Learning and excess volatility

James Bullard and John Duffy

No 1998-016, Working Papers from Federal Reserve Bank of St. Louis

Abstract: We introduce adaptive learning behavior into a general equilibrium lifecycle economy with capital accumulation. Agents form forecasts of the rate of return to capital assets using least squares autoregressions on past data. We show that, in contrast to the perfect foresight dynamics, the dynamical system under learning possesses equilibria characterized by persistent excess volatility in returns to capital. We explore a quantitative case for these learning equilibria. We use an evolutionary search algorithm to calibrate a version of the system under learning and show that this system can generate data that matches some features of the time series data for U.S. stock returns and per capita consumption. We argue that this finding provides support for the hypothesis that the observed excess volatility of asset returns can by explained by changes in investor expectations against a background of relatively small changes in fundamental factors.

Keywords: Capital; Stock - Prices (search for similar items in EconPapers)
Date: 1998
References: Add references at CitEc
Citations: View citations in EconPapers (26)

Published in Macroeconomic Dynamics, April 2001, 5(2), pp. 272-302

Downloads: (external link)
http://research.stlouisfed.org/wp/more/1998-016 (application/pdf)
http://research.stlouisfed.org/wp/1998/1998-016.pdf

Related works:
Journal Article: LEARNING AND EXCESS VOLATILITY (2001)
Working Paper: Learning and Excess Volatility (1999)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:fip:fedlwp:1998-016

Ordering information: This working paper can be ordered from

Access Statistics for this paper

More papers in Working Papers from Federal Reserve Bank of St. Louis Contact information at EDIRC.
Bibliographic data for series maintained by Scott St. Louis ().