Multi-Factor Function-on-Function Regression of Bond Yields on WTI Commodity Futures Term Structure Dynamics

TL;DR

This paper introduces a novel state-space functional regression model that captures the relationship between WTI crude oil futures and US Treasury yields, improving accuracy over traditional models and analyzing shock impacts.

Contribution

The study develops a new functional regression framework incorporating yield curve dynamics, enhancing modeling of commodity futures and bond yield interdependencies.

Findings

Model outperforms Schwartz-Smith two-factor model in short-term futures estimation.

Functional regression captures yield curve influences more effectively.

Stress testing reveals sensitivity of futures prices to yield shocks.

Abstract

In the analysis of commodity futures, it is commonly assumed that futures prices are driven by two latent factors: short-term fluctuations and long-term equilibrium price levels. In this study, we extend this framework by introducing a novel state-space functional regression model that incorporates yield curve dynamics. Our model offers a distinct advantage in capturing the interdependencies between commodity futures and the yield curve. Through a comprehensive empirical analysis of WTI crude oil futures, using US Treasury yields as a functional predictor, we demonstrate the superior accuracy of the functional regression model compared to the Schwartz-Smith two-factor model, particularly in estimating the short-end of the futures curve. Additionally, we conduct a stress testing analysis to examine the impact of both temporary and permanent shocks to US Treasury yields on futures price estimation.