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

TL;DR

This paper introduces quantile coherency as a novel frequency domain measure for capturing complex dependence structures in economic time series, especially useful for understanding tail risks in financial markets.

Contribution

It defines estimators for quantile coherency, analyzes their properties, and demonstrates their application in financial data analysis and model assessment.

Findings

Quantile coherency reveals dependence in tail regions of stock returns.

New insights into tail risk measurement in financial markets.

Methodology for inference on nonlinear economic processes.

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.