Authors: Baruník, Jozef
Kley, Tobias
Title: Quantile cross-spectral measures of dependence between economic variables
Language (ISO): en
Abstract: In this paper we introduce quantile cross-spectral analysis of multiple time series which is designed to detect general dependence structures emerging in quantiles of the joint distribution in the frequency domain. We argue that this type of dependence is natural for economic time series but remains invisible when the traditional analysis is employed. To illustrate how such dependence structures can arise between variables in different parts of the joint distribution and across frequencies, we consider quantile vector autoregression processes. We define new 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 one of the most prominent time series in economics and shed new light on the dependence of bivariate stock market returns.
Subject Headings: cross-spectral density
copula
ranks
time series
dependence
quantiles
URI: http://hdl.handle.net/2003/34316
http://dx.doi.org/10.17877/DE290R-16393
Issue Date: 2015-10-23
Appears in Collections:Sonderforschungsbereich (SFB) 823

Files in This Item:
File Description SizeFormat 
DP_4315_SFB 823_Barunik_Kley.pdfDNB1.11 MBAdobe PDFView/Open


This item is protected by original copyright



All resources in the repository are protected by copyright.