Testing for Mean Reversion in Heteroskedastic Data Ii: Autoregression Tests Based on Gibbs-Sampling-Augmented Randomization

16 Pages Posted: 12 Mar 1999

See all articles by Chang-Jin Kim

Chang-Jin Kim

Dept. of Economics, University of Washington

Charles R. Nelson

Dept of Economics

Date Written: November 1997

Abstract

A decade ago Fama and French (1988) estimated that 40% of variation in stock returns was predictable over horizons of 3-5 years, which they attributed to a mean reverting stationary component in prices. While it has been clear that the Depression and war years exert a strong influence on these estimates, it has not been clear whether the large returns of that period contribute to the information in the data or rather are a source of noise to be discounted in estimation. This paper uses the Gibbs-sampling-augmented randomization methodology to address the problem of heteroskedasticity in estimation of multi-period return autoregressions. Extending the sample period to 1995, we find little evidence of mean reversion. Examining subsamples, only 1926-46 provides any evidence of mean reversion, while the post war period is characterized by mean aversion. A test of structural change suggests that this difference between pre and post war periods is significant.

JEL Classification: C15

Suggested Citation

Kim, Chang-Jin and Nelson, Charles R., Testing for Mean Reversion in Heteroskedastic Data Ii: Autoregression Tests Based on Gibbs-Sampling-Augmented Randomization (November 1997). Available at SSRN: https://ssrn.com/abstract=148309 or http://dx.doi.org/10.2139/ssrn.148309

Chang-Jin Kim (Contact Author)

Dept. of Economics, University of Washington ( email )

Department of Economics (Box 353330)
University of Washington
Seattle, WA 98195-3330
United States

HOME PAGE: http://https://econ.washington.edu/people/chang-jin-kim

Charles R. Nelson

Dept of Economics ( email )

Box 353330
Seattle, WA 98195-3330
United States

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