Hedging Global Stock Markets with Bitcoin, Precious Metals, Copper, Crude Oil, and Agricultural Commodities: Evidence from Bivariate Threshold GARCH Approach

There are many kinds of metals in crude oil. The rare metals are essential metals for the development of atomic energy, aviation and space, national defense construction, electronic computer, high point scientific instruments and so on. Although the resources of rare metals are abundant in China, but for a long time, china supplies 90% of the world's rare metals at a relatively low price, the reserves of rare metal resources are declining at an alarming rate. By 2020, only 6 of the 45 major minerals could meet the needs of China. Through the establishment of metal element detection technology in crude oil, the crude oil of Toutai Oil Field in China is determined, and there are rich rare metal elements in the crude oil. For example, the rare-earth metal content is 62.61 micrograms per kilogram, the rare light metals is 78.4 micrograms per kilogram, and the rare refractory metals is 290.47 micrograms per kilogram, etc. Although the research on the prospect of comprehensive utilization of rare metals in crude oil is late in China, the extraction of rare metals from crude oil has a broad prospect.

The Predictive Content of Commodity Futures

In recent years, the international community has witnessed many crisis events, and the Russia-Ukraine war, which broke out on 24th February 2022, has increased international policy uncertainty and impacted the current world commodity and financial markets. Thus, we try to capture how the Russia-Ukraine war has affected the correlation structure of international commodity and stock markets. We study six groups of commodity daily returns and one group of stock daily returns and select the sample from 24th February 2022 to 1st June 2022 as the sample during the Russia-Ukraine war; in addition, we select the sample from 1st December 2019 to 31st December 2020 as the sample during COVID-19 control group, and the sample from 1st January 2014 to 31st December 2017 as the non-extreme event control group, to explore the correlation structure of international commodity and stock markets before the war, and to compare and uncover the impact of the uncertain event of the Russia-Ukraine war on the commodity and stock markets. In this paper, the marginal density function of each series is constructed using the ARMA-GARCH-std method, and the R-Vine copula model is built based on the marginal density function to analyze the correlation relationship between each market. From the Tree1 of the Vine copula, it is found that crude oil becomes the core connecting each commodity market and the stock market during the Russia-Ukraine war. The price fluctuations of crude oil may be contagious to agricultural and precious metal markets in the same direction, while the stock market price fluctuations are inversely correlated with commodity markets. Comparison with the selected control group sample reveals that the Russia-Ukraine war increases the correlation between the markets and enhances the possibility of risk transmission. The core of the correlation structure shifts from agricultural commodities and precious metals to crude oil after the Russia-Ukraine war.

In recent years, the relationship between agricultural commodities and crude oil has become increasingly close with the promotion of biofuel policies. This study examines the dynamic correlation between global crude oil futures and seven agricultural commodity futures by applying the consistent dynamic conditional correlation and dynamic equicorrelation models. The empirical results show that the dynamic correlation between the global crude oil futures market and China's agricultural futures market is weak compared to the global agricultural futures market. In particular, soybean oil has the strongest correlation with crude oil, while Dalian Commodity Exchange (DCE) corn and Zhengzhou Commodity Exchange wheat have the weakest correlation with crude oil. There is an indirect linkage between crude oil futures and DCE soybean meal and DCE soybean oil. Moreover, the dynamic correlation between crude oil and agricultural commodities increased during the financial crisis, the novel coronavirus (COVID‐19) epidemic, and the crude oil crash crisis. Brent crude oil has a stronger co‐movement with China's agricultural commodities than West Texas Intermediate crude oil and can better hedge the risk of agricultural commodities. The findings of this study provide some insights into the contagion risk management of crude oil futures and agricultural futures markets.

This paper investigates the cross market linkages of Indian commodity futures for nine commodities with futures markets outside India. These commodities range from highly tradable commodities to less tradable agricultural commodities. We analyze the cross market linkages in terms of return and volatility spillovers. The nine commodities consist of two agricultural commodities: Soybean, and Corn, three metals: Aluminum, Copper and Zinc, two precious metals: Gold and Silver, and two energy commodities: Crude oil and Natural gas. Return spillover is investigated through Johansen’s cointegration test, error correction model, Granger causality test and variance decomposition techniques. We apply Bivariate GARCH model (BEKK) to investtigate volatility spillover between India and other World markets. We find that futures prices of agricultural commodities traded at National Commodity Derivatives Exchange, India (NCDEX) and Chicago Board of Trade (CBOT), prices of precious metals traded at Multi Commodity Exchange, India (MCX) and NYMEX, prices of industrial metals traded at MCX and the London Metal Exchange (LME) and prices of energy commodities traded at MCX and NYMEX are cointegrated. In case of commodities, it is found that world markets have bigger (unidirectional) impact on Indian markets. In bivariate model, we found bi-directional return spillover between MCX and LME markets. However, effect of LME on MCX is stronger than the effect of MCX on LME. Results of return and volatility spillovers indicate that the Indian commodity futures markets function as a satellite market and assimilate information from the world market.

