Learning, Rare Disasters, and Asset Prices

35 Pages Posted: 1 Jan 2012 Last revised: 2 May 2016

Yang K. Lu

Hong Kong University of Science & Technology (HKUST)

Michael Siemer

Board of Governors of the Federal Reserve System

Multiple version iconThere are 2 versions of this paper

Date Written: January 21, 2016

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.

Keywords: rare events, disaster, Bayesian learning, time-varying risk premia, return predictability

JEL Classification: E21, G12, D83

Suggested Citation

Lu, Yang K. and Siemer, Michael, Learning, Rare Disasters, and Asset Prices (January 21, 2016). Available at SSRN: https://ssrn.com/abstract=1977867 or http://dx.doi.org/10.2139/ssrn.1977867

Yang K. Lu

Hong Kong University of Science & Technology (HKUST) ( email )

Clearwater Bay
Kowloon, 999999
Hong Kong

Michael Siemer (Contact Author)

Board of Governors of the Federal Reserve System ( email )

20th Street and Constitution Avenue NW
Washington, DC 20551
United States

HOME PAGE: http://www.michael-siemer.com

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