A newly published empirical investigation in Discover Analytics reveals that the safe-haven capacity of digital assets during major international crises is neither uniform nor guaranteed, but depends heavily on prevailing market conditions and specific token architectures. Researchers Vincent Adela, Samuel Duku Yeboah, David Korsah, Michael Provide Fumey, Mordecai Akondoh Ansah, and Lord Oduro examined the dynamic influence of geopolitical tensions triggered by the outbreak of the Russia–Ukraine war across ten prominent cryptocurrencies between February 1 and August 8, 2022. Utilizing an advanced quantile-on-quantile regression methodology, the research team demonstrated that digital currencies exhibit deeply heterogeneous and nonlinear reactions to escalating geopolitical threats. While leading benchmark assets such as Bitcoin and Ethereum displayed partial hedging capabilities only under moderate levels of international tension or during established bull markets, they suffered compounding losses during market downturns. Simultaneously, major alternative cryptocurrencies faced pronounced structural vulnerabilities, and the two dominant stablecoins moved in diametrically opposite directions throughout the conflict.

The investigation directly addresses an enduring contradiction within modern financial economics regarding whether decentralized digital currencies serve as robust crisis hedges or merely function as volatile speculative instruments. To contextualize investor decision-making, the authors evaluated multiple theoretical perspectives, including Portfolio Theory, Safe-Haven Theory, Asset Substitution Theory, and Behavioural Finance Theory. Under Portfolio Theory, rising global uncertainty encourages market participants to seek uncorrelated alternative assets to achieve effective portfolio diversification. Safe-Haven Theory traditionally associates crisis preservation with precious metals like gold, prompting recent inquiries into whether digital currencies offer comparable shelter against systemic shocks. Meanwhile, Asset Substitution Theory posits that escalating geopolitical confrontation causes capital reallocation away from embattled conventional markets toward perceived alternatives offering superior risk-adjusted preservation or return potential. Complementing these rational frameworks, Behavioural Finance Theory emphasizes how psychological phenomena, including collective panic and heightened uncertainty, amplify asset volatility. The Russia–Ukraine conflict provided a distinct natural experiment to evaluate these competing frameworks under extreme geopolitical disruption.

To capture the immediate shock of the conflict before secondary macroeconomic forces distorted price dynamics, the researchers deliberately restricted their sample window to the period from February 1 to August 8, 2022. This six-month window encapsulated the acute onset of military hostilities, international sanctions against Russia, and sudden structural volatility across global capital markets. The authors explained that extending the timeframe would have introduced confounding macroeconomic variables, including aggressive central bank interest rate hikes, broad inflationary pressures, and conflict fatigue, which would obscure direct war-induced effects. Daily price and return data for ten representative digital assets were retrieved from investing.com, encompassing market benchmarks Bitcoin and Ethereum, leading altcoins Binance Coin, Cardano, Ripple, Dogecoin, Polkadot, and Solana, as well as the prominent stablecoins USD Coin and Tether. To quantify geopolitical risk, the authors utilized the log-transformed Geopolitical Risk Index developed by Caldara and Iacoviello, which tracks the frequency of global press coverage addressing military clashes, terror threats, and political instability.

Descriptive statistical evaluations revealed significant structural variations across the examined digital assets throughout the conflict period. With the exception of the pegged stablecoins USD Coin and Tether, every cryptocurrency posted negative average daily returns, with Solana, Polkadot, and Cardano registering the steepest losses and highest return volatility, while Bitcoin and Binance Coin remained relatively more stable. Augmented Dickey-Fuller and Phillips-Perron unit root tests confirmed that all log-return series and the transformed geopolitical risk index were strictly stationary at the one percent significance level. Furthermore, Jarque-Bera tests conclusively demonstrated that all return distributions departed from normality, exhibiting significant skewness and excess kurtosis indicative of heavy tails and extreme price swings. Notably, most cryptocurrencies displayed negative skewness, demonstrating that downside shocks were more extreme than upward movements. Stablecoins exhibited stark divergence: USD Coin displayed positive skewness reflecting upward deviations from parity during flight-to-quality events, whereas Tether exhibited negative skewness caused by downward deviations during market distress.

To decipher the intricate interactions obscured by standard linear models, the researchers implemented a nonparametric quantile-on-quantile regression framework. Standard econometric methods typically focus on conditional mean relationships, failing to capture how an asset’s response changes when the market shifts from depression to irrational exuberance or when risk surges from minor friction to major war. The quantile-on-quantile technique overcomes this limitation by evaluating the full distribution of both variables, linking specific quantiles of geopolitical risk against specific quantiles of cryptocurrency returns. The authors categorized cryptocurrency performance into bearish regimes covering quantiles 0.05 to 0.30, intermediate stable regimes from 0.35 to 0.60, and bullish regimes from 0.65 to 0.95. Geopolitical risk was similarly grouped into minimal, moderate, and high levels. Applying a Gaussian kernel function with a fixed bandwidth parameter of h = 0.05 across nineteen discrete quantile increments from 0.05 to 0.95, the econometric model isolated granular slope parameters across diverse combinations of market conditions.

