Purpose: In India, commodity futures markets are the best alternative to the securities markets because of their familiarity with risk hedging. It makes sense that information regarding futures contract pricing is provided by the way prices behave in the futures market analysis. The relationship between futures contract prices and commodities spot market prices is made clearer by the price discovery process. The current study provided an example of how 10 agricultural prices were determined.

Methodology: The present study adopted exploratory and causal research designs based on cash and near-month futures contract prices of the selected agricultural commodities chosen through the purposive sampling method. The study used the augmented Dickey–Fuller (ADF) test, co-integration analysis, Granger causality test, and vector error correction model (VECM) to comprehend the efficiency of the price discovery mechanism of agricultural commodity futures markets in India.

Findings: The study’s statistical findings demonstrated that the cash and futures markets possessed comparable proficiency in terms of price discovery methods for chana, jeera, maize, moong, and soybean. However, the cash market drove the futures market for coriander and cotton cake, and the futures market directed the spot market for price discovery for barley, turmeric, and wheat.Practical

Implications: The study’s findings will be beneficial to market participants in adopting a range of arbitrage and trading techniques as well as to policymakers in confirming the consistency of both spot and futures markets, given the growing state of the commodity futures market in India.

Originality: The novelty of the present study is the selection of sample agricultural commodities that were actively trading and highly liquid in the commodities market using the purposive sampling method. The statistical results have highlighted the effectiveness of price discovery in spot and futures markets for all the commodities covered in the study. Traders thus profited from the study’s conclusions when they periodically framed their trading strategies.

