Iranian Journal of Finance

Abstract 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.