Architetture regolatorie dinamiche e governance del rischio sistemico nei mercati fi nanziari ad alta intensità tecnologica

This book develops a comprehensive theoretical and institutional framework for a dynamic regulatory model designed to govern systemic risk in high-technology financial markets. The work starts from a central diagnosis: a structural misalignment exists between the speed and complexity of technological innovation (particularly in artificial intelligence, blockchain, fintech, and crypto-assets) and the inherent rigidity of traditional regulatory paradigms, which are built on assumptions of predictability, linear causality, and stability that no longer hold in contemporary digital markets. This gap generates zones of regulatory inadequacy that amplify systemic vulnerability. The book traces the historical evolution of financial risk from natural-event models to socially constructed, algorithmically driven risk, and critically surveys the existing EU regulatory landscape, including experimental tools such as regulatory sandboxes, innovation deals, and living labs, exposing their structural limits. Against this backdrop, the author proposes a dynamic regulatory architecture – formally grounded, computationally assisted, and iteratively calibrated – capable of proactively monitoring and adjusting regulatory intervention in real time, based on formalized risk thresholds and empirical feedback cycles. The model is then applied to three high-priority sectors: AI-driven financial markets, where the opacity of generative models creates non-linear and difficult-to-map risk; crypto-asset markets, where algorithmic volatility and speculative dynamics (including politically endorsed memecoins) create unprecedented systemic contagion risks; and the broader intersection of political communication and decentralized financial infrastructure, a phenomenon illustrated by recent cases of heads of state legitimizing highly speculative digital assets. The final chapters address the institutional architecture required for implementing dynamic risk governance, arguing for a deep integration of law, economics, and computer science as a necessary condition for regulatory resilience.