|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|
|I will be available for interview at the American, British, and Spanish Meetings 2017/2018|
Disagreement, Changing Beliefs, and Stock Market Volatility - Pdf
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 cross-sectional data from the Survey of Professional Forecasters.
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
I discuss how neural networks can be used to model costly information acquisition under asymmetric information. I first characterize elementary properties of neural networks. I then show in a model of trade among differentially informed investors that neural networks permit information inference from prices at arbitrary precision but that information asymmetry can persist even in the absence of noise traders. Finally, I outline how such models might be able to explain why trade volume predicts returns years ahead — something that models of trade in the presence of noise traders cannot explain.