A Nonlinear Approach to the Factor Augmented Model: The FASTR Model

This research seeks to combine Factor Augmentation with Smooth Transition Regression, in order to be able to distinguish between regimes. Nine FASTR models are examined in the prediction of five stock excess returns and realized volatility. Statistical performance measures, such as the Directional Accuracy test, conclude positive significant accuracy for most time series. Excess returns achieved in portfolio optimization are up to 25.225%, with a Sharpe Ratio of 0.553. Expansions are added to the model, including the soft-thresholding method LARS, as well as factor selection. Results conclude the model with expansions performs even better on the Mean Squared Error and Correctly Predicted Signs tests.