Deja vu all over again \u2013 China retaliates, and again soybeans are on the list.<\/p>\n
Also a lot of other food items.<\/p>\n Some people (the now banned CoRev) argued against the usefulness of futures<\/a>, despite Chinn and \u00a0Olivier Coibion (Journal of Futures Markets, 2014<\/a>), in which we evaluate \u2014 among other commodities \u2014 soybean prices.\u00a0V. Fernandez (Resources Policy, 2017)\u00a0<\/a> conducted an update of our work, confirming our results.<\/p>\n To see our results, consider estimating the following equation, using OLS.<\/p>\n st+k<\/sub>\u00a0\u2013 st<\/sub>\u00a0= \u03b1 + \u03b2<\/i>(ft,k<\/sub>\u00a0\u2013 st<\/sub><\/i>)\u00a0+ ut+k<\/sub><\/i><\/p>\n Where\u00a0st<\/sub><\/i>\u00a0is the log spot rate at time t,\u00a0ft,k<\/sub><\/i>\u00a0is the log futures rate for a transaction k periods hence, and\u00a0u<\/i>\u00a0is an error term that is under the efficient markets hypothesis null a random expectations error (an innovation).<\/p>\n In Table I (from\u00a0Chinn and Coibion, 2014<\/a>), I highlight in yellow the SOYBEAN results of this regression for soybeans, at the 3, 6 and 12 month horizons.<\/p>\n Notice the point estimates are close to one, and statistically indistinguishable from that value, using standard errors robust to serial correlation and heteroskedasticity. Hence, the testing approach is conservative. A Wald test for the joint null \u03b1=0, \u03b2=1 is not rejected at conventional levels. That null hypothesis is consistent with the futures price being an unbiased predictor. In words, the results mean when the basis is 1%, the average change in the soybean price over the corresponding period will be\u20261%.<\/p>\n Interestingly, the R2<\/sup>\u2019s are high for soybeans. In contrast, similar results are\u00a0not<\/b>\u00a0obtained for metal commodities. In particular, the estimated \u03b2\u2019s are often negative. Hence, we can conclude that futures are unbiased predictors of future spot soybean prices for horizons of up to a year, and have measurable predictive power. For more on prediction (warning: one needs to know at a minimum what an ARIMA is, better yet to know the characteristics of a DMW statistic, in order to understand the content), see\u00a0this post<\/a>.<\/p>\n<\/p><\/div>\n![\"\"](\"https:\/\/econbrowser.com\/wp-content\/uploads\/2025\/03\/soybeans_4mar25.png\")
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Table I<\/b>\u00a0from\u00a0Chinn and Coibion, 2014<\/a>.<\/small><\/p>\n