Nonfundamental Asset Price Fluctuations and the Distributional Origins of Asset Premia
Abstract
Latest Version
This paper investigates the impact of nonfundamental asset price fluctuations on asset premia, macroeconomic aggregates, and inequality. I build and estimate a heterogeneous-agent business-cycle model featuring incomplete markets, portfolio choice, and nonfundamental asset price shocks. The estimated model successfully replicates empirical equity and term premia. Household heterogeneity is key, as it limits risk sharing, leading households to demand sizable risk compensation. Half of the equity premium arises from fundamental macroeconomic shocks, while the other half compensates for risks from nonfundamental asset price fluctuations. Despite this, nonfundamental asset price shocks have limited effects on aggregate outcomes and standard inequality measures. Finally, I use the model to explain variation of asset premia over time and to assess the effects of monetary policy on asset premia.
Can Public Debt crowd in Private Investment?
with Christian Bayer
Abstract
Latest Version
Working Paper
If households self-select into a risky high-income state through investment, increased government debt can stimulate investment and improve welfare. In a heterogeneous agent endogenous growth model, government debt helps households smooth consumption and encourages investment in risky, high-return assets, crowding in aggregate growth. However, when debt becomes excessive, capital crowding out and distortionary taxation negate these benefits. Using a model calibrated to U.S. data, we show that this crowding-in effect suggests a higher optimal debt-to-GDP ratio than currently observed.
Generative Economic Modeling
with Hanno Kase & Matthias Rottner
Abstract
Latest Version
Working Paper
GitHub Repo
Video Presentation
Media Coverage
We introduce a novel approach for solving quantitative economic models: generative economic modeling. Our method combines neural networks with conventional solution techniques. Specifically, we train neural networks on simplified versions of the economic model to approximate the complete model's dynamic behavior. Relying on these less complex sub-models circumvents the curse of dimensionality, allowing the use of well-established numerical methods. We demonstrate our approach across settings with analytical characterizations, nonlinear dynamics, and heterogeneous agents, employing asset pricing and business cycle models. Finally, we solve a high-dimensional HANK model with financial frictions to highlight how aggregate risk amplifies the precautionary motive.
