In this article we examine the structural stability of predictive regression models of U.S. quarterly aggregate real stock returns over the postwar era. We consider predictive regressions models of S&P 500 and CRSP equal-weighted real stock returns based on eight financial variables that display predictive ability in the extant literature. We test for structural stability using the popular Andrews SupF statistic and the Bai subsample procedure in conjunction with the Hansen heteroskedastic fixed-regressor bootstrap. We also test for structural stability using the recently developed methodologies of Elliott and Müller, and Bai and Perron. We find strong evidence of structural breaks in five of eight bivariate predictive regression models of S&P 500 returns and some evidence of structural breaks in the three other models. There is less evidence of structural instability in bivariate predictive regression models of CRSP equal-weighted returns, with four of eight models displaying some evidence of structural breaks. We also obtain evidence of structural instability in a multivariate predictive regression model of S&P 500 returns. When we estimate the predictive regression models over the different regimes defined by structural breaks, we find that the predictive ability of financial variables can vary markedly over time.

This article adopts a nonparametric quantile causality approach to examine the causal effects of the U.S. and Japan stock markets on the stock markets of the Pacific-Rim region. This approach allows us to detect not only nonlinear causalities in conditional return (mean) and conditional volatility (variance) but also the asymmetries of causalities under extreme market conditions (bullish vs. bearish states). Our results provide significant evidence of causality in return and volatility at different points of the conditional distributions of returns, with the greater effects from the U.S. than from Japan. Asymmetric quantile causality patterns are particularly pronounced in the case of Japan.