Financial networks of cryptocurrency prices in time-frequency domains

This paper explores financial networks of cryptocurrency prices in both time and frequency domains. We complement the generalized forecast error variance decomposition method based on a large VAR model with network theory to analyze the dynamic network structure and the shock propagation mechanisms...

Full description

Saved in:
Bibliographic Details
Published in:Quality & quantity 2024-04, Vol.58 (2), p.1389-1407
Main Authors: Pagnottoni, Paolo, Famà, Angelo, Kim, Jong-Min
Format: Article
Language:English
Subjects:
COVID-19
Decomposition
Digital currencies
Disease transmission
Methodology of the Social Sciences
Prices
Risk management
Social networks
Social Sciences
Time series
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper explores financial networks of cryptocurrency prices in both time and frequency domains. We complement the generalized forecast error variance decomposition method based on a large VAR model with network theory to analyze the dynamic network structure and the shock propagation mechanisms across a set of 40 cryptocurrency prices. Results show that the evolving network topology of spillovers in both time and frequency domains helps towards a more comprehensive understanding of the interactions among cryptocurrencies, and that overall spillovers in the cryptocurrency market have significantly increased in the aftermath of COVID-19. Our findings indicate that a significant portion of these spillovers dissipate in the short-run (1–5 days), highlighting the need to consider the frequency persistence of shocks in the network for effective risk management at different target horizons.
ISSN:0033-5177
1573-7845
DOI:10.1007/s11135-023-01704-w