  Yield curve forecasting is an important problem in finance. In this work we
explore the use of Gaussian Processes in conjunction with a dynamic modeling
strategy, much like the Kalman Filter, to model the yield curve. Gaussian
Processes have been successfully applied to model functional data in a variety
of applications. A Gaussian Process is used to model the yield curve. The
hyper-parameters of the Gaussian Process model are updated as the algorithm
receives yield curve data. Yield curve data is typically available as a time
series with a frequency of one day. We compare existing methods to forecast the
yield curve with the proposed method. The results of this study showed that
while a competing method (a multivariate time series method) performed well in
forecasting the yields at the short term structure region of the yield curve,
Gaussian Processes perform well in the medium and long term structure regions
of the yield curve. Accuracy in the long term structure region of the yield
curve has important practical implications. The Gaussian Process framework
yields uncertainty and probability estimates directly in contrast to other
competing methods. Analysts are frequently interested in this information. In
this study the proposed method has been applied to yield curve forecasting,
however it can be applied to model high frequency time series data or data
streams in other domains.
