Corn Cash Price Forecasting

Abstract

We examine the forecasting problem in a data set of daily corn cash prices from seven states: Iowa, Illinois, Indiana, Ohio, Minnesota, Nebraska, and Kansas. We assess thirty individual time series models and ten combined forecasts based on six trimming strategies across three out‐of‐time evaluation periods, seven horizons, and two systems (bi‐ and multivariate). Using the unrestricted least squares without an intercept to estimate combination weights of individual models without trimming arrives at the lowest root mean squared errors across all evaluation dimensions. Incorporating local cash prices in a model could benefit accuracy, especially for relatively longer out‐of‐time evaluation periods and forecast horizons. Our results suggest model recalibration frequency no lower than one month. Discussions of empirical findings at a more granular level also are presented, including comparisons of individual time series models and those of combined forecasts based on different trimming strategies. The forecasting framework shown here is not difficult to implement and has potential of generalizing to other commodities. This article thus contributes to fulfilling different forecasting users' information needs for decision making under various circumstances.

Commodity price forecasting via neural networks for coffee, corn, cotton, oats, soybeans, soybean oil, sugar, and wheat

“…Due to high price volatilities (Timmermann, 2006), significant influences on decision‐making, and hence on resource allocation and economic welfare, the importance of commodity price forecasting to the agricultural sector is evident. Previous research (Bessler et al, 2003; Xu, 2020; Yang et al, 2021) concentrates on a wide variety of econometric approaches, commercial services, and expert forecasts. Common econometric models for commodity price forecasting include the autoregressive moving average, vector autoregressive, and vector error correction models.…”

The Prediction of Gold Futures Prices at the Shanghai Futures Exchange Based on the MEEMD-CS-Elman Model

“…Because futures price has the characteristics of nonlinearity, chaos, and a long history (Wu & Duan, 2017), many previous studies have focused on comparing the accuracy of various models in price forecasting. Generally, time-series models and their combined approaches are widely used in the analysis and prediction of prices (Batten & Lucey, 2010;Xu, 2017Xu, , 2018Xu, , 2019aXu, , 2019bXu, , 2020. Compared with traditional methods, however, intelligent computing models, which are better and more powerful tools to deal with such problems, have been proposed to predict the price.…”

“…Autoregression moving average (ARMA) models, a mixture of autoregression (AR) and moving average (MA), are quite common. They have the characteristics of wide application and small prediction error, despite they might ignore other relevant information that is important to predict (Gülerce & Ünal, 2017;Xu, 2017Xu, , 2018Xu, , 2020. The vector autoregression (VAR) and vector error correction models (VECMs) cover these kinds of information (Baumeister & Kilian, 2012), but the choices of modeling strategies are vague (Allen & Fildes, 2005).…”

“…Comparatively, the combination models are superior to the individual models. They describe the change rule of timeseries data better and diversify against the uncertainty (Xu, 2020). Combined models contain the previous best forecast, equal-weighted average, inverse mean-squared error, leastsquares estimates of combination weights, bias-adjusted mean, shrinkage, and odds matrix approach (Capistrán & Timmermann, 2009).…”

“…Combined models contain the previous best forecast, equal-weighted average, inverse mean-squared error, leastsquares estimates of combination weights, bias-adjusted mean, shrinkage, and odds matrix approach (Capistrán & Timmermann, 2009). In practice, Xu has used the above seven models plus three variations of the least-squares estimates of combination weights method and two of the shrinkage to forecast the corn cash prices (Granger & Ramanathan, 1984;Xu, 2020).…”