Forecasting equity risk premium: A new method based on wavelet de-noising

Forecasting equity risk premium is notoriously difficult due to a mass of data noise in the raw series and the absence of clear tendency. Using the monthly S&P 500 excess returns from 1927:01 to 2018:12, we first de-noise the in-sample original returns series via wavelet method to capture the basic trend of equity risk premium, and then propose forecasting models to obtain one-step forward out-of-sample predicted values based on the de-noised returns. Our new models can provide substantially superior out-of-sample performance compared with other competing models and the historical average. Sizeable economic gains can be realized by a mean-variance investor if they allocate their asset through the new approach. Our findings are robust under different settings from both statistical and economic perspectives.