  We propose a factor state-space approach with stochastic volatility to model
and forecast the term structure of future contracts on commodities. Our
approach builds upon the dynamic 3-factor Nelson-Siegel model and its 4-factor
Svensson extension and assumes for the latent level, slope and curvature
factors a Gaussian vector autoregression with a multivariate Wishart stochastic
volatility process. Exploiting the conjugacy of the Wishart and the Gaussian
distribution, we develop a computationally fast and easy to implement MCMC
algorithm for the Bayesian posterior analysis. An empirical application to
daily prices for contracts on crude oil with stipulated delivery dates ranging
from one to 24 months ahead show that the estimated 4-factor Svensson model
with two curvature factors provides a good parsimonious representation of the
serial correlation in the individual prices and their volatility. It also shows
that this model has a good out-of-sample forecast performance.
