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The commodity and equity derivatives have a close resemblance between them in trade practices and mechanisms, which makes it easy for the investors to combine these two assets classes for building up their portfolio. The diversification of investment among asset classes builds some relation between them. The integration of market within a country is necessary to bring in a smooth and balanced economic growth. Thus, the purpose of this paper is to examine the spillover between the equity and commodity futures markets which will be helpful not only for the investors but also for the policy makers, producers and the regulators.

To examine the spillover between the equity and commodity market, the major benchmarking indices of these markets, namely COMDEX of MCX, Dhaanya of NCDEX and NIFTY 50 of NSE, were chosen. NIFTY 50 index was chosen as representative of equity market due to its composition of most active constituent stocks than any other broad market index of Indian stock market. As the commodity market indices are not been traded, their constituent commodities were taken for the study. Thus, 11 MCX-COMDEX constituents such as Gold, Silver, Copper, Zinc, Aluminum, Nickel, Lead, Crude oil, Natural gas, Kapaskhali and Mentha oil and eight NCDEX-Dhaanya constituents such as Castor seed, Chana, Cotton seed oilcake, Jeera, Mustard seed, Refined soy oil, Turmeric and Wheat futures prices were taken against the NIFTY 50 futures prices with daily trading data for ten years starting from January 1, 2006 till December 31, 2015 to analyze their spillover effect. The return series data were used to test the spillover between equity and commodity futures market as it gives the crux of investors’ diversification through the Vector Autoregression (VAR) model and verified with Impulse Response Function by testing the null hypothesis, H₀, that there is no return spillover between the equity and commodity futures market.

The investors move from equity to commodity when there is a threat in equity market and vice versa, thereby diversify their risk for those commodities which are vulnerable to global and domestic pressures in the economy. Investigating the spillover between equity and commodity market gives an insight of market integration effect. A nation can achieve its economic growth easily when its markets are integrated.

The commodity indices are still notional indices in the market; therefore, individual constituent commodities of commodities indices were considered with the benchmarking equity futures index, which is one of the limitations of the study.

The integration of market within a country is necessary to bring in a smooth and balanced economic growth.

Most of the past studies dealt only with few commodities and equities and not with the broad-based benchmarking indices. This paves way for enquiry into the commodity and equity markets integration with the major constituent commodities traded in the economy. Hence, this paper looks into the presence of spillover between the equity and commodity markets. The VAR model is verified with the impulse response function which explains the reaction of any dynamic system in response to a pulse change in another. The impulse response function is presented graphically for easy and better understanding.

This study unravels an attempt to investigate the dynamic connectedness of agri-commodity (wheat) of Russia with 10 financial markets of wheat importing counties during the Russia–Ukraine invasion.

This study took the daily prices of Wheat FOB Black Sea Index (Russia) along with stock indices of 10 major wheat-importing nations of Russia and Ukraine. The time frame for this study ranges from February 24, 2022 to July 31, 2022. This time frame was selected since it fully examines all of the effects of the crisis. The conditional correlations and volatility spillovers of these indices are predicted using the DCC-GARCH model, Diebold and Yilmaz (2012) and Baruník and Křehlík (2018) models.

It is found that there is dynamic linkage of agri-commodity of with stock markets of Iraq, Pakistan and Tanzania in short run while stock markets of Egypt, Turkey, Bangladesh, Pakistan, Brazil and Iraq are spilled by agri-commodity in long run. In addition, it documents that there is large spillover in short run than medium and long run comparatively. This signifies that investors have more diversification opportunity in short run then long run contemplating to invest in these markets.

To the best of the authors’ understanding this is the first study to undertake the dynamic linkage of agri-commodity (wheat) of Russia with financial market of select importing counties during the Russia–Ukraine invasion.

This paper aims to examine dynamic equicorrelations (DECO) and directional volatility spillover effects among four energy futures markets, namely, West Texas Intermediate crude oil, heating oil, natural gas and reformulated blendstock for oxygenate blending gasoline, by using a multivariate fractionally integrated asymmetric power ARCH–DECO–generalized autoregressive conditional heteroskedasticity (GARCH) model and the spillover index technique.

The empirical analysis uses the dynamic equicorrelation model of Engle and Kelly (2012) to examine time-varying correlations at equilibrium. The authors further analyze dynamic volatility transmission among energy futures by using Diebold and Yilmaz (2012) dynamic spillover index based on generalized value-at-risk framework.

