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Financial Markets and Portfolio Management

, Volume 33, Issue 2, pp 155–181 | Cite as

Price dynamics in corn cash and futures markets: cointegration, causality, and forecasting through a rolling window approach

  • Xiaojie XuEmail author
Article
  • 17 Downloads

Abstract

This paper examines the causal structure among the daily corn futures and seven cash price series from Midwestern states from January 3, 2006, to March 24, 2011, through a rolling approach that takes into account window sizes of a half, one, one and a half, and two years. Except for some testing samples, all series are tied together through cointegration and adjust toward the long-run relationship(s). Considering different forecasting lengths, the out-of-sample Granger causality test for each window generally reveals that no series gains persistent forecastability from another. These results shed light on the evolving causal structure among the different series. Discussions of empirical findings at a more granular level also are presented.

Keywords

Cash Futures Cointegration Causality Forecasting 

JEL Classification

C32 Q11 

Notes

Acknowledgements

The author acknowledges Kevin McNew and Geograin, Inc of Bozeman, Montana, for generously providing the data used in the analysis in this paper. The author thanks two anonymous referees and Markus Schmid (editor) for their helpful comments.

References

  1. Amato, J.D., Swanson, N.R.: The real-time predictive content of money for output. J. Monet. Econ, 48, 3–24 (2001).  https://doi.org/10.1016/s0304-3932(01)00070-8 CrossRefGoogle Scholar
  2. Ashley, R.A., Tsang, K.P.: Credible Granger-causality Inference with modest sample lengths: a cross-sample validation approach. Econometrics 2, 72–91 (2014).  https://doi.org/10.3390/econometrics2010072 CrossRefGoogle Scholar
  3. Bessler, D.A., Fuller, S.W.: Railroad wheat transportation markets in the central plains: modeling with error correction and directed graphs. Transport. Res. Part E: Logist. Transport. Rev. 36, 21–39 (2000).  https://doi.org/10.1016/S1366-5545(99)00015-0 CrossRefGoogle Scholar
  4. Bessler, D., Yang, J., Wongcharupan, M.: Price dynamics in the international wheat market: modeling with error correction and directed acyclic graphs. J. Reg. Sci. 43, 1–33 (2003).  https://doi.org/10.1111/1467-9787.00287 CrossRefGoogle Scholar
  5. Bossaerts, P., Hillion, P.P.: Implementing statistical criteria to select return forecasting models: what do we learn? Rev. Financ. Stud. 12, 405–428 (1999).  https://doi.org/10.1093/rfs/12.2.405 CrossRefGoogle Scholar
  6. Bierens, H.J., Martins, L.F.: Time-varying cointegration. Econom. Theory 26, 1453–1490 (2010).  https://doi.org/10.1017/S0266466609990648 CrossRefGoogle Scholar
  7. Campbell, J.Y.: Stock returns and the term structure. J. Financ. Econ. 18, 373–399 (1987).  https://doi.org/10.1016/0304-405x(87)90045-6 CrossRefGoogle Scholar
  8. Carter, C.A., Mohapatra, S.: How reliable are hog futures as forecasts? Am. J. Agric. Econ. 90, 367–378 (2008).  https://doi.org/10.1111/j.1467-8276.2007.01122.x CrossRefGoogle Scholar
  9. Chao, J., Corradi, V., Swanson, N.R.: Out-of-sample Tests for Granger causality. Macroecon. Dyn. 5, 598–620 (2001)Google Scholar
  10. Colino, E.V., Irwin, S.H.: Outlook vs. futures: three decades of evidence in hog and cattle markets. Am. J. Agric. Econ. 92, 1–15 (2010).  https://doi.org/10.1093/ajae/aap013 CrossRefGoogle Scholar
  11. Dickey, D., Fuller, W.: Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica 49, 1057–1072 (1981).  https://doi.org/10.2307/1912517 CrossRefGoogle Scholar
