Abstract
The Federal funds rate is a cornerstone of asset pricing that has a significant impact on asset valuation and portfolio performance. However, estimating it reliably can be a challenging issue given that the FOMC makes monetary policy decisions based on complex economic conditions. The authors leveraged existing literatures’ findings on factors and combined those major factor categories into the new model, which includes inflation, labor markets, financial condition, and proxy of global market, and the authors selected the optimal data series to optimize the effectiveness of detecting Fed decisions through a classification factor selection process. Also, the authors improved the regression from fixed coefficients to gradient boosting nonlinear regression approach to reflect the dynamic interconnections among all the factors and their lags through different periods. Upon conducting out-of-sample forecasting, with these selected factors and machine learning gradient boosting regression, the out-of-sample RMSE improved by 77% from traditional Taylor rule model, which offered an alternative robust solution for forecasting the Federal fund rates that can be further applied to asset pricing and investing.
Published Version
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