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| Author(s) | Period | Variables | Econometric methods | Empirical outcomes |
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| Indian commodity markets |
| Vijayakumar [19] | January 2017–March 2020 | Cardamom | Johansen cointegration, vector error correction model, Granger causality, and regression with dummy variables | Cardamom e-auction prices exhibit a negative association with cardamom futures but a positive relation with spot prices |
| Pradhan, Hall, and Toit [28] | 2009–2020 | Commodities (aluminum, copper, crude oil, gold, nickel, and silver) and indices (agriculture, livestock, and precious metals) | ARDL bounds testing | Long-run unidirectional causality from spot to futures prices for aluminum, copper, and silver but short-run bidirectional, unidirectional, and neutrality between spot and futures prices |
| Rout, Das, and Rao [16] | January 2010-December 2015 | Chana, chilli, jeera, soya bean, and turmeric. | Causality test, error correction model, EGARCH, and parametric VaR | Volatility spreads from the spot market to the futures market |
| Nair [22] | January 2008-December 2019 | Aluminium, Copper, Nickel, and Zinc | Johansen test, error correction model, and Granger causality | Metals’ futures prices are heavily weighted in predicting futures spot market prices |
| Nair [18] | January 2004–December 2019 | Pepper, cardamom, and natural rubber | Cointegration-ECM-GARCH framework | Price discovery in commodity futures markets is efficient |
| Mohanty and Mishra [17] | October 2015–March 2016 | Castor seed, cotton oil cake, rape mustard seed, soybean, refined soya oil, crude palm oil, jeera, chana (chickpea), and turmeric | Variance ratio tests | Agricultural commodity futures markets in India are inefficient in the short term both before and after merger |
| Manogna and Mishra [15] | 2010–2020 | Oil seeds (cotton seed, castor seed, soybean seed, rape mustard seed), spices (turmeric, jeera coriander), and grains (guar seed, chana) | Granger causality, vector error correction model (VECM) and exponential generalized autoregressive conditional heteroskedasticity (EGARCH) | Price discovery exists in all of the commodities studied, with the futures market outperforming the spot market in six of them: soybean seed, coriander, turmeric, castor seed, guar seed, and chana |
| Kaur and Singh [21] | 2007–2016 | Gold exchange traded funds | Johansen test of cointegration, fully modified ordinary least squares, Toda-Yamamoto test of causality | Spot and futures price movements have been found to lead those of exchange traded funds |
| Jena, Tiwari, Hammoudeh, and Roubaud [27] | 2005–2017 | Bullion commodities (gold and silver), and energy commodities (Brent crude oil and natural gas) | Causality-in-quantiles test | Because of its informational efficiency, the foreseeability of the futures market is high in the normal market and declines when the spot market enters severe bearish and bullish situations |
| Bhaumik, Karanasos, and Kartsaklas [24] | 1995–2007 | NSE index | Bivariate ARFI-FIGARCH | The integration of futures trading lessens spot variability |
| Inoue and Hamori [23] | January 2006–March 2011 | The spot index (MCXSCOMDEX) and futures index (MCXCOMDEX) | Dynamic ordinary least squares (DOLS) and fully modified ordinary least squares (FMOLS) | The futures market for commodities appears to be efficient |
| Joseph, Sisodia, and Tiwari [25] | January 2008–December 2012 | Gold, silver, crude oil, natural gas, aluminium, copper, chana, and soybean | Granger causality test and causality analysis in the frequency domain | Almost all of the commodities selected have one-way relationships from futures to spot |
| Mahalik, Acharya, and Babu [50] | June 2005–December 2008 | Agriculture futures price index (LAGRIFP), energy futures price index (LENERGYFP), and aggregate commodity index (LCOMDEXFP) | Vector error correction model (VECM) and bivariate exponential Garch model (EGARCH) | Futures commodity markets exert a leading role and offer effective price discovery in the spot commodity market |
| Ali and Gupta [20] | 2004–2007 | Wheat, rice, maize, chickpea, black lentil, red lentil, guar seed, pepper, cashew, castor seed, soybean, and sugar | Johansen cointegration analysis, and Granger causality | Most agricultural commodities exhibit a long-term connection among futures and spot prices |
| Worldwide commodity markets |
| Jian, Li, and Zhu [51] | April 2015–April 2018 | CSI300, SSE50, and CSI500 | Skewness-dependent multivariate conditional autoregressive value at risk model (SDMV-CAViaR) | Severe risk overflows in both directions among the Chinese stock index futures and spot markets |
| Chen and Tongurai [52] | April 2015–March 2020 | Copper, aluminium, zinc, lead, nickel, and tin | Forecast error variance decomposition | Chinese futures markets for base metals tend to produce more spillover effects than spot markets |
| Yu, Ding, Sun, Gao, Jia, Wang, and Guo [53] | July 2003–December 2019 | Shanghai metal exchange copper spot prices, COMEX copper futures prices, LME copper futures prices, and Shanghai futures exchange copper futures prices | Wavelet decomposition | The futures markets in New York and London are more associated with the Chinese spot market than the Shanghai futures market |
| Ausloos, Zhang, and Dhesi [54] | 2007–2013 | CSI-300 index (China-Shanghai-Shenzhen-300-Stock index) and CSI-300 index futures (CSI-300-IF) | TGARCH, Granger causality, and regression analysis | Two-way Granger causality among futures and spot market in China |
| Go and Lau [55] | January 2000–July 2016 | Crude palm oil spot and futures prices in Malaysian currency | Variance ratio tests | During the bear market time span, spot and futures prices are strongly linked |
| Kirkulak-Uludag and Lkhamazhapov [56] | 2008–2013 | Russian spot and three-month futures gold prices | Corrected dynamic conditional correlation model | The conditional correlation among spot and futures gold returns is significantly greater |
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