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

European Journal of Finance

Volumn 21, Issue 4, 2015, Pages 352-375

  Dunis, Christian L ^a   Laws, Jason ^b   Middleton, Peter W ^b   Karathanasopoulos, Andreas ^c  

^a LIVERPOOL JOHN MOORES UNIVERSITY   (United Kingdom)

^b UNIVERSITY OF LIVERPOOL MANAGEMENT SCHOOL   (United Kingdom)

^c LONDON METROPOLITAN UNIVERSITY   (United Kingdom)

Author keywords

corn futures; ethanol futures; genetic programming algorithm; higher order neural network; leveraging; multilayer perceptron neural network; RiskMetrics; spread trading; time varying leverage

Indexed keywords

EID: 84924082209     PISSN: 1351847X     EISSN: 14664364     Source Type: Journal    
DOI: 10.1080/1351847X.2013.830140     Document Type: Article

References (34)

1. Ang, A., S. Gorovyy, and G. B. Van Inwegen. 2010. Hedge Fund Leverage. Working Paper. Columbia University.
2. Appel, G. 1979. The Moving Average Convergence-Divergence Method. Great Neck, NY: Signalert.
3. Aranha, C. C., and H. Iba 2008. “A Tree-Based GA Representation for the Portfolio Optimization Problem.” In Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation, GECCO ’08, Atlanta, GA, USA, July 12–16, 2008, edited by M. Keijzer, 873–880. New York, NY: ACM.
4. Brinker, A. J., J. L. Parcell, K. C. Dhuyvetter, and J. Franken 2009. “Cross-hedging Distillers Dried Grains: Corn and Soybean Meal Futures Contracts.” Journal of Agribusiness 27 (1/2): 1–15.
5. Butterworth, D., and P. Holmes 2002. “Inter-market Spread Trading: Evidence from UK Index Futures Markets.” Applied Financial Economics 12 (11): 783–791. doi: 10.1080/09603100110044236
6. CBOT. 2007. “Ethanol Futures – Corn Crush Reference Guide.” Accessed April, 2010. http://www.cbot.com/cbot/docs/73511.pdf
7. CME. 2010. “Commodity Products – Trading the Corn for Ethanol Crush.” Accessed April, 2010. http://www.cmegroup.com/trading/agricultural/files/AC-406_DDG_CornCrush_042010.pdf
8. Dahlgran, R. A. 2009. “Inventory and Transformation Hedging Effectiveness in Corn Crushing.” Journal of Agricultural and Resource Economics 34 (1): 154–171.
9. Dunis, C. L., J. Laws, and B. Evans 2006. “Modelling and Trading the Soybean-Oil Crush Spread with Recurrent and Higher Order Networks: A Comparative Analysis.” Neural Network World 13 (3/6): 193–213.
10. Dunis, C. L., J. Laws, and P. W. Middleton. 2011. Modelling and Trading the Corn/Ethanol Crush Spread with Neural Networks. CIBEF Working Paper. Liverpool Business School. www.cibef.com
11. Dunis, C. L., and J. Miao 2006. “Volatility Filters for Asset Management: An Application to Managed Futures.” Journal of Asset Management 7/3 (4): 179–189. doi: 10.1057/palgrave.jam.2240212
12. Dunis, C. L., G. Sermpinis, and A. Karathanasopoulos. 2011. Forecasting and Trading the EUR/USD Exchange Rate with Gene Expression and Psi Sigma Neural Networks. CIBEF Working Paper. Liverpool Business School. www.cibef.com
13. Engle, R. F., and C. W. Granger 1987. “Co-integration and Error-Correction: Representation, Estimation and Testing.” Econometrica 55 (2): 251–276. doi: 10.2307/1913236
14. Ferreira, C. 2006. Gene Expression Programming: Mathematical Modelling by an Artificial Intelligence. San Francisco, CA: Springer.
15. Franken, J. R. V., and J. L. Parcell 2003. “Cash Ethanol Cross-hedging Opportunities”. Journal of Agricultural and Applied Economics 35 (3): 1–19.
16. Fulcher, J., M. Zhang, and S. Xu 2006. The Application of Higher-Order Neural Networks to Financial Time Series. Artificial Neural Networks in Finance and Manufacturing. Hershey, PA: Idea Group, London.
