Trading and hedging the corn/ethanol crush spread using time-varying leverage and nonlinear models

Christian L. Dunis, Jason Laws, Peter W. Middleton and Andreas Karathanasopoulos

The European Journal of Finance, 2015, vol. 21, issue 4, 352-375

Abstract: In contribution to Dunis et al. [ Modelling and Trading the Corn/Ethanol Crush Spread with Neural Networks . CIBEF Working Paper. Liverpool Business School. www.cibef.com ], this investigation endeavours to expand the selection of forecasting applications by delving further into the realm of artificial intelligence and nonlinear modelling. The performances of a multilayer perceptron (MLP) neural network and higher order neural network (HONN) are gauged against a genetic programming algorithm (GPA). Further to this, a time-varying volatility filter is applied by leveraging during lower volatility regimes in order to enhance the trading performance of the spread while avoiding trading completely during times of high volatility. This paper models the corn/ethanol crush spread over a six-year period commencing on 23 March 2005 (when the ethanol futures contract was first traded on Chicago Board of Trade) through to 31 December 2010. The spread acts as a good indicator of an ethanol producer's profit margin, with corn being the principal raw ingredient used in a process called 'corn crushing' to produce ethanol as a means for alternative energy. Without leveraging, the GPA achieves the highest risk-adjusted returns followed by the HONN model. Once a time-varying leverage strategy is introduced, the ranking is maintained as GPA continues to be the most profitable model with the HONN registering the second best risk-adjusted returns, followed by the MLP neural network. On that basis, and without the benefit of hindsight as in the real world, a fund manager would have selected the GPA model regardless of whether he decides to leverage or not. Furthermore, it is also observed that the time-varying leveraging strategy significantly improves annualised returns as well as reducing maximum drawdowns, two desirable outcomes for trading and hedging.

Date: 2015
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DOI: 10.1080/1351847X.2013.830140

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