This session will delve into the critical role of granular market data and advanced analytical techniques for optimizing algorithmic trading strategies and ensuring best execution. It will touch upon the relevance of these concepts in traditional and emerging markets.
Financial crises, even at their worst, are familiar, episodic, and reversible. We know the tools: liquidity injections, recapitalizations, emergency legislation. Physical risks—climate change, demographic shifts, geopolitical disruptions, and the longer-term implications of AI—are different. They propagate through supply chains, cascade across industries and nations, and resist rapid remedy. A shuttered factory cannot be bailed out over a weekend; a water shortage or rising sea level reshapes entire economies and societies.
This talk discusses the framework for modeling physical and supply chain risk. The goal is not risk management in the narrow of employing statistical tools based on past prices. Rather, it is to build a structural understanding: to see where the real economy is most exposed, to map how shocks might spread, and to frame the constraints of adaptation and control. Compared to the familiar shocks of markets, the risks in view are longer-lived, harder to measure, and without precedent, and potentially existential. Modeling them requires rethinking the foundations of risk analysis.
