Michael Johannes

Sequential Learning, Predictability, and Optimal Portfolio Returns

The Journal of Finance · 2013-11-19 · 225 citations

ABSTRACT This paper finds statistically and economically significant out‐of‐sample portfolio benefits for an investor who uses models of return predictability when forming optimal portfolios. Investors must account for estimation risk, and incorporate an ensemble of important features, including time‐varying volatility, and time‐varying expected returns driven by payout yield measures that include share repurchase and issuance. Prior research documents a lack of benefits to return predictability…

Parameter Learning in General Equilibrium: The Asset Pricing Implications

American Economic Review · 2016-02-29 · 224 citations

Parameter learning strongly amplifies the impact of macroeconomic shocks on marginal utility when the representative agent has a preference for early resolution of uncertainty. This occurs as rational belief updating generates subjective long-run consumption risks. We consider general equilibrium models with unknown parameters governing either long-run economic growth, rare events, or model selection. Overall, parameter learning generates long-lasting, quantitatively significant additional macro…

Learning about Consumption Dynamics

The Journal of Finance · 2015-01-27 · 126 citations

ABSTRACT This paper characterizes U.S. consumption dynamics from the perspective of a Bayesian agent who does not know the underlying model structure but learns over time from macroeconomic data. Realistic, high‐dimensional macroeconomic learning problems, which entail parameter, model, and state learning, generate substantially different subjective beliefs about consumption dynamics compared to the standard, full‐information rational expectations benchmark. Beliefs about long‐run dynamics are v…

Option Pricing of Earnings Announcement Risks

Review of Financial Studies · 2018-05-11 · 97 citations

This paper uses option prices to learn about the equity price uncertainty surrounding information released on earnings announcement dates. To do this, we introduce reduced-form models and estimators to separate price uncertainty about earnings announcements from normal day-to-day volatility. Empirically, we find strong support for the importance of earnings announcements. We find that the anticipated price uncertainty is quantitatively large, varies across time, and is informative about the futu…

Parameter Learning in General Equilibrium: The Asset Pricing Implications

SSRN Electronic Journal · 2012-01-01 · 87 citations