1. Bohl, M. T., Siklos, P. L., Stefan, M., & Wellenreuther, C. (2019). Price discovery in agricultural commodity markets: Do speculators contribute? (CAMA Working Paper 42/2019). https://cama.crawford.anu.edu.au/sites/default/files/publication/cama_crawford_anu_edu_au/2019-06/42_2019_bohl_siklos_stefan_wellenreuther.pdf
2. Dhivya, R., Prahadeeswaran, M., Parimalaragan, R., Thangamani, C., & Kavitha, S. (2023). Commodity future trading and cointegration of turmeric markets in India. Asian Journal of Agricultural Extension, Economics & Sociology, 41(9), 190–199. https://doi.org/10.9734/ajaees/2023/v41i92031
3. Dickey, D. A., & Fuller, W. A. (1981). Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica, 49(4), 1057–1072. https://doi.org/10.2307/1912517
4. Elumalai, K., Rangasamy, N., & Sharma, R. K. (2009). Price discovery in Indis's agricultural commodity futures markets. Indian Journal of Agricultural Economics, 64(3), 315–323. https://doi.org/10.22004/ag.econ.204633
5. Engle, R. F., & Granger, C. W. J. (1987). Co-Integration and error correction: Representation, estimation, and testing. Econometrica, 55(2), 251–276. https://doi.org/10.2307/1913236
6. Garbade, K. D., & Silber, W. L. (1983). Price movements and price discovery in futures and cash markets. The Review of Economics and Statistics, 65(2), 289–297. https://doi.org/10.2307/1924495
7. Garg, M., Singhal, S., Sood, K., Rupeika-Apoga, R., & Grima, S. (2023). Price discovery mechanism and volatility spillover between national agriculture market and national commodity and derivatives exchange: The study of the Indian agricultural commodity market. Journal of Risk and Financial Management, 16(2), 62. https://doi.org/10.3390/jrfm16020062
8. Gerakos, J., & Gramacy, R. (2013). Regression-based earnings forecasts. Available at SSRN. https://doi.org/10.2139/ssrn.2112137
9. Gong, Q., Tang, Z., & Xu, B. (2021). Trading behaviors on knowledge of price discovery in futures markets. Journal of Innovation & Knowledge, 6(3), 191–195. https://doi.org/10.1016/j.jik.2021.03.001
10. Huda, M. N., Hossain, S. M. A., & Shaturaev, J. (2023). Effect of commodity exchange on the economy-prospects and challenges (MPRA Paper No. 118331). Munich Personal RePEc Archive. https://mpra.ub.uni-muenchen.de/118331/
11. Irfan, M., & Hooda, J. (2017). An empirical study of price discovery in commodities futures market. Indian Journal of Finance, 11(3), 41–57. https://doi.org/10.17010/ijf/2017/v11i3/111648
12. Jacobs, H. (2016). Market maturity and mispricing. Journal of Financial Economics, 122(2), 270–287. https://doi.org/10.1016/j.jfineco.2016.01.030
13. Johansen, S., & Juselius, K. (1990). Maximum likelihood estimation and inference on cointegration — with applications to the demand for money. Oxford Bulletin of Economics and Statistics, 52(2), 169–210. https://doi.org/10.1111/j.1468-0084.1990.mp52002003.x
14. Kumar Mahalik, M., Acharya, D., & Suresh Babu, M. (2014). Price discovery and volatility spillovers in futures and spot commodity markets: Some Indian evidence. Journal of Advances in Management Research, 11(2), 211–226. https://doi.org/10.1108/JAMR-09-2012-0039
15. Li, S., & Deng, L. (2021). Investor attention and price discovery efficiency of futures market: The role of investor structure. Proceedings of the 2021 International Conference on Economic Development and Business Culture (ICEDBC 2021) (Vol. 182, pp. 52–56). Atlantis Press. https://doi.org/10.2991/aebmr.k.210712.010
16. Manogna, R. L., & Mishra, A. K. (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, 10(4), 447–473. https://doi.org/10.1108/JADEE-10-2019-0175
17. Pani, U., Gherghina, Ş. C., Mata, M. N., Ferrão, J. A., & Mata, P. N. (2022). Does Indian commodity futures markets exhibit price discovery? An emprical analysis. Hindawi-Discrete Dynamics in Nature and Society, Article 6431403. https://doi.org/10.1155/2022/6431403
18. Rao, N., & Mukherjee, K. (1971). Random walk hypothesis: An empirical study. Arthaniti, 4(1), 53–59.
19. Rout, B. S., Das, N. M., & Rao, K. (2021). Competence and efficacy of commodity futures market: Dissection of price discovery, volatility, and hedging. IIMB Management Review, 33(2), 146–155. https://doi.org/10.1016/j.iimb.2021.03.014
20. Sargan, J. D. (1964). Wages and prices in the United Kingdom: A study in econometric methodology. In P. E. Hart, G. Mills, & J. K. Whitaker (eds.), Econometric analysis for national economic planning (Vol. 16, pp. 25 – 63). Butterworth Co.
21. Sehgal, S., Rajput, N., & Dua, R. K. (2012). Price discovery in Indian agricultural commodity markets. International Journal of Accounting and Financial Reporting, 2(2), 34–54. https://doi.org/10.5296/ijafr.v2i2.2224
22. Shalini, D., Deepthy, K., & Jayanthi, D. (2020). A study of price discovery process in commodities market in India: A case study of turmeric market. Journal of Critical Reviews, 7(17), 1547–1551.
23. Tomek, W. G. (1980). Price behavior on a declining terminal market. American Journal of Agricultural Economics, 62(3), 434–444. https://doi.org/10.2307/1240198
24. Venkatesh, P., Senthilnathan, C. R., Dhayalan, V., & Murugan, K. (2021). Study on price discovery of selected Indian agricultural commodity with special reference to NCDEX. International Journal of Advance and Innovative Research, 8(4), 76–84.
25. Vijayakumar, A. N. (2022). Price discovery and market efficiency of Cardamom in India. Vilakshan - XIMB Journal of Management, 19(1), 28–44. https://doi.org/10.1108/xjm-11-2020-0215
26. Viswanatha Reddy, C. (2019). Predicting the stock market index using stochastic time series ARIMA modelling: The sample of BSE and NSE. Indian Journal of Finance, 13(8), 7–25. https://doi.org/10.17010/ijf/2019/v13i8/146301
27. Viswanatha Reddy, C. (2020). Modelling and forecasting: NCDEX AGRIDEX. SCMS Journal of Indian Management, 17(3), 114–130.
28. Wooldridge, J. M. (2012). Introductory econometrics: A modern approach (5th ed.). South-Western Cengage Learning.