Paper: arXiv 2412.05889

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.

Complexity vs Empirical Score

  • Math Complexity: 8.5/10
  • Empirical Rigor: 7.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: 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.

Research Flowchart

  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"]

Cited References (36)

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  2. Energy futures prices: term structure models with Kalman filter estimation (2002)
  3. Which risk factors drive oil futures price curves? (2020)
  4. Calibration and filtering for multi factor commodity models with seasonality: incorporating panel data from futures contracts (2013)
  5. Stochastic convenience yield implied from commodity futures and interest rates (2005)
  6. A functional time series analysis of forward curves derived from commodity futures (2020)
  7. Forecasting functional time series (2009)
  8. Functional dynamic factor models with application to yield curve forecasting (2012)
and 28 more
  1. Parameter estimation for a bidimensional partially observed Ornstein–Uhlenbeck process with biological application (2010)
  2. An N -factor Gaussian model of oil futures prices (2006)
  3. Commodity Price Forecasts, Futures Prices, and Pricing Models (2019)
  4. Modeling seasonality in agricultural commodity futures (2002)
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  7. Fundamentals of electricity derivatives (1999)
  8. Time-changed L 'e vy processes and option pricing (2004)
  9. Risk premia and L 'e vy jumps: theory and evidence (2023)
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  13. How do commodity futures respond to macroeconomic news? (2008)
  14. Seasonal effects in natural gas prices and the impact of the economic recession (2014)
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  16. The yield curve as a predictor of US recessions (1996)
  17. Predicting real growth using the yield curve (1996)
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  19. The yield curve and predicting recessions (2006)
  20. How to read the future: the yield curve, affect, and financial prediction (2009)
  21. The predictive power of the yield curve across countries and time (2015)
  22. On correlated measurement errors in the Schwartz–Smith two-factor model (2022)
  23. On Autoregressive Measurement Errors in a Two-Factor Model (2024)
  24. Multi-Factor Polynomial Diffusion Models and Inter-Temporal Futures Dynamics — → 2409.19386
  25. Nonlinear component analysis as a kernel eigenvalue problem (1998)
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  28. Kernel PCA for novelty detection (2007)

1 of 36 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.