Abstract:
This study investigates the predictability of 11 industrialized stock returns with emphasis on the
role of U.S. returns. Using monthly data spanning 1980:2 to 2014:12, we show that there exist
multiple structural breaks and nonlinearities in the data. Therefore, we employ methods that are
capable of accounting for these and at the same time date stamping the periods of causal
relationship between the U.S. returns and those of the other countries. First we implement a
subsample analysis which relies on the set of models, data set and sample range as in Rapach et
al. (2013). Our results show that while the U.S. returns played a strong predictive role based on
the OLS pairwise Granger causality predictive regression and news-diffusion models, it played
no role based on the pooled version of the OLS model and its role based on the adaptive elastic
net model is weak relative to Switzerland. Second, we implement our preferred model: a
bootstrap rolling window approach using our newly updated data on stock returns for each
countries, and find that U.S. stock return has significant predictive ability for all the countries at
certain sub-periods. Given these results, it would be misleading to rely on results based on
constant-parameter linear models that assume that the relationship between the U.S. returns and
those of other industrialized countries are permanent, since the relationship is, in fact, time varying,
and holds only at specific periods.
