Abstract
Cryptocurrency markets have become increasingly integrated into the global financial system, yet their systemic vulnerabilities to geopolitical shocks remain poorly understood. This paper provides the first empirical analysis linking US-China tensions (UCT) to systemic risk in cryptocurrency markets. Using monthly data from August 2015 to February 2024, we measure systemic interconnectedness via a TVP-VAR Total Connectedness Index, applying linear and nonlinear ARDL frameworks to the full sample, as well as to bearish and bullish markets separately. The results reveal strong persistence in systemic risk. Crucially, UCT emerges as the dominant driver of contagion across all regimes, whereas general geopolitical risk (GPR) measures exhibit limited explanatory power. The nonlinear analysis uncovers symmetric long-run effects, indicating that the mere uncertainty of UCT, whether increasing or decreasing, elevates long-run systemic risk. Furthermore, lagged Bitcoin trading volume consistently exacerbates contagion across all regimes. These findings provide implications for regulators and investors, highlighting the necessity for regime-sensitive, geopolitically informed risk management strategies.
| Original language | English |
|---|---|
| Article number | 105574 |
| Journal | International Review of Economics and Finance |
| Volume | 110 |
| Early online date | 6 Jul 2026 |
| DOIs | |
| Publication status | Published - Sept 2026 |
Bibliographical note
We thank the participants of the 8th Cryptocurrency Research Conference (CRC2025) in Athens, as well as Hanh Duyen Dinh, Lambis Dionysopoulos, Matteo Foglia, and Larisa Yarovaya, for their valuable comments and suggestions, which have enhanced the quality of this paper.Publisher Copyright:
© 2026 The Authors.
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 8 Decent Work and Economic Growth
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SDG 16 Peace, Justice and Strong Institutions
Keywords
- Systemic risk
- Cryptocurrencies
- Geopolitical risk
- US–China tensions
- TVP-VAR
- ARDL model
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