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Risk assessment of blockchain-based stablecoins: Modeling USDT volatility and tail risk with EVT, VaR, and expected shortfall

Aktam U. BurkhanovDSc., Professor, Faculty of Economics, Department of Finance and Credit, National University of Uzbekistan named after Mirzo Ulugbek; DSc., Professor, Faculty of Economics, Department of Management and Marketing, Alfraganus University, UzbekistanAbdul Jalil MahamaPh.D. Researcher, Senior Lecturer, Faculty of Economics, Department of Finance and Credit, National University of Uzbekistan named after Mirzo Ulugbek; Senior Research Fellow, President Academy of Public Policy and Administration, UzbekistanIlyоs AbdullaevDSc, Professor, Faculty of Economics, Department of Business and Management, Urgench State University, UzbekistanNodira B. AbdusalomovaDSc., Professor, Faculty of Accounting, Department of Accounting, Tashkent State University of Economics, UzbekistanBunyod UsmonovDSc, Associate Professor, Faculty of Accounting, Department of Financial Analysis, Tashkent State University of Economics, UzbekistanS. A. RustamovPh.D. Researcher, Faculty of Digital Economics and Information Technology, Department of Mathematical Methods in Economics, Tashkent State University of Economics, Uzbekistan

2026

ABI

Annotatsiya

Type of the article: Research ArticleAbstractStablecoins 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.AcknowledgmentsThe 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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DOI: 10.21511/imfi.23(2).2026.33

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