Price volatility spillovers between Canadian and US, agricultural and fuel markets

Abstract

A good understanding of risk transmission between agricultural and energy industries can be important to the sustainability of agriculture and the development of renewable energy industries. Literature has shown that the volatility of international agricultural and energy markets has mutual influences, and that these influences may change over time and space. This analysis adds value to the literature by identifying whether, and to what extent, price and volatility spillovers exist between the Canadian wheat market and US corn and energy markets (i.e. gasoline and ethanol). Though many studies have investigated pass-through between corn and energy markets, the addition of wheat markets in Canada is of interest because of the increasing use of wheat in producing first generation ethanol, and the potential for second generation ethanol that could use Canadian wheat straw as a feedstock. Our investigation adopts a modified vector error-correction model with a multivariate generalized autoregressive conditional heteroskedasticity framework. Key results include: (1) wheat and ethanol prices adjust to a deviation in the equilibrium relationship with corn and gasoline prices; (2) price volatility in the wheat market is only affected by its own shocks, not by the spillovers from US corn, ethanol or gasoline markets; (3) corn price volatility is not affected by shocks to wheat, ethanol, or gasoline markets. However, corn price volatility is largely affected by the covariance between wheat and corn. This information is useful for developing business strategies, as well as for policy makers interested in the development of a second-generation wheat straw-based ethanol industry in Canada. Our results also improve the understanding of impacts of US energy policies on Canada.

Keywords:

• Price volatility
• spillovers
• Canadian wheat
• energy and agriculture
• ethanol

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1 The Berndt-Hall-Hall-Hausman (BHHH) algorithm is used to estimate the BEKK-MGARCH system.

2 The r dimension represents the number of cointegrating vectors.

3 An ARCH-LM test is performed on the residuals, and the test fails to reject the null hypothesis that there is no serial correlation.

4 The other three terms associated with ARCH coefficients, $2A_{11}{A}_{2,1},$ $2A_{11}{A}_{3,1},$ and $2A_{11}{A}_{4,1}$ are difficult to interpret and have little economic meaning.

Additional information

Funding

This work was supported by Canada First Research Excellence Fund (grant identification number CFREF-2015-00001); University of Alberta Future Energy Systems; and Alberta Biojet Initiative (grant identification number RES0045103).