Forecasting International Sugar Prices: A Bayesian Model Average Analysis

Abstract

AbstractThis paper examines the relative importance of key variables for the prediction of international sugar prices. Understanding movements in world sugar prices helps policy-makers and participants in the sugar value chain to formulate effective investment strategies and forecast the effects of market shocks more accurately. We combine a Bayesian model averaging (BMA) technique to address specification uncertainty with an out-of-sample analysis to evaluate price predictability. Results show that world sugar quotations are mostly influenced by their own dynamics, changes in international staple food prices, sugar production costs, and macroeconomic variables. The predictability of the BMA is found to be generally high, compared with a sample of benchmark time series approaches.