Risk assessment of blockchain-based stablecoins: Modeling USDT volatility and tail risk with EVT, VaR, and expected shortfall
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DOIhttp://dx.doi.org/10.21511/imfi.23(2).2026.33
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Article InfoVolume 23 2026, Issue #2, pp. 460–472
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Type of the article: Research Article
Abstract
Stablecoins serve as the primary liquidity and settlement platform for decentralized finance, yet recent market shocks and de-pegging events demonstrate systemic vulnerability regarding their stability. The purpose of this study is to quantify the tail risk of Tether (USDT) to determine the accuracy of different risk modeling frameworks during periods of extreme market stress. This study employs historical simulation, parametric Gaussian models, Monte Carlo simulation, and Extreme Value Theory using the Peaks-Over-Threshold approach on daily log returns from 2015 to 2025. Statistical diagnostics confirm high excess kurtosis of 24.3 and a negative skewness of –3.1 in the asset returns, which explicitly invalidates normal distribution assumptions. The empirical results reveal that Gaussian methods systematically underestimate extreme risk by 47% during high-volatility regimes. Extreme Value Theory models capture fat-tailed behavior with 50% higher precision than traditional models, identifying a maximum potential one-day loss of 1.50%. Backtesting parameters at the 95% and 99% confidence levels show that standard Value at Risk models fail to predict 14 out of 18 historical tail-risk anomalies. Expected Shortfall calculations under the generalized Pareto distribution successfully cover 99.8% of historical volatility spikes. This study concludes that Extreme Value Theory frameworks are essential for the robust design of decentralized finance protocols and the development of institutional risk management standards.
Acknowledgments
The authors express gratitude to our respective university departments and institutional research groups for providing the technical infrastructure necessary to conduct this study. We also recognize the participants of internal research seminars whose early feedback helped refine the core empirical parameters of this stablecoin risk framework.
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JEL Classification (Paper profile tab)G32, C14, C58, G15
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References47
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Tables3
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Figures5
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- Figure 1. Conceptual model
- Figure 2. Daily log returns of USDT (2015–2025)
- Figure 3. Frequency of the 100 most negative daily returns for USDT
- Figure 4. Frequency of the 100 most positive daily returns for USDT
- Figure 5. Histogram of USDT daily log returns
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- Table 1. Descriptive statistics of USDT daily log returns (2015–2025)
- Table 2. Value-at-risk (VaR) and expected shortfall (ES) estimates for USDT
- Table 3. Kupiec POF backtesting results for 99% VaR
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