Political uncertainty and multi-scale systemic risk spillovers in the cryptocurrency market: A STVAR-based network topology approach - Zhecheng Wang; Xu Zhang; Wenting Xu; Xian Yang; Abdul Rauf (PLoS ONE, 2026)
This study employs a smooth transition vector autoregressive model combined with a network topology approach to capture the nonlinear impact of political uncertainty on multi-scale systemic risk spillovers in cryptocurrency market. To reveal the risk characteristics at different time scales, we use wavelet packet decomposition to decompose the sequence into short-term, medium-term, and long-term components; We also use forecast error variance decomposition to quantify risk spillovers, in order to study the direction and intensity of risk spillovers between different cryptocurrencies. In terms of the responses of cryptocurrency systemic risks to political uncertainty shocks, we find significant asymmetries. The mid- and long-term risk components indicate that most cryptocurrencies exhibit a stronger response during periods of high political uncertainty than during low periods. Moreover, shocks during high political uncertainty periods enlarge the cross-cryptocurrency risk spillovers. Finally, Bitcoin, Ethereum and Monero are stable risk transmitters, while Peercoin and Namecoin are more vulnerable risk receivers. This paper provides some insights into the differential impact of political uncertainty on the stability and interconnectivity of various cryptocurrencies.