Robust Price Discovery to Heavy-Tailed Market Shocks

Jaeho Kim, Scott C. Linn and Sora Chon
Additional contact information
Jaeho Kim: Sogang University
Scott C. Linn: University of Oklahoma
Sora Chon: Inha University

No 2025-1, Inha University IBER Working Paper Series from Inha University, Institute of Business and Economic Research

Abstract: We show that conclusions drawn from widely used measures of price discovery are highly sensitive to the presence of price outliers in the calculations. We demonstrate using simulation studies however that the long-run information share (LFS) measure of price discovery location proposed by Kim and Linn (2022), coupled with Bayesian estimation of a Vector Error Correction Model (VECM) allowing for outliers, provides the most robust and reliable metric for evaluating price discovery in the presence of outliers. A separate empirical analysis of the spot and futures prices of non-ferrous metals shows the pervasive presence of price outliers. Implementation of our proposed estimation of a VECM using Bayesian methods allowing for outliers and the subsequent calculation of LFS, provides strong evidence that both spot and futures markets for non-ferrous metals contribute significantly to the price discovery process when daily price data are employed.

Keywords: Price discovery; Cointegration; Outliers; Robust estimation; Heavytailed distributions. (search for similar items in EconPapers)
JEL-codes: C11 C32 C58 G14 (search for similar items in EconPapers)
Pages: 13 pages
Date: 2025-12
New Economics Papers: this item is included in nep-min and nep-mst
References: Add references at CitEc
Citations:

Downloads: (external link)
https://drive.google.com/file/d/12qa5H7NnzMgILk3N8 ... bmF/view?usp=sharing First version, 2025 (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:inh:wpaper:2025-1

Access Statistics for this paper

More papers in Inha University IBER Working Paper Series from Inha University, Institute of Business and Economic Research Contact information at EDIRC.
Bibliographic data for series maintained by Bogang Jun ().