Bayesian Extensions to Diebold-li Term Structure Model

In this article we propose a statistical model to adjust, interpolate and forecast the term structure of interest rates. This model is based on extensions for the term structure model of interest rates proposed by [Diebold & Li, 2006], through a Bayesian estimation using Markov Chain Monte Carlo. The proposed extensions involve the use of a more flexible parametric form for the yield curve, making all parameters time-varying using a structure of latent factors, and adding a stochastic volatility structure to control the presence of conditional heteroscedasticity observed in the interest rates. The Bayesian estimation enables the exact distribution of estimators in finite samples, and as a sub product, the estimation enables obtaining the distribution of forecasts for the term structure of interest rates. The methodology developed does not need a pre-interpolation of the yield curve as it happens in some econometric models of term structure. We do an empirical exercise of this methodology in which we adjust daily data of the term structure of interest rates implicit in Swap DI-PRÉ contracts traded in the Mercantile and Futures Exchange (BM&F)