Geoeconomic Pressure

We examine whether and how granular, real-time predictive models should be in- tegrated into central banks’ macroprudential toolkit. First, we develop a tractable framework that formalizes the tradeoff regulators face when choosing between imple- menting models that forecast systemic risk accurately but have uncertain causal content and models with the opposite profile. We derive the regulator’s optimal policy in a set- ting in which private portfolios react endogenously to the regulator’s model choice and policy rule. We show that even purely predictive models can generate welfare gains for a regulator, and that predictive precision and knowledge of causal impacts of policy interventions are complementary. Second, we introduce a deep learning architecture tailored to financial holdings data—a graph transformer—and we discuss why it is op- timally suited to this problem. The model learns vector embedding representations for both assets and investors by explicitly modeling the relational structure of holdings, and it attains state-of-the-art predictive accuracy in out-of-sample forecasting tasks including trade prediction.