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

ArXiv ID: 2412.05889 “View on arXiv”

Authors: Unknown

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.

Keywords: State-Space Functional Regression, Yield Curve Dynamics, Schwartz-Smith Model, Factor Model, Stress Testing, Commodities (Futures)

Complexity vs Empirical Score

• Math Complexity: 8.5/10
• Empirical Rigor: 7.0/10
• Quadrant: Holy Grail
• Why: The paper presents a novel state-space functional regression model with extensive mathematical formulations (multi-factor OU processes, risk-neutral measures, Kalman filtering, kernel PCA) and provides a comprehensive empirical analysis including WTI futures data, comparative benchmarking against the Schwartz-Smith model, and stress testing scenarios.

  flowchart TD
    A["Research Goal: Model commodity futures yield curve<br/>using functional regression with yield curve predictors"] --> B["Data: WTI Crude Oil Futures & US Treasury Yields"]
    B --> C["Methodology: State-Space Functional Regression Model"]
    C --> D["Computational Process:<br/>Model Estimation & Comparison vs. Schwartz-Smith Model"]
    D --> E["Key Finding 1: Superior accuracy in short-end estimation"]
    D --> F["Key Finding 2: Functional model captures interdependencies"]
    D --> G["Analysis: Stress Testing for Treasury Yield Shocks"]