Quantile Coherency: A General Measure for Dependence between Cyclical Economic Variables

53 Pages Posted: 24 Oct 2015 Last revised: 30 Dec 2018

See all articles by Jozef Baruník

Jozef Baruník

Charles University in Prague - Department of Economics; Institute of Information Theory and Automation, Prague

Tobias Kley

London School of Economics & Political Science (LSE) - Department of Statistics

Date Written: October 23, 2015

Abstract

In this paper, we introduce quantile coherency to measure general de- pendence 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 which capture the general dependence structure, provide a detailed analysis of their asymp- totic 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

Suggested Citation

Barunik, Jozef and Kley, Tobias, Quantile Coherency: A General Measure for Dependence between Cyclical Economic Variables (October 23, 2015). Available at SSRN: https://ssrn.com/abstract=2678977 or http://dx.doi.org/10.2139/ssrn.2678977

Jozef Barunik (Contact Author)

Charles University in Prague - Department of Economics ( email )

Opletalova 26
Prague 1, 110 00
Czech Republic

HOME PAGE: http://ies.fsv.cuni.cz/en/staff/barunik

Institute of Information Theory and Automation, Prague ( email )

Pod vodarenskou vezi 4
CZ-18208 Praha 8
Czech Republic

HOME PAGE: http://staff.utia.cas.cz/barunik/home.htm

Tobias Kley

London School of Economics & Political Science (LSE) - Department of Statistics ( email )

Houghton Street
London, England WC2A 2AE
United Kingdom

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