A factor state-space approach with stochastic volatility is proposed for modeling and forecasting the maturity structure of future commodity contracts. The proposed 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. A computationally fast and easy to implement MCMC algorithm for the Bayesian posterior analysis is developed, which exploits the conjugacy of the Wishart and the Gaussian distribution. 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.

提出了一种具有随机波动性的因子状态空间方法，用于对未来商品合约的到期结构进行建模和预测。所提出的方法建立在动态 3 因子 Nelson-Siegel 模型及其 4 因子 Svensson 扩展之上，并假设潜在水平、斜率和曲率因子是具有多元 Wishart 随机波动过程的高斯向量自回归。开发了一种用于贝叶斯后验分析的计算快速且易于实现的 MCMC 算法，该算法利用了 Wishart 和高斯分布的共轭性。对规定交割日期为提前 1 到 24 个月的原油合约每日价格的实证应用表明，具有两个曲率因子的 4 因子 Svensson 模型提供了一个很好的简约表示单个价格和它们的序列相关性。挥发性。这也表明该模型具有良好的样本外预测性能。