  12. Diebold, F.X., Mariano, R.S.: Comparing predictive accuracy. J. Bus. Econ. Stat. 13, 253–263 (1995).  https://doi.org/10.2307/1392185 Google Scholar
  13. Engle, R., Granger, C.W.J.: Co-integration and error correction: representation, estimation and testing. Econometrica 55, 251–276 (1987).  https://doi.org/10.2307/1913236 CrossRefGoogle Scholar
  14. Ferraro, D., Rogoff, K., Rossi, B.: Can oil prices forecast exchange rates? An empirical analysis of the relationship between commodity prices and exchange rates. J. Int. Money Finance 54, 116–141 (2015).  https://doi.org/10.1016/j.jimonfin.2015.03.001 CrossRefGoogle Scholar
  15. Garbade, K.D., Silber, W.L.: Price movements and price discovery in futures and cash markets. Rev. Econ. Stat. 65, 289–297 (1983).  https://doi.org/10.2307/1924495 CrossRefGoogle Scholar
  16. Garcia, P., Irwin, S.H., Smith, A.: Futures market failure? Am. J. Agric. Econ. 97, 40–64 (2014).  https://doi.org/10.1093/ajae/aau067 CrossRefGoogle Scholar
  17. Granger, C.W.J.: Investigating causal relations by econometric models and cross-spectral methods. Econometrica 37, 424–438 (1969).  https://doi.org/10.2307/1912791 CrossRefGoogle Scholar
  18. Haigh, M.S., Holt, M.T.: Hedging multiple price uncertainty in international grain trade. Am. J. Agric. Econ. 82, 881–896 (2000).  https://doi.org/10.1111/0002-9092.00088 CrossRefGoogle Scholar
  19. Harvey, D., Leybourne, S., Newbold, P.: Testing the equality of prediction mean squared errors. Int. J. Forecast. 13, 281–291 (1997).  https://doi.org/10.1016/s0169-2070(96)00719-4 CrossRefGoogle Scholar
  20. Hernandez, M., Torero, M.: Examining the dynamic relationship between spot and future prices of agricultural commodities. International Food Policy Research Institute (IFPRI) Discussion Paper 00988 (2010)Google Scholar
  21. Hill, J.B.: Efficient tests of longrun causation in trivariate VAR processes with a rolling window study of the money-income relationship. J. Appl. Econ. 22, 747–765 (2007).  https://doi.org/10.1002/jae.925 CrossRefGoogle Scholar
  22. Hoffman, L.A., Aulerich, N.: Recent Convergence Performance of Futures and Cash Prices for Corn, Soybeans, and Wheat. Economic Research Service/USDA. FDS-13L-01 (2013)Google Scholar
  23. Johansen, S.: Statistical analysis of cointegration vectors. J. Econ. Dyn. Control 12, 231–254 (1988).  https://doi.org/10.1016/0165-1889(88)90041-3 CrossRefGoogle Scholar
  24. Johansen, S.: Determination of cointegration rank in the presence of a linear trend. Oxf. Bull. Econ. Stat. 54, 383–397 (1992).  https://doi.org/10.1111/j.1468-0084.1992.tb00008.x CrossRefGoogle Scholar
  25. Karali, B., McNew, K., Thurman, W.N.: Price discovery and the basis effects of failures to converge in soft red winter wheat futures markets. J. Agric. Resour. Econ. 43, 1–17 (2018)Google Scholar
  26. Kawaller, I.G., Koch, P.D., Koch, T.W.: The relationship between the S&P 500 Index and the S&P 500 index futures prices. Fed. Reserve Bank Atlanta Econ. Rev. 73, 2–10 (1988)Google Scholar
  27. Koop, G., Leon-Gonzalez, R., Strachan, R.W.: Bayesian inference in a time varying cointegration model. J. Econom. 165, 210–220 (2011).  https://doi.org/10.1016/j.jeconom.2011.07.007 CrossRefGoogle Scholar
  28. Kroner, K.F., Sultan, J.: Time-varying distributions and dynamic hedging with foreign currency futures. J. Financ. Quantit. Anal. 28, 535–551 (1993).  https://doi.org/10.2307/2331164 CrossRefGoogle Scholar
