Aktam Burkhanov

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.

Type of the article: Research Article

Abstract
The global transition toward non-bank capital allocation systems has altered the trajectory of modern industrial development during volatile market cycles. The primary objective of this study is to evaluate the macroeconomic impact of private risk equity across a panel of thirty-eight developed economies from 2010 to 2024. Quantitative estimations rely on a panel error correction model and interactive fixed effects specifications to separate long-run equilibrium trajectories from short-run transactional volatility. Long-run empirical parameters establish that a ten percent expansion in venture capital inflows drives a long-run real gross domestic product increase of 1.43%. The structural systems display robust dynamic stability by automatically correcting 45.2% of growth deviations annually following capital deployment disruptions. Short-run transmission vectors show that a ten percent liquidity injection increases research and development intensity by an absolute 0.0085 percentage points within the same calendar year. Interaction modeling reveals a significant negative moderation coefficient of –0.0004877 for the small enterprise tracking path. This negative vector demonstrates that high-quality public governance structures flatten the short-run capital transmission slope, acting as a structural buffer that shifts the baseline intercept of small business contributions from a low-governance margin of 11.33 up to an insulated high-governance margin of 14.11. Strong institutional protections and anti-corruption frameworks ensure that financial innovations enhance long-term economic stability rather than causing short-term friction.

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 the econometric models.