Project 2: Identification, measurement and pricing of spillover risks and uncertainty in networks
Work Package 1:
Empirical determination of regulatory risk and uncertainty in financial networks
Work Package 2:
Asset pricing in network models under regulatory uncertainty
This package builds on Herskovic (2018) to analyze asset pricing in a network economy where regulations alter production and financial linkages. We incorporate recursive preferences to capture long-run “network risk,” model tariffs and taxes as frictions, and allow for endogenous network formation. Regulatory uncertainty is represented by regime-switching models with stochastic policy shocks. Theoretical insights are linked to empirical findings from WP1 to explain how regulation-driven changes in network structure influence risk premia, asset returns, and the long-run stability of the financial system.
This work package measures the impact of regulatory risk arising from trade policy decisions on financial and real-economy networks. Using data from the U.S. “trade war period,” we construct dynamic variance spillover networks to identify how shocks propagate across sectors. We develop new time-varying VAR and difference-in-differences techniques to detect significant changes in connectedness before and after tariff announcements. The results provide empirical measures of regulatory spillovers, priced uncertainty through variance risk premia, and the channels through which policy-induced shocks affect asset prices and systemic risk.
This project investigates how regulatory uncertainty affects asset pricing in network economies. Regulatory measures—such as tariffs, CO₂ taxes, or financial rules—do not only influence targeted sectors but also spill over through supplier-customer and credit linkages, shaping firms’ behavior and asset prices. We combine theoretical and empirical analyses to capture these dynamics.
On the theoretical side, we model regulatory shocks within a regime-switching framework and incorporate recursive preferences to derive long-run risk premia for network uncertainty. We extend existing models by allowing for endogenous network formation, where firms adjust their links in response to new regulations such as trade tariffs or emission limits.
Empirically, we quantify the effects of regulatory uncertainty—particularly during the U.S. trade war period—on financial network connectedness and asset returns. New econometric tools are developed to detect and measure spillovers of regulatory shocks.
The research is organized into two main Work Packages:
