  The importance of unspanned macroeconomic variables for Dynamic Term
Structure Models has been intensively discussed in the literature. To our best
knowledge the earlier studies considered only linear interactions between the
economy and the real-world dynamics of interest rates in DTSMs. We propose a
generalized modelling setup for Gaussian DTSMs which allows for unspanned
nonlinear associations between the two and we exploit it in forecasting.
Specifically, we construct a custom sequential Monte Carlo estimation and
forecasting scheme where we introduce Gaussian Process priors to model
nonlinearities. Sequential scheme we propose can also be used with dynamic
portfolio optimization to assess the potential of generated economic value to
investors. The methodology is presented using US Treasury data and selected
macroeconomic indices. Namely, we look at core inflation and real economic
activity. We contrast the results obtained from the nonlinear model with those
stemming from an application of a linear model. Unlike for real economic
activity, in case of core inflation we find that, compared to linear models,
application of nonlinear models leads to statistically significant gains in
economic value across considered maturities.
