Analyzing Commodity Futures Using Factor State-Space Models with Wishart Stochastic Volatility

TL;DR

This paper introduces a factor state-space model with Wishart stochastic volatility for forecasting commodity futures, demonstrating its effectiveness on crude oil contracts with strong in-sample fit and out-of-sample predictive accuracy.

Contribution

It develops a novel Bayesian factor model with Wishart stochastic volatility for commodity futures, extending existing Nelson-Siegel and Svensson models with efficient MCMC inference.

Findings

The 4-factor Svensson model captures serial correlation and volatility well.

The model achieves strong out-of-sample forecast performance.

It provides a computationally efficient Bayesian estimation method.

Abstract

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.