  29. Kwiatkowski, D., Phillips, P., Schmidt, P., Shin, Y.: Testing the null hypothesis of stationarity against the alternative of a unit root: how sure are we that economic time series have a unit root? J. Econom. 54, 159–178 (1992).  https://doi.org/10.1016/0304-4076(92)90104-Y CrossRefGoogle Scholar
  30. Li, S., Thurman, W.: Grain Transport on the Mississippi River and Spatial Corn Basis. Selected Paper prepared for Presentation at the Southern Agricultural Economics Association SAEA Annual Meeting, Orlando, Florida, 35 February 2013 (2013)Google Scholar
  31. Meese, R., Rogoff, K.S.: Exchange rate models of the seventies. Do they fit out of sample? J. Int. Econ. 14, 3–24 (1983).  https://doi.org/10.1016/0022-1996(83)90017-X CrossRefGoogle Scholar
  32. Moosa, I.A.: An econometric model of price determination in the crude oil futures markets. Proc. Econom. Soc. Australas. Meet. 3, 373–402 (1996)Google Scholar
  33. National Agricultural Statistics Service. Field Crops Usual Planting and Harvesting Dates. (2010). Available at http://usda.mannlib.cornell.edu/usda/current/planting/planting-10-29-2010.pdf. Accessed 3 Sept 2014
  34. Phillips, P., Perron, P.: Testing for a unit root in time series regression. Biometrica 75, 335–346 (1988).  https://doi.org/10.2307/2336182 CrossRefGoogle Scholar
  35. Rosenberg, J.V., Traub, L.G.: Price Discovery in the Foreign Currency Futures and Spot Market. Federal Reserve Bank of New York Staff Reports, No. 262 (2006).  https://doi.org/10.2139/ssrn.922317
  36. Sarno, L., Valente, G.: Exchange rates and fundamentals: footloose or evolving relationship? J. Eur. Econ. Assoc. 7, 786–830 (2009).  https://doi.org/10.1162/jeea.2009.7.4.786 CrossRefGoogle Scholar
  37. Schroeder, T.C., Goodwin, B.K.: Price discovery and cointegration for live hogs. J. Futures Markets 11, 685–696 (1991).  https://doi.org/10.1002/fut.3990110604 CrossRefGoogle Scholar
  38. Silvapulle, P., Moosa, I.A.: The relationship between spot and futures prices: evidence from the crude oil market. J. Futures Markets 19, 175–193 (1999).  https://doi.org/10.1002/(sici)1096-9934(199904)19:2<175::aid-fut3>3.3.co;2-8
  39. Silvapulle, P.S., Podivinsky, J.M.: The effect of non-normal disturbances and conditional heteroskedasticity on multiple cointegration tests. J. Stat. Comput. Simul. 65, 173–189 (2000).  https://doi.org/10.1080/00949650008811997 CrossRefGoogle Scholar
  40. Swanson, N.R.: Money and output viewed through a rolling window. J. Monet. Econ. 41, 455–473 (1998).  https://doi.org/10.1016/S0304-3932(98)00005-1 CrossRefGoogle Scholar
  41. Swanson, N.R., White, H.: A model-selection approach to assessing the information in the term structure using linear models and artificial neural networks. J. Bus. Econ. Stat. 13, 265–275 (1995).  https://doi.org/10.2307/1392186 Google Scholar
  42. Swanson, N.R., White, H.: A model selection approach to real-time macroeconomic forecasting using linear models and artificial neural networks. Rev. Econ. Stat. 79, 540–550 (1997).  https://doi.org/10.1162/003465397557123 CrossRefGoogle Scholar
  43. Tang, C.F., Abosedra, S.: Tourism and growth in Lebanon: new evidence from bootstrap simulation and rolling causality approaches. Empir. Econ. 50, 679–696 (2016).  https://doi.org/10.1007/s00181-015-0944-9 CrossRefGoogle Scholar
  44. Tang, Y.N., Mak, S.C., Choi, D.F.S.: The causal relationship between stock index futures and cash index prices in Hong Kong. Appl. Financ. Econ. 2, 187–190 (1992).  https://doi.org/10.1080/758527099 CrossRefGoogle Scholar
  45. Wang, Z., Yang, J., Li, Q.: Interest rate linkages in the eurocurrency market: contemporaneous and out-of-sample granger causality tests. J. Int. Money Finance 26, 86–103 (2007).  https://doi.org/10.1016/j.jimonfin.2006.10.005 CrossRefGoogle Scholar
