Impact of Listed Firms’ Correlation on Idiosyncratic Volatility Co-movement—A Network and Wavelet Analysis
Yang Zhao () and
Jian Chen
Additional contact information
Yang Zhao: Southeast University
Jian Chen: Southeast University
Computational Economics, 2025, vol. 66, issue 3, No 9, 2055-2076
Abstract:
Abstract Idiosyncratic risk reflects the specific and non-systemic risk of a firm. Based on Capital Asset Pricing Model (CAPM), idiosyncratic volatility (IVOL) measures a portion of the variation in asset returns that cannot be explained by a particular CAPM. Existing literature shows that there is a co-movement phenomenon among listed-firms, which contradicts the definition of IVOL. Analogous to the inter-stock correlation phenomenon, we test whether the inter-firm network relationship is the underlying reason for the IVOL co-movement phenomenon among the listed-firms. Using a comparative analysis method, we construct three network correlation structures (geographic distance, stock return correlation, and risk transmission) and examine whether the degree of IVOL co-movement significantly decreases after eliminating various network effects. Wavelet analysis reveals that the co-movement phenomenon persists even after accounting for network correlations, suggesting the presence of unique relationships among the firms, such as interpersonal relationship (Chinese ‘Guanxi’ culture). Given the results, we suggest that policy makers can focus on the unobserved interpersonal relationship network when determining the portfolios.
Keywords: Idiosyncratic volatility co-movement; Inter-firm network correlation; Wavelet analysis; Wavelet correlation coefficient (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:
Downloads: (external link)
http://link.springer.com/10.1007/s10614-024-10780-5 Abstract (text/html)
Access to the full text of the articles in this series is restricted.
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:kap:compec:v:66:y:2025:i:3:d:10.1007_s10614-024-10780-5
Ordering information: This journal article can be ordered from
http://www.springer. ... ry/journal/10614/PS2
DOI: 10.1007/s10614-024-10780-5
Access Statistics for this article
Computational Economics is currently edited by Hans Amman
More articles in Computational Economics from Springer, Society for Computational Economics Contact information at EDIRC.
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().