A Computational Approach to Affine Models of the Term Structure

This dissertation makes contributions to the term structure modeling literature by examining the role of observed macroeconomic data in the pricing kernel and providing a single computational framework for building and estimating a range of affine term structure models. Chapter 2 attempts to replicate and extend the model of Bernanke et al. (2005), finding that proxies for uncertainty, particularly for practitioner disagreement and stock volatility, lower the pricing error of models estimated only with observed macroeconomic information. The term premia generated by models including the proxies produce term premia that are higher during recessions, suggesting that these proxies for uncertainty represent information that is of particular value to bond market agents during crisis periods. Chapter 3 finds that a real-time data specified pricing kernel produces lower average pricing errors compared to analogous models estimated using final release data. Comparisons between final release and real-time data driven models are performed by estimating observed factor models with two, three, and four factors. The real-time data driven models generate more volatile term premia for shorter maturity yields, a result not found in final data driven models. This suggests that the use of real-time over final release data has implications for model performance and term premia estimation. Chapter 4 presents a unified computational framework written in the Python programming language for estimating discrete-time affine term structure models, supporting the major canonical approaches. The chapter also documents the use of the package, the solution methods and approaches directly supported, and development issues encountered when writing C-language extensions for Python packages. The package gives researchers a flexible interface that admits a wide variety of affine term structure specifications.