Employing annual returns generated from overlapping monthly price indexes for the G-7 stock markets, this paper examines asymmetry and common nonlinearities in long-horizon stock returns. Identifying widespread nonlinearities based on LSTAR or ESTAR models, we find that the asymmetric nonlinear dynamics induces a substantial portion of predictable variations in long-horizon stock returns. The nonlinear models clearly outperform linear models "in sample" and in most of the out of sample forecasting exercises. With nonlinear impulse responses suggesting strong stability of return dynamics, the empirical results of this paper provide useful information in developing annual investment strategies for international stock markets.
- Long-horizon stock returns
- Smooth transition autoregressive model