Fabian Schuetze
Fabian Schuetze
Fabian Schuetze





Research Interest

Macroeconomics, Asset Pricing, Learning in Financial Markets


Ramon Marimon (Chair), Piero Gottardi, Rody Manuelli


Click here for my CV


Villa La Fonte, Via delle Fontanelle 18, 50014 San Domenico, Florence, Italy


+39 380 77 60 900




Placment Director

David K. Levine david@dklevine.com

Placement Secretary

Anne Banks anne.banks@eui.eu


Working Papers

Disagreement, Changing Beliefs, and Stock Market Volatility - Pdf, Data, Slides

Job Market Paper, updated regularly

I provide conditions under which disagreement about dividend growth forecasts amplifies stock market volatility, in line with empirical evidence. In a frictionless economy with two Epstein-Zin investors, I model disagreement as exogenous heterogeneity in beliefs: one investor is pessimistic, the other is not. I show that disagreement amplifies volatility only if investors switch beliefs, that is if an investor is only temporarily optimistic. If instead one investor is permanently pessimistic, prices are less volatile than dividends, and higher disagreement lowers volatility — in contradiction with evidence. Finally, I provide empirical support for switching beliefs among investors, using panel data from the Survey of Professional Forecasters.


Lucas' asset pricing model when utility is unbounded - Pdf, Slides, Code on Quant-Econ

together with Joao Brogueira

Economic Theory Bulletin, October 2017

This note presents a proof of the existence of a unique equilibrium in a Lucas (1978) economy when the utility function displays constant relative risk aversion, and the logarithm of dividends follow a normally distributed autoregressive process of order one with positive autocorrelation. We provide restrictions on the coefficient of relative risk aversion, the discount factor and the conditional variance of the consumption process that ensure the existence of a unique equilibrium.


Work In Progress

Trade Volume, Noise Traders, and Information Acquisition with Neural Networks - Pdf

Measures of trade volume in financial markets predict excess returns years ahead. Such relationship is difficult to rationalize with existing models of trade in financial markets among asymmetrically informed investors. I discuss how neural networks can be used to generate trade among asymmetrically informed investors and why neural neural networks could be used to explain the empirical relationship between trade volume and excess returns.




Credit, Money, Interest, and Prices, by Saki Bigio and Yuli Sannikov - Slides

EUI, Florence, November 2016