Modeling the Term Structure of Interest Rates with Restricted Boltzmann Machines

Abstract: This thesis investigates if Gaussian restricted Boltzmann machines can be used to model the Swedish term structure of interest rates. The models are evaluated based on the ability to make one-day-ahead forecasts and the ability to generate long term scenarios. The results are compared to simple benchmark models, such as assuming a random walk. The effects of preprocessing the input data with principal component analysis are also investigated. The results show that the ability to make one-day-ahead forecasts, measured as a mean squared error, is comparable to a random walk benchmark, both in-sample and out-of-sample. The ability to generate long term scenarios show promising results. The scenarios are evaluated based on visual properties and one-year-ahead forecast errors on semi-out-of-sample data. The results outperform the benchmark models. The main focus of the thesis is not to optimize performance of the models, but instead to serve as an introduction to modeling the term structure of interest rates with Gaussian restricted Boltzmann machines.