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Price discovery and volatility spillover: an empirical evidence from spot and futures agricultural commodity markets in India

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  • Manogna R L
  • Aswini Kumar Mishra

Abstract

Purpose - Price discovery and spillover effect are prominent indicators in the commodity futures market to protect the interest of consumers, farmers and to hedge sharp price fluctuations. The purpose of this paper is to investigate empirically the price discovery and volatility spillover in Indian agriculture spot and futures commodity markets. Design/methodology/approach - This study uses Granger causality, vector error correction model (VECM) and exponential generalized autoregressive conditional heteroskedasticity (EGARCH) to examines the price discovery and spillover effects for nine most liquid agricultural commodities in spot and futures markets traded on National Commodity and Derivatives Exchange (NCDEX). Findings - The VECM results show that price discovery exists in all the nine commodities with futures market leading the spot in case of six commodities, namely soybean seed, coriander, turmeric, castor seed, guar seed and chana. Whereas in case of three commodities (cotton seed, rape mustard seed and jeera), price discovery takes place in the spot market. The Granger causality tests indicate that futures markets have stronger ability to predict spot prices. Supporting these, the results from EGARCH volatility test reveal that there exist mutual spillover effects on futures and spot markets. Thus, it could be inferred that futures market is more efficient in price discovery of agricultural commodities in India. Research limitations/implications - These results can help the market participants to benefit by hedging out the uncertainty and the policymakers to design futures contracts to improve the efficiency of the agricultural commodity derivatives market. Practical implications - The findings provide fresh view on lead–lag relationship between future and spot prices using the latest data confirming that futures market indeed is dominant in price discovery. Originality/value - There are very few studies that have explored the efficiency of the agricultural commodity spot and futures markets in India using both price discovery and volatility spillover in a detailed manner, especially at the individual agriculture commodity level.

Suggested Citation

  • Manogna R L & Aswini Kumar Mishra, 2020. "Price discovery and volatility spillover: an empirical evidence from spot and futures agricultural commodity markets in India," Journal of Agribusiness in Developing and Emerging Economies, Emerald Group Publishing Limited, vol. 10(4), pages 447-473, May.
  • Handle: RePEc:eme:jadeep:jadee-10-2019-0175
    DOI: 10.1108/JADEE-10-2019-0175
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    Citations

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    Cited by:

    1. Paramasivam Ramasamy & Umanath Malaiarasan, 2023. "Agricultural credit in India: determinants and effects," Indian Economic Review, Springer, vol. 58(1), pages 169-195, June.
    2. A.N. Vijayakumar, 2023. "Declining trade interest in Indian commodity derivatives: a survey-based study on cardamom futures contract," Global Business and Economics Review, Inderscience Enterprises Ltd, vol. 28(3), pages 333-346.
    3. Weiyi Xia & Tao Xiong & Miao Li, 2024. "Can night trading reduce price volatility? Evidence from China's corn and corn starch futures markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 44(4), pages 585-604, April.
    4. Rahul Kumar Singh, 2023. "Efficiency of Wheat Futures across APMC Mandis," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 21(3), pages 681-701, September.

    More about this item

    Keywords

    Agricultural commodity; Spillover; Volatility; Cointegration; Causality; Price discovery; Futures market; India; C32; G10; G13; G14; Q13;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • Q13 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Agricultural Markets and Marketing; Cooperatives; Agribusiness

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