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Predicting Wheat Futures Prices in India

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  • Raushan Kumar

    (University of Delhi)

Abstract

Futures markets perform their economic roles of price discovery and hedging only when they are efficient. One of the important features of efficient market is that one cannot make abnormal profits from the futures markets by trading in it. This paper addresses the question of whether Indian wheat futures prices can be forecast. This would add to our knowledge whether wheat futures market is efficient, and would enable brokers, traders and speculators to develop profitable trading strategy. We employ the economic variable model to predict the wheat futures prices, and employ out of sample point forecasts. We also evaluate the robustness of our results by employing several alternative specifications, viz. ARMA process and artificial neural network technique. We then test the statistical significance of point forecast using the Diebold and Mariano test. We consider random walk orecast as the bench mark. In order to predict the evolution of wheat futures prices, we use traders’ expectations about the futures prices, a number of economic variables and futures prices (lagged) of wheat. The study finds that the futures price of wheat cannot be forecast, and the wheat futures market is efficient.

Suggested Citation

  • Raushan Kumar, 2021. "Predicting Wheat Futures Prices in India," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 28(1), pages 121-140, March.
  • Handle: RePEc:kap:apfinm:v:28:y:2021:i:1:d:10.1007_s10690-020-09320-6
    DOI: 10.1007/s10690-020-09320-6
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    More about this item

    Keywords

    Futures market; Forecasting; Artificial neural network (ANN) and agricultural derivative market;
    All these keywords.

    JEL classification:

    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • Q14 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Agricultural Finance

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