The empirical results provide evidence of heightened equicorrelations at times of financial turmoil. More specifically, the dynamic spillover analysis shows that volatility is transmitted predominantly from crude oil to the other markets and risk transfer among four markets exhibits asymmetries. Spillovers are found to be highly responsive to dramatic events such as the 9/11 terror attack, 2008–2009 global financial crisis and 2014–2016 oil glut.

The results of this study have important practical implications for investors, portfolio managers and energy policymakers as the presence of time-varying co-movements and spillovers suggests the need for dynamic trading strategies. There are also implications regarding risk management practices, as there is evidence of increased volatility transmission at times of financial turmoil and uncertainty. Finally, the results provide insights to policymakers in a better understanding of the spillover dynamics.

This paper investigates the DECOs and spillover effects among crude oil, natural gas, heating oil and gasoline futures markets. To the best of the knowledge, this is one of a few studies that examine co-movements and risk transfer in energy futures in a comprehensive framework.

The paper analyzes the volatility transmission within the cotton markets by utilizing commodity futures prices from the USA, China and India, encompassing important global events that have significantly influenced the global cotton markets, like the China-USA trade dispute, the COVID-19 outbreak and the Russia–Ukraine conflict.

The authors employ a volatility spillover measure developed by Diebold and Yilmaz (2009, 2012, 2014). Additionally, the methodology proposed by Baruník and Křehlík (2018), which divides the overall volatility spillover into short, medium and long-term segments has been used. To investigate the volatility connectedness, weekly (close-to-close) returns of the cotton futures contracts that are traded on the Chicago Board of Trade Dalian Commodity Exchange National Commodity Exchange of India (NCDEX), and Multi Commodity Exchange (MCX) are considered.

The paper identifies the presence of long-term volatility transmission among the three cotton futures markets. It demonstrates that a global shock like the Russia–Ukraine conflict has a greater impact on volatility in other markets than USA–China trade disputes. It also highlights the weakening role of the US cotton futures markets as a price leader for Indian and Chinese markets.

Since only three major markets have been studied, the future studies can explore the interconnectedness by including other important markets including Brazil, Turkey, Bangladesh, etc. Further, the moderating role of relationship between other important variables such as cotton production, harvest, inventory, exchange rate, oil price, trade policies, etc. can be examined. Furthermore, the interconnectedness with the regional spot markets in India can also be examined to study how the volatility from the futures market can affect the volatility in the spot markets and vice-versa.

The understanding of domestic food price volatility and its transmission from international to domestic markets is crucial for designing effective policies to address excessive volatility and protect vulnerable groups like producers, consumers, etc.

The findings emphasize on the substantial market dependence with the US and the Chinese markets which have a significant impact on the Indian markets with considerable implications for hedgers, producers and exporters, particularly during periods of higher volatility.

This study assesses the interdependencies among three major cotton-producing countries and the influence of factors like the USA–China trade tensions in 2018, the COVID-19 crisis and the Russia–Ukraine conflict in order to gauge the degree of volatility interconnection among these key players in the cotton market.

The purpose of the study is to investigate the long-run and short-run dynamic relationship between crude oil prices and the movement of Sensex for the period of 2000–2018.

The study uses the augmented Dickey–Fuller test for the presence of unit root, Johansen cointegration test for estimating the cointegration among the variables. Further, in the case of no cointegration found, the study employed the vector autoregression (VAR) model to estimate the long-run relationship and the Granger causality/Wald test for short-run relationship. The study also conducted tests for the prerequisites of the model: serial correlation, heteroskedasticity and normality of data.

The study found that both the variables, crude oil prices and Sensex are integrated of order 1, that is, I (1), and there is no cointegration between them. Further, the results proliferated from the VAR model unfold the marked effect of previous month crude oil prices (lag 1) on the movement of Indian stock market represented by Sensex considered as the benchmark index. Furthermore, VAR–Granger causality/block exogeneity Wald tests results indicated that there is a causal relationship between the crude oil prices and Sensex under the VAR environment. The model does not have any serial correlation and heteroskedasticity indicating toward the unbiased and robust estimates.

The study is conducted till the year 2018, and data for the present period (post-2018) is excluded due to ongoing trade issues between the USA and oil-exporting countries such as Iran. The current COVID-19 outbreak has also put serious issues. Due to limited time and availability of standardized data, researchers have considered Sensex as equity index only, but for more generalized research outcome few other equity indexes could have been taken for study.

The study is completely original in nature and is an extensive study of the relationship between the crude oil price and Indian stock market with reference to causality between the variables.