17. Funk, S. M., J. E. Zook, and A. M. Featherstone 2008. “Chicago Board of Trade Ethanol Contract Efficiency.” Selected paper prepared for a presentation at the Southern Agricultural Economics Association Annual Meeting, Dallas, TX, February 2–6. http://ageconsearch.umm/bitstream/6811/2/sp08fu01.pdf.
18. Gallagher, P. W. 2009. “Roles for Evolving Markets, Policies, and Technology, Improvements in U.S. Corn Ethanol Industry Development.” Federal Reserve Bank of St. Louis Regional Economic Development 5 (1): 12–33.
19. Giles, L., and T. Maxwell 1987. “Learning, Invariance and Generalization in Higher Order Neural Networks.” Applied Optics 26 (23): 4972–4978. doi: 10.1364/AO.26.004972
20. Holland, J. 1975. Adaptation in Natural and Artificial Systems. Ann Arbor: University of Michigan Press.
21. Iba, H. 1999. “Bagging, Boosting and Bloating in Genetic Programming.” InProceedings of the Genetic and Evolutionary Computation Conference (GECCO’99), edited by Wolfgang Banzhaf, Jason Daida, Agoston E Eiben, Max H. Garzon, Vasant Honavar, Mark Jakiela and Robert E. Smith, 1053–1060. San Francisco, CA: Morgan Kaufmann.
22. Karayiannis, N., and A. Venetsanopoulos 1994. “On the Training and Performance of High-Order Neural Networks.” Mathematical Biosciences 129 (2): 143–168. doi: 10.1016/0025-5564(94)00057-7
23. King, K., and C. Zulauf 2010. “Are New Crop Futures and Option Prices for Corn and Soybeans Biased? An Updated Appraisal.” Paper presented at the NCCC-134 Conference on Applied Commodity Price Analysis, Forecasting, and Market Risk Management, St. Louis, MO, 19–20.
24. Knowles, A., A. Hussein, W. Deredy, P. Lisboa, and C. L. Dunis. 2005. Higher-Order Neural Networks with Bayesian Confidence Measure for Prediction of EUR/USD Exchange Rate. Artificial Higher Order Neural Networks for Economic and Business, 1, 48–59. CIBEF Working Papers. http://www.cibef.com
25. Koza, J. R. 1992. Genetic Programming: On the Programming of Computers by Means of Natural Selection. Cambridge, MA: MIT Press.
26. Koza, J. R. 1998. “Genetic Programming.” In Encyclopedia of Computer Science and Technology, edited by J. G. Williams and A. Kent, 29–43. New York: Marcel-Dekker
27. Lan, Yingcong, Neng Wang, and Jinqiang Yang. 2011. The Economics of Hedge Funds: Alpha, Fees, Leverage, and Valuation. Working Paper. Columbia University and Hunan University.
28. Li, J., and E. P. K. Tsang 1999. “Improving Technical Analysis Predictions: An Application of Genetic Programming.” Proceedings of the 12th International Florida AI Research Society Conference, Orlando, FL, 108–112.
29. McKay, P. A. 2006. “Demand for Ethanol Aggravates Pain at the Pump.” Wall Street Journal, Eastern Edition, June 19, C4.
30. Morgan, J. P. 1997. Risk Metrics Technical Document. New York: Morgan Guaranty Trust.
31. Neely, J, Christopher, Paul A. Weller, and Robert Dittmar 1997. “Is Technical Analysis Profitable in the Foreign Exchange Market? A Genetic Programming Approach.” Journal of Financial and Quantitative Analysis 32 (4): 405–436. doi: 10.2307/2331231
32. RFA (Renewable Fuels Association). 2011. Building Bridges to a More Sustainable Future: 2011 Ethanol Industry Outlook. Washington, DC: RFA, pp. 26.
33. Sopena, J. M., E. Romero, and R. Alquezar 1999. “Neural Networks with Periodic and Monotonic Activation Functions: A Comparative Study in Classification Problems.” Proceedings of 9th International Conference on Artificial Neural Networks, Edinburgh, Scotland. Vol. 1, 323–328.
34. Zhang, G. P., and M. Qi 2005. “Neural Network Forecasting for Seasonal and Trend Time Series.” European Journal of Operational Research 160 (2): 501–514. doi: 10.1016/j.ejor.2003.08.037