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This present work aims at looking into whether or not introducing commodities in international equity portfolios helps reduce the market risk and if hedging is carried out with the same effectiveness across different regional stock markets.

The authors determine the optimal hedge ratios and hedging effectiveness of a number of commodity-hedged emerging and developed equity markets, using three versions of MGARCH model: DCC, ADCC and GO-GARCH. The authors also use a rolling window estimation procedure for the purpose of constructing out-of-sample one-step-ahead forecasts of dynamic conditional correlations and optimal hedge ratios.

Empirical results evince that commodities significantly display effective risk-reducing hedge instruments in short and long runs. The main finding is that commodities do not seem to hedge regional stock markets in the same way. They tend to provide evidence of a rather effective hedging regarding mainly the East European and Latin American stock markets.

The authors study whether commodities can hedge stock markets at regional context and if hedging effectiveness differ from one region to another.

This study aims to investigate the role of international price volatility and inventories on domestic market price dynamics in the case of agricultural commodities.

A structural model is employed to uncover relationships among commodity price, price volatility, inventories and convenience yield. Monthly producer price data along with annual data on trade, consumption, inventories and tariffs for 71 countries and 13 commodities covering 2010–2019 are assembled to estimate the model. With a first-stage Least Absolute Shrinkage and Selection Operator (LASSO) estimator to identify the best instrument set, a nonlinear approach is used to estimate the model.

Results show that international market information plays a critical role in domestic market price dynamics. International price volatility has a stronger effect on domestic prices than that of international inventories.

Current upheaval in commodity markets requires an understanding of how prices move together and inventories affect that movement. A country's internal price is not independent of the effects of global market events.

Although hypotheses exist that global market information (volatility and inventories) helps countries manage domestic commodity prices, there have been limited studies on this relationship, especially with a structured model and cross-country data.

This paper aims to examine from commodity portfolio managers’ perspective the performance of liquidity adjusted risk modeling in assessing the market risk parameters of a large commodity portfolio and in obtaining efficient and coherent portfolios under different market circumstances.

The implemented market risk modeling algorithm and investment portfolio analytics using reinforcement machine learning techniques can simultaneously handle risk-return characteristics of commodity investments under regular and crisis market settings besides considering the particular effects of the time-varying liquidity constraints of the multiple-asset commodity portfolios.

In particular, the paper implements a robust machine learning method to commodity optimal portfolio selection and within a liquidity-adjusted value-at-risk (LVaR) framework. In addition, the paper explains how the adapted LVaR modeling algorithms can be used by a commodity trading unit in a dynamic asset allocation framework for estimating risk exposure, assessing risk reduction alternates and creating efficient and coherent market portfolios.

The optimization parameters subject to meaningful operational and financial constraints, investment portfolio analytics and empirical results can have important practical uses and applications for commodity portfolio managers particularly in the wake of the 2007–2009 global financial crisis. In addition, the recommended reinforcement machine learning optimization algorithms can aid in solving some real-world dilemmas under stressed and adverse market conditions (e.g. illiquidity, switching in correlations factors signs, nonlinear and non-normal distribution of assets’ returns) and can have key applications in machine learning, expert systems, smart financial functions, internet of things (IoT) and financial technology (FinTech) in big data ecosystems.

Market efficiency leads to transparent and fair price discovery of commodity markets, thus enhancing the value chain for competitive benefit. The purpose of this paper is to investigate the market efficiency of Indian agricultural commodities at spot, futures and mandi markets apart from exploring price risk management in these markets.

This study uses Johansen co-integration, vector error correction model and granger causality for analyzing market efficiency of the nine most liquid agricultural commodities across three markets, namely, spot, futures and mandi. All these nine commodities are traded on National Commodity and Derivatives Exchange.

The statistical results indicate price discovery exists in the mandi market and spot market leading to futures prices. Mandi price returns are seen to negatively influence futures returns in the case of cotton seed, guar seed and spot returns in the case of jeera, coriander and chana. For castor seed, the three markets are seen to have no long run relationship. The results of Granger causality reveal short run relationship between all the three markets in the case of soybean seed and coriander. In these commodities, prices in all three markets are capable of predicting the prices in the other markets. For the case of cottonseed, Rape Mustard seed, jeera, guar seed, the results indicate unidirectional causality between the mandi markets and the other two markets.

These results shall facilitate policymakers to explore intervention through integrated agri-platform (IAP) in price discovery and market efficiency.

