Agricultural commodity price dynamics and their determinants: A comprehensive econometric approach

Abstract

We present a comprehensive modelling framework aimed at quantifying the response of agricultural commodity prices to changes in their potential determinants. The problem of model uncertainty is assessed explicitly by concentrating on specification selection based on the quality of short-term outof-sample forecasts (1 to 12 months ahead) for the price of wheat, soybeans and corn. Univariate and multivariate autoregressive models (autoregressive [AR], vector autoregressive [VAR] and vector error correction [VEC] specifications, estimated using frequentist and Bayesian methods), specifications with heteroskedastic errors (AR conditional heteroskedastic [ARCH] and generalized AR conditional heteroskedastic [GARCH] models) and combinations of these are entertained, including information about market fundamentals, macroeconomic and financial developments, and climatic variables. In addition, we assess potential non-linearities in the commodity price dynamics along the business cycle. Our results indicate that variables measuring market fundamentals and macroeconomic developments (and, to a lesser extent, financial developments) contain systematic predictive information for out-of-sample forecasting of commodity prices and that agricultural commodity prices react robustly to shocks in international competitiveness, as measured by changes in the real exchange rate.

Forecasting Selected Commodities’ Prices with the Bayesian Symbolic Regression

“…Following, for instance, Cuaresma et al (2018Cuaresma et al ( , 2021, leading indicators were used (CLI, amplitude adjusted, except for China, for which the normalised index was taken due to data availability). The U.S., G7, Euro area, and China were considered (OECD 2022).…”

“…The selection of countries was made with a focus on the largest exporters and importers of commodities and to include so-called "commodities currencies". Indeed, according to the WTO (2022), amongst the largest commodities exporters and importers in 2020 and 2019 were Australia, Brazil, Canada, China, Germany, India, Japan, Russia, the United Arab Emirates, and the U.S. A similar set of variables was used by Cuaresma et al (2018Cuaresma et al ( , 2021, Gargano and Timmermann (2014), Chen et al (2010), Clements andFry (2008), andCashin et al (2004). These variables were transformed into logarithmic differences.…”

Yellow corn wholesale price forecasts via the neural network

“…For example, Zhou (2021) used the ARIMA for modeling monthly corn prices in China during April 2019–February 2021 and forecasting the price in March 2021, and obtained good accuracy. Crespo Cuaresma, Hlouskova and Obersteiner (2021) studied auto-regressive models, VARs, VECMs and their variations and combinations for forecasts of different agricultural commodity prices that include those of corn. They found that market fundamentals and macroeconomic developments contribute systematic predictive information for the forecast purpose.…”

Using a machine learning approach and big data to augment WASDE forecasts: Empirical evidence from US corn yield

“…Crop yield forecasts help the global supply chain become more efficient in meeting food demand and improving food security (Ahumada & Cornejo, 2016). Crop yield forecasts are essential to many market participants and provide informational value for commodity markets (Crespo Cuaresma et al, 2021) and may also be a valuable input for price forecasting (Ahumada & Cornejo, 2016;Crespo Cuaresma et al, 2018Gargano & Timmermann, 2014). The US Department of Agriculture (USDA) has two of 13 federal government forecasting agencies: the National Agricultural Statistics Service (NASS) and the Economic Research Service (ERS).…”