Rethinking Quantile-on-Quantile Connectedness

Giovanni Bonaccolto (), Massimiliano Caporin () and Rangan Gupta
Additional contact information
Giovanni Bonaccolto: Department of Economics and Law, ``Kore" University of Enna, Piazza dell'Universita, 94100 Enna, Italy
Massimiliano Caporin: Department of Statistical Sciences, University of Padova, Via Cesare Battisti 241/243, Padova, Italy

No 202629, Working Papers from University of Pretoria, Department of Economics

Abstract: We revisit Quantile-on-Quantile connectedness and argue that estimating the equations of a Quantile Vector AutoRegressive model at different quantile levels does not fully capture the conventional Quantile-on-Quantile mechanism. We propose a local, kernel-weighted QVAR approach that explicitly accounts for the quantile location of the conditioning variable. An empirical comparison using U.S. Treasury yields shows that this modification substantially affects estimated connectedness, producing a more homogeneous pattern across quantile pairs and markedly attenuating the differences between directly and reversely related quantiles.

Keywords: Quantile-on-Quantile; Connectedness; Quantile VAR; Spillovers; Quantile Regression (search for similar items in EconPapers)
JEL-codes: C22 C32 (search for similar items in EconPapers)
Pages: 15 pages
Date: 2026-09
New Economics Papers: this item is included in nep-ets and nep-rmg
References: Add references at CitEc
Citations:

Downloads: (external link)
https://drupalwebprod-files.up.ac.za/Public/2026-0 ... nvVC1fTlCpBDeMwlijtZ (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:pre:wpaper:202629

Access Statistics for this paper

More papers in Working Papers from University of Pretoria, Department of Economics Contact information at EDIRC.
Bibliographic data for series maintained by Rangan Gupta ().