Predictability of sugar futures: evidence from the Indian commodity market

Abstract

Purpose – The forecasting power of commodity futures is a matter of intensive research as evidenced by a number of related publications. The purpose of this paper is to illustrate how advanced forecasting techniques improve the predictability of sugar futures in the Indian commodity market. Design/methodology/approach – The forward premium is estimated using ordinary least square regression technique. Different linear and nonlinear models are used to forecast the sugar future spot prices from the futures prices. The forecasting accuracy of each pair of models is then compared by estimating the corresponding Diebold-Mariano test statistics. Findings – From the estimated forward premiums, it is found that there is more volatility toward the date of maturity for a three-month horizon compared to six-month, and 12-month horizons. It is established that the futures prices of sugar, when used in a model, are able to generate better forecasts for the future spot prices. Moreover, the forecasting accuracy is found to be better for a shorter futures horizon. Research limitations/implications – The present study is restricted only to sugar. If sufficient data are available, the same study could be extended to other commodities as well. The findings imply that technical traders would benefit by using advanced forecasting techniques along with futures prices of sugar to determine the expected future spot prices. Practical implications – The findings in this paper suggest that though simple statistical models may be adopted to relate future spot prices to futures prices, more accurate prediction of the price behavior is possible with advanced forecasting methods like the artificial neural network. Social implications – The findings will help market participants such as traders to be better informed about the future spot prices and hence get a better deal. Originality/value – This is one of the first investigations to assess the predictability of commodity futures by employing advanced forecasting techniques.