The results of this study are useful in understanding the price discovery of mandi markets and its role in the spot and futures market. Agricultural commodities price discovery depends upon the integration of all these three markets. Introduction of IAP as described in the paper shall facilitate price risk management apart from improving the efficiency of price discovery.

To the best of the knowledge, this is the first study considering mandi, spot and futures prices in the price discovery process in India. In addition, this study found the role of mandi markets in serving the economic function of price discovery and price risk management. Hence, suggests for policy intervention for Indian agricultural commodities to manage price risk.

In this paper, the authors aim to investigate the short‐run as well as long‐run market efficiency of Indian commodity futures markets using different asset pricing models. Four agricultural (soybean, corn, castor seed and guar seed) and seven non‐agricultural (gold, silver, aluminium, copper, zinc, crude oil and natural gas) commodities have been tested for market efficiency and unbiasedness.

The long‐run market efficiency and unbiasedness is tested using Johansen cointegration procedure while allowing for constant risk premium. Short‐run price dynamics is investigated with constant and time varying risk premium. Short‐run price dynamics with constant risk premium is modeled with ECM model and short‐run price dynamics with time varying risk premium is modeled using ECM‐GARCH in‐Mean framework.

As far as long‐run efficiency is concerned, the authors find that near month futures prices of most of the commodities are cointegrated with the spot prices. The cointegration relationship is not found for the next to near months futures contracts, where futures trading volume is low. The authors find support for the hypothesis that thinly traded contracts fail to forecast future spot prices and are inefficient. The unbiasedness hypothesis is rejected for most of the commodities. It is also found that for all commodities, some inefficiency exists in the short run. The authors do not find support of time varying risk premium in Indian commodity market context.

In context of Indian commodity futures markets, probably this is the first study which explores the short‐run market efficiency of futures markets in time varying risk premium framework. This paper also links trading activity of Indian commodity futures markets with market efficiency.

The aim of this study was to investigate the dynamic network connectedness between stock markets and commodity futures and its implications on hedging strategies. Specifically, the authors studied the impact of the 2014 oil price drop and coronavirus disease 2019 (COVID-19) pandemic on risk spillovers and portfolio allocation among stock markets (United States (SP500), China (SSEC), Japan (Nikkei 225), France (CAC40) and Germany (DAX)) and commodities (oil and gold).

In this study, the authors used the Baba, Engle, Kraft and Kroner–generalized autoregressive conditional heteroskedasticity (BEKK–GARCH) model to estimate shock transmission among the five financial markets and the two commodities. The authors rely on Diebold and Yılmaz (2014, 2015) methodology to construct network-associated measures.

Relying on the BEKK–GARCH, the authors found that the recent health crisis of COVID-19 intensified the volatility spillovers among stock markets and commodities. Using the dynamic network connectedness, the authors showed that at the 2014 oil price drop and the COVID-19 pandemic shock, the Nikkei225 moderated the transmission of volatility to the majority of markets. During the COVID-19 pandemic, the commodity markets are a net receiver of volatility shocks from stock markets. In addition, the SP500 stock market dominates the network connectedness dynamic during the COVID-19 pandemic, while DAX index is the weakest risk transmitter. Regarding the portfolio allocation and hedging strategies, the study showed that the oil market is the most vulnerable and risky as it was heavily affected by the two crises. The results show that gold is a hedging tool during turmoil periods.

This study contributes to knowledge in this area by improving our understanding of the influence of fluctuations in oil prices on the dynamics of the volatility connection between stock markets and commodities during the COVID-19 pandemic shock. The study’s findings provide more implications regarding portfolio management and hedging strategies that could help investors optimize their portfolios.

This paper aims to investigate whether the establishment of commodity futures can effectively hedge systemic risk in the spot network, given the context of financialization in the commodity futures market.

Utilizing industry association data from the Chinese commodity market, the authors identify systemically important commodities based on their importance in the production process using multiple graph analysis methods. Then the authors analyze the effect of listing futures on the systemic risk in the spot market with the staggered difference-in-differences (DID) method.

