Cryptocurrency Systemic Risk Spillovers Under Political Uncertainty

A 5.5 KB Excel dataset by Zhecheng Wang, last updated in May 2026, analyzes systemic risk spillovers in cryptocurrency markets. It uses wavelet packet decomposition to examine risk at short-, medium-, and long-term scales and forecast error variance decomposition to quantify spillover intensity and direction. The study finds asymmetrical responses to political uncertainty, identifying Bitcoin, Ethereum, and Monero as stable risk transmitters and Peercoin and Namecoin as vulnerable receivers.

Use Cases

• Modeling nonlinear risk spillovers between cryptocurrencies based on the described vector autoregressive and network topology approach.
• Analyzing the asymmetric impact of political uncertainty on market stability using decomposed short-, medium-, and long-term risk components.
• Identifying stable risk transmitters and vulnerable receivers in the cryptocurrency ecosystem based on the findings for Bitcoin, Ethereum, Monero, Peercoin, and Namecoin.
• Studying how cross-cryptocurrency risk spillovers are amplified during periods of high political uncertainty.

Strengths

• Dataset is openly licensed under CC-BY-4.0.
• Analysis employs multiple advanced econometric methods, including smooth transition vector autoregressive models and wavelet packet decomposition.
• Findings provide specific insights into the roles of individual cryptocurrencies like Bitcoin and Ethereum as risk transmitters.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The dataset is very small at 5.5 KB, indicating limited scope.

Provenance

Source

figshare

Collection Method

Analysis likely derived from public cryptocurrency market data and political uncertainty indices, using the described econometric models.

Freshness

Last updated 2026-05-28 17:28:53; freshness should be verified.