The Energy Independence and Security Act (EISA) of 2007 states an increase in ethanol production to 36 billion gallons per year by 2022. Biofuels mainly are produced from agricultural commodities, so that increasing demand of biofuels would have an impact on agricultural commodity prices. The linear relationships between crude oil prices and prices for agricultural commodities are well documented, but not appropriate to explain the asymmetric dependency. Vine copula modeling which is used in this study can extend to higher dimensions easily and provide a flexible measurement to capture an asymmetric dependence among commodities. The purpose of this study is to analyze the degree and the dependence structure of commodities with the policy effect of EISA 2007 along the biofuel supply chain in the United States agricultural market. We employ vine copulas in order to better capture an asymmetric dependence among commodities using six U.S. agricultural commodities’ and crude oil. The empirical results provide that vine Copula-based ARMA-EGARCH (1,1) is an appropriate model with the skewed Student t innovations to analyze returns dependency of crude oil and agricultural commodities before EISA 2007 (January 1st, 2003- January 17th, 2007) and after EISA 2007 (January 18th, 2007-December 31st, 2012). Our findings on the relationship among agricultural commodities can provide policymakers and industry participants appropriate strategies for risk management, hedging strategies, and asset pricing.

Asymmetric effects of market uncertainties on agricultural commodities

Contemporaneous interactions among fuel, biofuel and agricultural commodities

The energy sector has been the main economic hub in everyone’s lives and in world geopolitics. Consequently, oil, gas, electricity and energy from renewable sources (wind and solar) are traded on the stock market, and all interconnected around the world. On the other hand, a global health crisis, such as COVID-19, can produce a great economic catastrophe. In this scenario, a robust statistical analysis will be performed here with respect to the concept of interdependence and contagion effect. For this project, we chose to study the relationship between the main source of energy (crude oil, WTI and Brent) and two (Gold and Silver) precious metals (which are a safe haven for investment). Therefore, with the novelty of the application of ρDCCA and ΔρDCCA coefficients before and during the COVID-19 crisis (announced by the World Health Organization), the interdependence and the contagion effect were calculated. We verified that COVID-19 had no influence on contagion effect between crude oil in its indexes, WTI and Brent, since they have already shown to be highly interdependent, both before and after the World Health Organization COVID-19 decree. Likewise, COVID-19 had a significant influence on the crude oil and precious metal sectors, which was evident as we identified an increase in its interdependence, with a clearly positive contagion. These results show that COVID-19 imposed a restructuring in the relationship between energy (crude oil) and precious metals. More details will be presented throughout this article.

Should investors and policy makers in agricultural markets consider oil market's incontestable impact on portfolio risk management? This paper investigates the time-varying market linkages between energy and agricultural commodities in the presence of two important exogenous shocks, viz., the COVID-19 pandemic and the subsequent 2022 Russia–Ukraine military conflict. We use a novel time-varying parameter vector autoregressive model with a common factor error structure to estimate the tail connectedness between energy and agricultural commodities for the period December 31, 2019 to December 18, 2023. Our findings provide clear evidence of asymmetry in the volatility evolution. We determine that volatility spillover magnitudes are much stronger across quantiles than at the mean. We note that crude oil is the main transmitter of shocks in the system before the onset of the 2022 Russia-Ukraine conflict at the lower tail of the distribution. While crude oil and natural gas transmit volatility in both pre- and post-conflict announcement periods. Furthermore, the 2022 Russia–Ukraine conflict is found to impact the transmission of volatility between energy and agricultural commodities. Numerous agricultural commodities are observed to shift their position from transmitters to receivers of volatility, and vice versa, due to the military conflict in Ukraine. Our causality results depict time-varying patterns in the connectedness between crude oil and other commodities. We determine that crude oil has varying impact on agricultural markets in pre- and post-conflict announcement periods. Commodities for which both conflicting countries are major world exports of, such as wheat, have notably increased their dependency on crude oil. Thus, we advise investors and policymakers in agricultural markets to seriously consider oil market's impact on portfolio risk management and monitoring policies.

