Risk Management Framework for Complex Systems: A Production Engineering Approach to Cryptocurrency Portfolio Optimization

This study presents a comprehensive risk management framework for complex systems, applying production engineering methodologies to cryptocurrency portfolio optimization. The framework integrates principal component analysis, K-means clustering, Hidden Markov Model regime detection, structural shock decomposition, network causality analysis, GARCH volatility modeling, and stationarity testing to provide a multifaceted approach to risk assessment and decision support. Analysis of fourteen cryptocurrency assets over a multi-year period reveals extreme risk concentration with 67.89% of portfolio variance explained by the first systematic factor, six distinct operational clusters, and five market regimes with volatility ratios reaching 6.91×. The study identifies supply chain disruption events as the primary source of negative abnormal returns, averaging negative 9.2%, while network analysis reveals hierarchical information transmission structures with Ethereum and Bitcoin as dominant hubs. GARCH modeling demonstrates mean volatility persistence of 0.938 with half-lives averaging 20.8 days. These findings validate return-based methodologies and provide actionable insights for resource allocation, position sizing, and dynamic hedging strategies in high-volatility environments. The framework establishes a foundation for production engineering approaches to financial risk management under extreme uncertainty.