In an increasingly volatile and uncertain financial landscape, particularly within the cryptocurrency market, robust risk assessment methods are essential. This study introduces an interdisciplinary framework that applies engineering concepts, specifically the three-sigma (3σ) criterion, to financial asset management. Drawing on the analogy between structural stress and financial return volatility, this study conceptualizes market returns as a stochastic stress process and asset strength as a dynamically adjusted resilience threshold. Using LUNA coin as a case study, the research employs Monte Carlo simulations, statistical process control principles, and a range of statistical tests, including the Shapiro-Wilk, Kolmogorov–Smirnov, and ANOVA tests, to evaluate the probability of structural failure, modeled as the first passage beyond a critical return threshold. The results reveal a first breach probability of 1.96% and identify a failure threshold of –0.3838, highlighting the model's capacity to detect extreme downside risk more conservatively than traditional Value-at-Risk (VaR) approaches. These findings support the use of three-sigma thresholds in highly volatile markets and align with previous studies emphasizing tail-risk modeling and engineering-inspired risk measures. This framework not only improves the understanding of asset fragility in crypto markets but also provides a practical tool for dynamic and real-time risk management. This study contributes to the evolving field of financial engineering by bridging statistical design principles and asset resilience modeling, offering new insights for researchers, investors, and policymakers.
