Unmasking the impact of geopolitical risk on Bitcoin and Ethereum: regime-dependent and time-varying evidence
SN Business and Economics, cilt.6, sa.9, 2026 (Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 6 Sayı: 9
- Basım Tarihi: 2026
- Doi Numarası: 10.1007/s43546-026-01369-1
- Dergi Adı: SN Business and Economics
- Derginin Tarandığı İndeksler: Scopus, ABI/INFORM
- Anahtar Kelimeler: Cryptocurrency markets, Geopolitical risk, Quantile regression, Regime-dependence, TVP-VAR, Volatility spillover, Wavelet analysis
- Erciyes Üniversitesi Adresli: Evet
Özet
The present paper focuses on examining the regime-dependent relationship between geopolitical risk (GPR) and cryptocurrencies. Unlike previous research, which relies on static approaches, the current paper employs a more sophisticated methodology including Time-Varying Parameter Vector Autoregression (TVP-VAR), Wavelet Power Spectrum, and Quantile Regression analysis. Using daily data from 2017 to 2022, we find that in a highly interconnected financial market environment, cryptocurrencies play a role of shock transmitters. Additionally, using wavelet analysis, we identify that cryptocurrency volatilities are predominantly short-term in comparison to the medium-to-long term characteristics of safe-haven assets such as gold. More importantly, our results show that Bitcoin presents a weak and conditional safe haven at low return regimes, whereas this feature gets reversed at high returns, while Ethereum shows an inconsistent relationship with GPR. A sensitivity analysis using an alternative Butterworth-filtered GPR decomposition and percentile-bootstrap inference confirms that robust GPR effects are confined to specific quantile-frequency cells, and that broad safe-haven claims for either cryptocurrency should therefore be regarded as conditional on time horizon, market regime, and decomposition method. Based on the above discussion, we conclude that the response of cryptocurrencies to geopolitical risk is heterogeneous since it is conditional upon time horizons and market regimes. Furthermore, to investigate further potential asymmetries, we utilize the Markov regime-switching model to capture sudden changes in market behavior. Specifically, we employ dynamic regressions that detect abrupt regime changes associated with shifts in volatility. Our findings suggest that Bitcoin’s and Ethereum’s responsiveness to geopolitical risks is distinctly regime-dependent.