The empirical results uncovered striking behavioral asymmetries for the market’s two benchmark assets, Bitcoin and Ethereum. When operating within bearish market conditions, Bitcoin exhibited a consistently negative response across all levels of geopolitical risk, confirming that mounting international turmoil exacerbated market sell-offs rather than prompting safe-haven accumulation. Under stable market conditions, Bitcoin responded positively to moderate geopolitical tensions, suggesting that modest uncertainty stimulated hedging demand, whereas extreme geopolitical shocks depressed returns. In contrast, during bullish phases, Bitcoin reacted positively across all geopolitical risk quantiles, indicating that speculative optimism transformed crisis headlines into buying opportunities. Ethereum demonstrated similar regime dependence: during market downturns, it experienced uniform negative pressure across all geopolitical risk levels, eroding investor confidence. However, Ethereum maintained positive responses across all risk levels during neutral market states, behaving as a resilient asset, while under bullish conditions it responded favorably to extreme geopolitical spikes despite being dampened by minor risk fluctuations.

Beyond the primary market benchmarks, major altcoins demonstrated pronounced structural vulnerability, displaying limited capacity to shelter investor capital during geopolitical instability. Binance Coin and Cardano generated strongly negative correlations with geopolitical risk across both bearish and stable market environments, confirming that geopolitical uncertainty triggers broad risk aversion and depresses valuations in non-bullish settings. However, both tokens rebounded positively across all risk levels during bullish regimes, underscoring that their upside relies heavily on speculative sentiment. Ripple displayed a nuanced profile: while falling under low and moderate risk in downturns, it generated positive returns during extreme geopolitical shocks, hinting at occasional flight-to-liquidity interest. Dogecoin exhibited predominantly negative responses in bearish and neutral conditions, recovering only under high geopolitical risk within bullish settings. Polkadot and Solana showed persistent negative returns across market downturns, with Solana managing positive reactions only under the most extreme geopolitical quantiles, confirming that high-beta altcoins amplify broader financial stress rather than providing stability.

The most dramatic empirical divergence emerged in the stablecoin sector, where USD Coin and Tether exhibited completely opposite trajectories across the entire study period. USD Coin established a uniformly positive relationship with geopolitical risk across all market environments and risk quantiles, affirming its role as an effective safe haven and validating predictions from Asset Substitution Theory. When geopolitical tensions escalated, capital consistently flowed into USD Coin as investors sought a transparent, reserve-backed digital dollar to preserve wealth. Conversely, Tether recorded a uniformly negative response to geopolitical risk across all quantiles and market conditions, suffering persistent outflows and slight downward price pressure during periods of heightened uncertainty. The authors linked this stark contrast to institutional design and market perceptions: while USD Coin was widely viewed as a regulated store of value, Tether functioned primarily as a transactional trading vehicle burdened by market scrutiny regarding its backing reserves and liquidity resilience.

To ensure the reliability of their empirical conclusions, the authors conducted comprehensive robustness checks comparing the quantile-on-quantile parameter estimates against standard quantile regression models. Because standard quantile regression models evaluate only the quantiles of the dependent variable conditional on an independent variable, the authors decomposed the quantile-on-quantile slope estimates across nineteen discrete quantile intervals spanning from 0.05 to 0.95. The resulting comparison plots demonstrated that the average slope coefficients from the quantile-on-quantile framework closely mirrored the trajectory of traditional quantile estimates, differing primarily in magnitude rather than direction. This confirmed that the nonparametric quantile-on-quantile approach successfully preserved standard baseline properties while uncovering critical distribution-wide asymmetries. Additionally, the preliminary correlation analysis substantiated these findings, showing that while most cryptocurrencies maintained weak negative correlations with geopolitical risk, USD Coin exhibited an inverse correlation with general crypto tokens, and Tether moved in direct opposition to USD Coin, underscoring their distinct operational roles.

In their concluding remarks, the authors emphasized critical limitations and practical takeaways for digital asset management during acute international crises. The research team acknowledged that their focus on a single geopolitical event across a focused six-month period restricts broad generalizability to multi-year conflicts or distinct regional crises. Furthermore, the deliberate exclusion of macroeconomic controls such as interest rate shifts and inflation dynamics means that secondary monetary transmission channels were unobserved. Nevertheless, the findings offer vital guidance for institutional portfolio managers and financial regulators, proving that cryptocurrencies cannot be treated as a monolithic asset class or assumed to act as automatic hedges like physical gold. Safe-haven status is highly conditional, requiring investors to differentiate sharply between asset classes, token governance models, and underlying market regimes. The authors recommended that future studies examine longer analytical horizons, compare digital assets against sovereign bonds and fiat currencies, and incorporate real-time sentiment analytics to further refine geopolitical risk modeling.

Subject of Research: Cryptocurrency and stablecoin market responses to geopolitical risk during the Russia–Ukraine war

Article Title: Dynamic effect of geopolitical risk on major cryptocurrencies amid the Russia–Ukraine war

Article References: Adela, V., Yeboah, S. D., Korsah, D., Fumey, M. P., Ansah, M. A., & Oduro, L. (2026). Dynamic effect of geopolitical risk on major cryptocurrencies amid the Russia–Ukraine war. Discover Analytics, 4(1), Article 3. https://doi.org/10.1007/s44257-025-00051-4

Image Credits: AI Generated

DOI: 10.1007/s44257-025-00051-4

Keywords: geopolitical risk, Russia-Ukraine war, cryptocurrencies, Bitcoin, Ethereum, stablecoins, USD Coin, Tether, quantile-on-quantile regression, safe-haven assets, financial stability, market volatility, Binance Coin, Cardano, Dogecoin, Asset Substitution Theory, Caldara and Iacoviello index

News Source: Denise Maddox. (November 14, 2026). Major Cryptocurrencies Reacted Unevenly to Geopolitical Risk During Russia-Ukraine War. Scienmag.