Interest Rate Forecasting and Refinancing Decisions

Abstract

Prepayment behavior is traditionally a significant component of the MBS pricing and hedging models. I introduce the following innovation in the prepayment behavior forecasting: (a) combine empirical and theoretical approach to the forecasting, simulating the rate path, while predicting the target rate with an empirical model (b) offer two methods for target interest rate forecasting including basic regression analysis with macro-variables and contemporary Natural Language Processing (NLP) based on Federal Open Market Committee (FOMC) announcements. I find that forecasting the mortgage rates with macro-variables is plausible in 1,3-,6-,9- and 12-month periods. I further establish methodology for the interest rate path and prepayment rates forecasting in falling rate environment and rising rate environment. Interestingly, results suggest high probability of loan fallout in the rising rate environment due to rate fluctuations along its path. The results also suggest that borrowers make interest rate-related prepayment decisions rather early in the period of the analysis.