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

Jozef Baruník, Tobias Kley

Research output: Contribution to journalArticle (Academic Journal)peer-review

20 Citations (Scopus)
5 Downloads (Pure)

Abstract

In 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 which 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.
Original languageEnglish
Article numberutz002
Pages (from-to)131-152
JournalEconometrics Journal
Volume22
Issue number2
Early online date29 Jan 2019
DOIs
Publication statusPublished - 1 May 2019

Keywords

  • Cross-spectral analysis
  • Ranks
  • Copula
  • Stock market
  • Risk

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