Forecasting the Term Structure of Interest Rates: A Bayesian Dynamic Graphical Modeling Approach
This thesis addresses the financial econometric problem of forecasting the term structure of interest rates by using classes of Dynamic Dependence Network Models (DDNMs). This Bayesian econometric framework defines structured dynamic graphical models for multivariate time series that utilize a hierarchical, contemporaneous dependence structure across series augmented with time-varying autoregressive components. Using yield and macroeconomic data from the post-Volcker era, various such models are explored and evaluated. On the basis of economic reasoning and empirical statistical evaluations, we specify an interpretable model which outperforms a standard time-varying vector autoregressive model in forecast accuracy particularly at longer horizons relevant for economic policy considerations. In particular, the chosen model reduces forecast error metrics and produces stable forecast trajectories for yields on U.S. Treasuries up to 12 months ahead. The out-of-sample performance of the DDNM is robust to changes in model specification, hyper-parameter choices, and exogenous macroeconomic information sets. The analysis highlights the utility of this class of models and suggests next steps in research and development in this area of Bayesian macroeconomics.
Dynamic graphical modeling
Term structure of interest rates
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