Date of Award
8-2026
Degree Type
Doctoral Dissertation
Degree Name
Doctor of Business Administration (DBA)
Department
Jack Welch College of Business & Technology
Dissertation Supervisor
Dr. W. Keener Hughen
Committee Member
Dr. Loran Chollete
Committee Member
Kwamie Dunbar
Abstract
This study evaluates the implied volatility term structure (IVTS) slope (𝜅) for forecasting 21-day realized volatility against traditional time-series benchmarks and a 3-month continuous futures baseline. Using a matched sample of eight energy and agricultural commodities spanning 2007 to 2019, the study explicitly tests whether the option-implied slope contains unique predictive information beyond deferred price histories. The empirical findings yield three primary insights. First, redundancy testing indicates that the slope parameter delivers a statistically significant, independent risk signal even when controlling for deferred continuous futures paths. Second, an integrated joint encompassing framework that combines near-term historical volatility, generalized autoregressive conditional heteroskedasticity (GARCH) persistence, and the term-structure slope performs best out of sample for most of the portfolio, improving predictive accuracy over standard GARCH benchmarks by 75% to 89%. A formal two-factor stochastic volatility extension in the crude oil market provides additional structural validation of this predictive association. Third, by applying a robust specification to filter nonlinear processing noise in the soybean crush spread, the model successfully isolates the term-structure signal, achieving a 67% forecast improvement in that specific market. Overall, these findings indicate that the implied volatility slope provides unique forward-looking information, equipping risk managers to systematically optimize capital efficiency and minimize margin requirements.
JEL Classification
G13, G17, Q02, C53, C58
Recommended Citation
McKenzie, L.E. (2026). The information content of the implied volatility term structure slope for forecasting commodity market volatility. Jack Welch College of Business & Technology dissertation, Sacred Heart University, Fairfield CT. Retrieved from https://digitalcommons.sacredheart.edu/wcob_theses/53/
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Included in
Business Administration, Management, and Operations Commons, Corporate Finance Commons, Finance and Financial Management Commons
Comments
Submitted as partial fulfillment of the requirements for the degree of Doctor of Business Administration in Finance Sacred Heart University, Jack Welch College of Business and Technology, Sacred Heart University.
DISSERTATION Number DBA03/2026