Estimation of a nonparametric model for bond prices from cross-section and time series information

Abstract

We develop estimation methodology for an additive nonparametric panel model that is suitable for capturing the pricing of coupon-paying government bonds followed over many time periods. We use our model to estimate the discount function and yield curve of nominally riskless government bonds. The novelty of our approach is the combination of two different techniques: cross-sectional nonparametric methods and kernel estimation for time varying dynamics in the time series context. The resulting estimator is used for predicting individual bond prices given the full schedule of their future payments. In addition, it is able to capture the yield curve shapes and dynamics commonly observed in the fixed income markets. We establish the consistency, the rate of convergence, and the asymptotic normality of the proposed estimator. A Monte Carlo exercise illustrates the good performance of the method under different scenarios. We apply our methodology to the daily CRSP bond market dataset, and compare ours with the popular Diebold and Li (2006) method.

Simultaneous Sieve Inference for Time-Inhomogeneous Nonlinear Time Series Regression

“…In many scientific endeavors, researchers are interested in understanding the specific functional form of a relationship between variables. To name but a few, the estimation of household Engle curves [2], the estimation of demand functions [4], the estimation of an asset's price [17,25,39], the understanding of the functional relation between genetics and diseases [62] and the relationship between social and economic factors in social studies [50]. There are usually two main challenges associated with these applications.…”