Diagnosing cryptocurrency security vulnerability through time-series decomposition

Cryptocurrency markets have experienced repeated systemic breakdowns over the past decade, exposing structural fragilities within digital asset ecosystems. Prominent examples include the Mt. Gox collapses (2011–2014), the COVID-19-driven “312” flash crash, the “519” crash in 2021 following environmental concerns, the 2017–2018 crypto winter, the collapse of the Terra/Luna ecosystem and the FTX exchange in 2022, and the combined effects of Grayscale ETF outflows and tariff conflicts in early 2025. These disruptions were driven by regulatory shocks, exchange failures, excessive leverage, macroeconomic instability, and automated liquidation cascades, often producing billions in losses within hours and erasing trillions in market capitalization. This study proposes a mathematical framework for diagnosing cryptocurrency market failures through the decomposition of financial time-series data. The approach integrates nonlinear signal analysis with regime-shift detection techniques to identify critical transitions preceding major breakdown events. Empirical examination of multiple crisis episodes reveals consistent precursory signatures, including structural changes in volatility dynamics, distortions in trading flows, and abnormal amplitude fluctuations in price signals. These indicators provide insight into latent systemic instability and suggest the feasibility of early diagnostic signals for emerging market stress. The proposed framework contributes a quantitative perspective for analyzing crypto-market fragility and offers analytical tools for examining complex, chaotic behaviors that remain inadequately captured by conventional financial risk metrics.