley
Authors registered in the RePEc Author Service: Jozef Baruník

The Econometrics Journal, 2019, vol. 22, issue 2, 131-152

Abstract: SummaryIn this paper, we introduce quantile coherency to measure general dependence structures emerging in the joint distribution in the frequency domain and argue that this type of dependence is natural for economic time series but remains invisible when only the traditional analysis is employed. We define estimators that capture the general dependence structure, provide a detailed analysis of their asymptotic properties, and discuss how to conduct inference for a general class of possibly nonlinear processes. In an empirical illustration we examine the dependence of bivariate stock market returns and shed new light on measurement of tail risk in financial markets. We also provide a modelling exercise to illustrate how applied researchers can benefit from using quantile coherency when assessing time series models.

Keywords: Cross-spectral analysis; ranks; copula; stock market; risk (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (103)

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Working Paper: Quantile Coherency: A General Measure for Dependence between Cyclical Economic Variables (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:oup:emjrnl:v:22:y:2019:i:2:p:131-152.

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