Agricultural Economics Department


First Advisor

Fabio L. Mattos

Date of this Version

Summer 8-2023


Dahal, S. 2023. "Exploring the Presence of Nonlinear Deterministic Dynamics in Commodity Prices." Master's Thesis. Department of Agricultural Economics, University of Nebraska-Lincoln.


A THESIS Presented to the Faculty of The Graduate College at the University of Nebraska In Partial Fulfillment of Requirements For the Degree of Master of Science, Major: Agricultural Economics, Under the Supervision of Professor Fabio L. Mattos. Lincoln, Nebraska: August 2023

Copyright © 2023 Sagar Dahal


Determining whether commodity prices (and volatility) are driven by linear stochastic processes or low-dimensional nonlinear deterministic dynamics (“chaos”) is crucial for policymaking, forecasting, production, storage, investment, risk management, and hedging decisions. Previous studies that used Lyapunov exponents and correlation dimensions to identify chaotic structures in price series may be unreliable in practical applications because these methods rely on asymptotic properties that require large, noiseless data which is often not available. We applied nonlinear time series analysis approaches to empirically detect the underlying market dynamics using the daily futures prices of ten agricultural commodities. We used phase space reconstruction to reconstruct the empirical attractor from the observed price series, as well as nonlinear predictive skill and permutation entropy measures to distinguish between linear stochastic and nonlinear deterministic dynamics. We find evidence of low-dimensional nonlinear deterministic dynamics in commodity price series. Our results suggest that the observed volatility is most likely endogenously generated by an inherently unstable market which cannot be expected to self-correct, suggesting the need for government intervention to stabilize prices and reduce volatility. It also suggests that long-term forecasts are unlikely due to the nature of the dynamics, but short-term forecasts may be improved using nonlinear prediction methods.

Advisor: Fabio L. Mattos