The findings suggest that futures contracts help reduce systemic risks in the underlying spot network. Systemic risk for a commodity will decrease by approximately 5.7% with the introduction of each corresponding futures contract, since the hedging function of futures reduces the timing behavior of firms in the spot market. Establishing futures contracts for upstream commodities lowers systemic risks for downstream commodities. Energy commodities, such as crude oil and coal, have higher systemic importance, with the energy sector dominating systemic importance, while some chemical commodities also have considerable systemic importance. Meanwhile, the shortest transmission path for risk propagation is composed of the energy industry, chemical industry, agriculture/metal industry and final products.

The paper provides the following policy insights: (1) The role of futures contracts is still positive, and future contracts should be established upstream and at more systemically important nodes in the spot production chain. (2) More attention should be paid to the chemical industry chain, as some chemical commodities are systemically important but do not have corresponding futures contracts. (3) The risk source of the commodity spot market network is the energy industry, and therefore, energy-related commodities should continue to be closely monitored.

Considering the frequency of extreme events, enhancing the global financial system's stability has become crucial. This study aims to investigate the contagion effects of extreme risk events in the international commodity market on China's financial industry. It highlights the significance of comprehending the origins, severity and potential impacts of extreme risks within China's financial market.

This study uses the tail-event driven network risk (TENET) model to construct a tail risk spillover network between China's financial market and the international commodity market. Combining with the characteristics of the network, this study employs an autoregressive distributed lag (ARDL) model to examine the factors influencing systemic risks in China's financial market and to explore the early identification of indicators for systemic risks in China's financial market.

The research reveals a strong tail risk contagion effect between China's financial market and the international commodity market, with a more pronounced impact from the latter to the former. Industrial raw materials, food, metals, oils, livestock and textiles notably influence China's currency market. The systemic risk in China's financial market is driven by systemic risks in the international commodity market and network centrality and can be accurately predicted with the ARDL-error correction model (ECM) model. Based on these, Chinese regulatory authorities can establish a monitoring and early warning mechanism to promptly identify contagion signs, issue timely warnings and adjust regulatory measures.

This study provides new insights into predicting systemic risk in China's financial market by revealing the tail risk spillover network structure between China's financial and international commodity markets.

This study aims to systematically reveal the complex interaction between uncertainty and the international commodity market (CRB).

A composite uncertainty index and five categorical uncertainty indices, together with wavelet analysis and detrended cross-correlation analysis, were used. First, in the time-frequency domain, the coherency and lead-lag relationship between uncertainty and the commodity markets were investigated. Furthermore, the transmission direction of the cross-correlation over different lag periods and asymmetry in this cross-correlation under different trends were identified.

First, there is significant coherency between uncertainties and CRB mainly in the short and medium terms, with natural disaster and public health uncertainties tending to lead CRB. Second, uncertainty impacts CRB more markedly over shorter lag periods, whereas the impact of CRB on uncertainty gradually increases with longer lag periods. Third, the cross-correlation is asymmetric and multifractal under different trends. Finally, from the perspective of lag periods and trends, the interaction of uncertainty with the Chinese commodity market is significantly different from its interaction with CRB.

First, this study comprehensively constructs a composite uncertainty index based on five types of uncertainty. Second, this study provides a scientific perspective on examining the core and diverse interactions between uncertainty and CRB, as achieved by investigating the interactions of CRB with five categorical and composite uncertainties. Third, this study provides a new research framework to enable multiscale analysis of the complex interaction between uncertainty and the commodity markets.

This study investigate the return and volatility spillover among agricultural commodities and emerging stock markets during various crises, including the COVID-19 pandemic and the Russian-Ukrainian war.

This return and volatility spillover is estimated using Diebold and Yilmaz (2012, 2014) approach.

The results reveal the weak connectedness between agricultural commodities and emerging stock markets. Corn and sugar are the highest and lowest transmitters, respectively, whereas soya bean and coffee are the largest and smallest recipients of spillover over time. Most equity indices are the net recipient except for India, China, Indonesia, Argentina and Mexico, during the entire sample period. Most commodities are net transmitters of volatility spillover except coffee and soya bean. At the same time, major equity indices are the net recipient of the volatility spillover except for India, Indonesia, China, Argentina, Malaysia and Korea. In addition, the return and volatility spillover increase during various crises like the COVID-19 pandemic and the Russian-Ukrainian war, but the major increase in spillovers occurs during the COVID-19 pandemic.

The empirical results show a weak relationship between agricultural commodities and emerging stock markets which is helpful for investors and portfolio managers in the construction and reallocation of their portfolios under different periods, most notably under COVID-19 and the Russian-Ukrainian war.

It is an original paper.