This study examines whether precious metals, industrial metals, energy and agricultural commodities, or cryptocurrencies form trustworthy safe havens against extreme price volatility of major global bank stock indices during black-swan events such as the COVID-19 pandemic and the Russia–Ukraine conflict. Using daily data and applying Quantile-VAR dynamic pairwise and extended joint connectedness methodologies, we investigate dynamic connectedness between major financial assets and major bank indices during exceptional crises. Findings provide evidence that crude oil and both Ethereum and Bitcoin present evidence of propagating significant shocks towards bank stock indices during crises, but other large-cap cryptocurrencies present no evidence of any specific influence. Further, gold, natural gas, and wheat are identified as the main absorbers of spillovers from banking indices during analysed crises, with more pronounced effects identified during exceptional phases of volatility. Such findings suggest that risk in the banking sector can be efficiently hedged by traditional safe havens such as gold and counterbalanced by highly outperforming assets such as natural gas and wheat. The study significantly contributes to understanding the interplay between banking sectors and various financial assets during crises and the subsequent strategies available for managing systemic risks, providing valuable insights for policymakers, regulators, and investors alike.

The rise of Soybean in international commodity markets: A quantile investigation

This research paper employs input-output pricing model based on ecological-economic approach to investigate the impacts of internal factors as well as external forces on agriculture commodities. To empirically test our model, we select two different methodologies such as the optimal scaling regression with nonlinear transformations and feedforward artificial neural networks. Our sample includes data related to price of agriculture and energy commodities (cocoa, coffee and crude oil), production of crops and livestock, emissions of greenhouse gases (GHG) from agriculture from 1961 to 2019. Results find a bidirectional relationship between cocoa price and coffee price explaining by the fact that commodity-dependent countries often use kindred production landscapes and similar supply chain management when dealing with coffee and cocoa. Therefore, effect of supply side shocks may be transmitted from one market to another. We also present evidence that greenhouse gas emissions have strong effect on commodity price, thus we encourage an integrated approach including both concrete technological and proactive managerial measures in order to mitigate global warming impacts on the food system. We believe that these findings will be of interest to commodity producers, asset managers and academics who look a better understanding of the dynamics of commodity markets. JEL classification numbers: C50, Q02, Q57. Keywords: Agriculture commodity, Input-output pricing model, Ecological-economic approach, Artificial neural networks, Optimal scaling regression.

Correlation dynamics of crude oil with agricultural commodities: A comparison between energy and food crops

Background:This study explored the relationship between currency fluctuations, particularly those of the US dollar, and commodity prices, focusing on crude oil, gold, wheat, and soybeans. The inverse correlation between the US dollar and commodity prices is widely accepted, but this study aimed to provide primary data analysis to quantify this relationship and assess the sensitivity of different commodities to currency movements.Methods:A prospective cohort study was conducted over a 12-month period, collecting real-time data on commodity prices and US dollar exchange rates. Fifty participants, including commodity traders and financial analysts, provided transaction data for analysis. Descriptive statistics, correlation analysis, and multiple regression were used to analyze the data. The sensitivity of commodities to changes in the US dollar was assessed, and volatility and risk mitigation strategies employed by traders were also examined.Results:The study found a strong inverse relationship between the US dollar and the prices of crude oil and gold, with correlation coefficients of -0.72 and -0.65, respectively. Regression analysis showed that US dollar fluctuations explained 52% of the variance in crude oil prices (P = 0.0001) and 47% of the variance in gold prices (P = 0.0003). Agricultural commodities such as wheat and soybeans had weaker correlations with the US dollar, showing coefficients of -0.42 and -0.35, respectively. Volatility analysis indicated that crude oil and gold were more susceptible to price fluctuations than agricultural commodities. Hedging strategies, including forward contracts and currency options, were used by 65% of traders to mitigate currency risks.Conclusion:The study confirmed a significant inverse relationship between US dollar fluctuations and commodity prices, with crude oil and gold being most sensitive to currency movements. Agricultural commodities were less affected by currency changes, with external factors such as supply-demand dynamics playing a larger role. The findings suggest that currency fluctuations remain a critical factor for commodity traders, who actively employ hedging strategies to mitigate risks.