Asset Pricing With Disaster Risk

Paper Session

Saturday, Jan. 7, 2017 8:00 AM – 10:00 AM

Hyatt Regency Chicago, Dusable
Hosted By: Econometric Society
  • Chair: Francois Gourio, Federal Reserve Bank of Chicago

Asset Pricing Under Rational Learning About Rare Disasters

Christos Koulovatianos
,
University of Luxembourg
Volker Wieland
,
Goethe University Frankfurt

Abstract

Why is investment in stocks so persistently weak after a rare disaster? If investors erroneously become pessimistic about stock returns, how long does it take until negative market outcomes due to self-fulfilling expectations revert? Rational-expectations models with variable disaster risk often fail to tightly connect disaster episodes with post-disaster expectations. We demonstrate that introducing limited information and learning about rare disaster risk, while retaining full rationality, can generate stock-investment behavior that seems similar to persistent investor fear after a rare disaster. We build a workable asset-pricing model of variable disaster risk under rational expectations (RE) and we introduce two limited-information variations to it: (a) rational learning for state verification (RLS), in which investors know the data-generating process of disaster riskiness but cannot observe whether the economy is in a riskier state or not, and (b) rational learning about the data-generating process (RLP) of disaster risk, an environment in which investors neither know the data-generating process of disaster riskiness nor they can observe the state of disaster riskiness. We provide analytical results for all setups (RE, RLS, and RLP), and examine both the transitional dynamics of asset prices after a disaster and their long-run behavior.

Learning, Rare Disasters, and Asset Prices

Michael Siemer
,
Federal Reserve Board
Yang K. Lu
,
Hong Kong University of Science and Technology

Abstract

We incorporate joint learning about state and parameter into a consumption-based asset pricing model with rare disasters. Agents are uncertain whether a negative shock signals the onset of a disaster or how much long-term damage a disaster will cause and they update their beliefs over time. The interaction of state and parameter uncertainty increases the total amount of uncertainty and slows learning. Once the two types of uncertainty are both priced in asset prices, their joint effect enables our model to account for the level and volatility of U.S. equity returns without relying on exogenous variation in disaster risk or any realization of disaster shock in the data sample.

Option Prices in a Model With Stochastic Disaster Risk

Sang Byung Seo
,
University of Houston
Jessica Wachter
,
University of Pennsylvania

Abstract

Contrary to well-known asset pricing models, volatilities implied by equity index options exceed realized stock market volatility and exhibit a pattern known as the volatility skew. We explain both facts using a model that can also account for the mean and volatility of equity returns. Our model assumes a small risk of economic disaster that is calibrated based on international data on large consumption declines. We allow the disaster probability to be stochastic, which turns out to be crucial to the model's ability
both to match equity volatility and to reconcile option prices with macroeconomic data on disasters.
Discussant(s)
Anna Orlik
,
Federal Reserve Board
Julien Penasse
,
University of Luxembourg
Martin M. Andreasen
,
Aarhus University
JEL Classifications
  • G0 - General