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

A decade ago Fama and French (1998) estimated that 40% variations 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.