Stock returns and interest rates in China: the prequential approach

Abstract

This article aims to study whether interest rates help to forecast stock returns in China using the prequential approach. A bivariate VAR model and a univariate autoregressive model are examined. Out-of-sample probability forecasts, generated based on both a bootstrap-like simulation method and a nonparametric kernel-based simulation method, are evaluated from both calibration (reliability) and sorting (resolution) perspectives. The results from calibration test indicate that including interest rates in the model improves the model’s ability to issue realistic probability forecasts of stock returns (be well-calibrated). Considering stock returns also enhances the prediction of interest rates with respect to calibration. Assessment through Brier score and Yates partition suggests that the model performs better in distinguishing stock returns that actually occur and stock returns that do not occur after incorporating the influence of interest rates. Overall, interest rates help in forecasting stock returns in China in terms of both calibration and sorting.