  46. Xu, X.: Price Discovery in U.S. Corn Cash and Futures Markets: The Role of Cash Market Selection. Selected Paper prepared for presentation at the Agricultural & Applied Economics Association’s 2014 AAEA Annual Meeting, Minneapolis, MN, July 27–29 (2014a)Google Scholar
  47. Xu, X.: Causality and Price Discovery in U.S. Corn Markets: An Application of Error Correction Modeling and Directed Acyclic Graphs. Selected Paper prepared for presentation at the Agricultural & Applied Economics Association’s 2014 AAEA Annual Meeting, Minneapolis, MN, July 27–29 (2014b)Google Scholar
  48. Xu, X.: Cointegration among regional corn cash prices. Econ. Bull. 35, 2581–2594 (2015)Google Scholar
  49. Xu, X.: Contemporaneous causal orderings of US Corn cash prices through directed acyclic graphs. Empir. Econ. 52, 731–758 (2017a).  https://doi.org/10.1007/s00181-016-1094-4 CrossRefGoogle Scholar
  50. Xu, X.: The rolling causal structure between the Chinese stock index and futures. Financ. Markets Portf. Manag. 31, 491–509 (2017b).  https://doi.org/10.1007/s11408-017-0299-7 CrossRefGoogle Scholar
  51. Xu, X.: Short-run price forecast performance of individual and composite models for 496 corn cash markets. J. Appl. Stat. 44, 2593–2620 (2017c).  https://doi.org/10.1080/02664763.2016.1259399 CrossRefGoogle Scholar
  52. Xu, X.: Causal structure among US Corn Futures and regional cash prices in the time and frequency domain. J. Appl. Stat. 45, 2455–2480 (2018a).  https://doi.org/10.1080/02664763.2017.1423044 CrossRefGoogle Scholar
  53. Xu, X.: Cointegration and price discovery in US Corn cash and futures markets. Empir. Econ. 55, 1889–1923 (2018b).  https://doi.org/10.1007/s00181-017-1322-6 CrossRefGoogle Scholar
  54. Xu, X.: Intraday price information flows between the CSI300 and futures market: an application of wavelet analysis. Empir. Econ. 54, 1267–1295 (2018c).  https://doi.org/10.1007/s00181-017-1245-2 CrossRefGoogle Scholar
  55. Xu, X.: Linear and nonlinear causality between corn cash and futures Prices. J. Agric. Food Ind. Organ. 16, Article 20160006 (2018d).  https://doi.org/10.1515/jafio-2016-0006
  56. Xu, X.: Using local information to improve short-run corn price forecasts. J. Agric. Food Ind. Organ. 16, Article 20170018 (2018e).  https://doi.org/10.1515/jafio-2017-0018
  57. Xu, X.: Contemporaneous and Granger causality among US Corn cash and futures prices. Eur. Rev. Agric. Econ. (forthcoming).  https://doi.org/10.1093/erae/jby036
  58. Xu, X., Thurman, W.N.: Using local information to improve short-run corn cash price forecasts. In: Proceedings of the NCCC-134 Conference on Applied Commodity Price Analysis, Forecasting, and Market Risk Management. St. Louis, MO (2015a)Google Scholar
  59. Xu, X., Thurman, W.: Forecasting local grain prices: An evaluation of composite models in 500 corn cash markets. Selected Poster prepared for presentation at the 2015 Agricultural & Applied Economics Association and Western Agricultural Economics Association Joint Annual Meeting, San Francisco, CA, July 26–28 (2015b)Google Scholar
  60. Yang, J., Bessler, D.A., Leatham, D.J.: Asset storability and price discovery in commodity futures markets: a new look. J. Futures Markets 21, 279–300 (2001).  https://doi.org/10.1002/1096-9934(200103)21:3<279::aid-fut5>3.0.co;2-l

Copyright information

© Swiss Society for Financial Market Research 2019

Authors and Affiliations

  1. 1.North Carolina State UniversityRaleighUSA

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