Commodity Markets Research Articles

Are carbon trading markets efficient? A longitudinal investigation

ABSTRACT We examine the evolution of price efficiency in the European Union Carbon Emissions Market and the net cross-correlations between the carbon and commodities markets using Multifractal Detrended Fluctuation Analysis (MFDFA), and Detrended Partial Cross-Correlation Analysis (DPCCA), methods based on dynamic systems derived from econophysics. Our analysis is based on daily values of the S&P GSCI Carbon Emission Allowances (EUA) EUR index and the S&P GSCI index for the period between January 2010 and June 2024 and uses seven-year rolling windows. Our results are supportive of the adaptive market hypothesis and indicate that the efficiency of the EU ETS markets has been improving steadily and that its correlation with the broader commodities market is increasing over time.

The Determinants of Marginal Convenience Yield in Agricultural Commodity Markets

AbstractWe report a significant downward trend in the convenience yield for holding physical inventory in agricultural commodity futures markets, attributing this negative trend to speculative demand shocks, which in turn, leads in decreasing agricultural convenience yields. Moreover, agricultural convenience yields appear negative on average during the recent financialization (of commodities) period. We additionally show that the response of agricultural convenience yields to commodity price uncertainty and supply shocks is much less pronounced in magnitude and persistence compared to that of hedging demand shocks. Overall, our analysis verifies the Keynesian theory of normal backwardation by showing a long‐lasting positive response of agricultural convenience yields to a hedging demand shock, thereby leaving the hedging demand as the most significant factor explaining the less frequently observed backwardations in agricultural futures markets.

Dynamic Connectedness across Equity, Currency, and Commodity Markets in South Asia: A Time- Varying Parameter Vector Autoregressive Approach

This study examines the dynamic return spillovers among major South Asian financial markets, including equity indices from India (NIFTY50R), Pakistan (KSER), and Sri Lanka (SLASPIR), as well as key global commodities such as Brent crude oil (BrentR) and gold (GoldR), and the relevant exchange rates (USDINR, USDPKR, and USDLKR). Using the Time-Varying Parameter Vector Autoregressive (TVP- VAR) connectedness approach, the analysis covers the period from March 2, 2005, to February 29, 2024, providing insights into how financial shocks propagate across these interconnected markets over nearly two decades. The findings reveal that the Indian equity market (NIFTY50R) and the Indian Rupee exchange rate (USDINR) serve as significant transmitters of shocks within the South Asian region, influencing other regional markets. Conversely, the Sri Lankan (SLASPIR) and Pakistani (KSER) equity indices are identified as net receivers of shocks, indicating their vulnerability to external financial disturbances. The study also highlights the pivotal role of Brent crude oil prices (BrentR) in driving spillovers, particularly affecting the Indian financial markets. The study's findings have significant implications for understanding the complexities of market interconnectedness in South Asia, particularly in the context of global economic integration.

Comovement between the Stock Markets and Commodity Market

The study extends the literature by investigating the comovement between the stock markets and commodity market of developed and emerging countries. Developed countries are represented by G7 countries while the emerging countries are represented by BRICS countries. Further, for the commodity market, the Global S&P GSCI commodity index is used that comprised of all major global commodities. The daily log returns of all the markets are used for analysis. Further, the wavelet coherence model is used to investigate the comovement between the stock markets and commodity market. The results show that the comovement between the stock markets of all the countries is increases over time particularly at the larger frequency scales. The comovement is higher during the crisis periods like the global financial crisis and COVID-19. Generally, the comovement in the context of developed countries is higher compared to the comovement in the context of emerging countries.

Economic Policy Uncertainty in the United States: Does It Matter for Equity, Commodity and Cryptocurrency Markets?

In recent years, the issue of worldwide uncertainty has gained more attention in academic literature. Therefore, the current study examines how the United States (U.S.) economic policy uncertainty (EPU) affects various stock indices, commodities and cryptocurrencies. This study takes data on stock indices and commodities from February 2005 to December 2023 and data on cryptocurrency from October 2017 to December 2023. For estimations, we employ the Quantile-on-Quantile regression (QQR) approach to investigate the impact and to understand how changes in EPU affect stock indices, commodities, and cryptocurrency returns at different levels of quantiles. The findings reveal that EPU has a negative impact on the stock indices and cryptocurrencies. For stocks, high uncertainty leads to more volatility, while EPU exhibits higher volatility for cryptocurrencies, indicating sensitivity to policy changes. Similarly, commodities react differently to the U.S. EPU, while gold tends to appreciate in uncertain times. Furthermore, we employ quantile regression for robustness check, and the findings validate the outcome of QQR at various levels of quantiles from lower to higher. Moreover, the findings of this study are helpful for investors, portfolio managers, and policymakers to develop better investment strategies and effectively manage risks across different asset classes.

How has the relationship between major financial markets changed during the Russia–Ukraine conflict?

Geopolitics events have a significant impact on financial markets in the process of global economic integration and financial liberalization. Focusing on the 2022 Russia-Ukraine conflict, this paper utilizes the TVP-VAR network connectedness approach to investigate its specific impacts on the connectedness of major global financial markets. It examines the dynamic evolution of high-frequency and low-frequency shocks on market correlations during the pre-conflict, initial, and stalemate stages, revealing the changes in risk transmission paths. Findings show that the conflict led to increased market interconnectedness. Low-frequency became a major part of the total spillover, signaling long-term structural changes. The stock and foreign exchange market initially responded swiftly, serving as starting points for risk diffusion. The commodity market has been risk receivers throughout the conflict period. Intra-market and cross-market linkages have also changed. The German stock market was the main source of risk spillovers in the conflict, influencing international market linkages. In the global financial market network, several stock markets were the primary risk transmitters in the early stage of the conflict, while foreign exchange markets took on this role during the stalemate period. In addition, gold continues to play a safe-haven role. Furthermore, the TVP-VAR model is used to quantitatively assess the extent of the impact of the conflict on the spillover effects of major global financial markets at different time points and lags. Based on these findings, this paper constructs diversified portfolios and proposes a series of targeted risk management recommendations, which provide a theoretical basis for future policy formulation under similar crises.

Competitiveness of Pepper Commodity as a Flagship Product to Compete In The Global Market

Pepper is one of the spices that has an important function in the global food industry. Although Indonesia is one of the major pepper producers, it faces considerable challenges from competing countries such as Vietnam and Brazil. This study aims to evaluate the competitiveness of Indonesian pepper in the global market, identify factors that affect export performance, and provide strategic recommendations to improve Indonesia's position in the international market. This study uses a quantitative descriptive approach to evaluate the competitiveness of Indonesian pepper. Secondary data used are export statistics and global commodity market reports. The Revealed Comparative Advantage (RCA) and Export Product Dynamic (EPD) methods were used to analyze pepper export performance. The results showed that Indonesia experienced a decline in participation in the global value chain of pepper, with a decrease in exports from 29,692 tons in 2019 to 12,945 tons in 2022. Although Indonesia has an advantage in natural resources, it faces challenges from competing countries that have lower production costs and more efficient farming systems. Indonesia's pepper competitiveness is influenced by several factors such as product quality, innovation, and government policy support. Cooperation between the government and farmers is essential to improve the competitiveness of pepper in the international market. This study confirms that although Indonesia has great potential in pepper production, the existing challenges require serious attention. Improving Indonesia's pepper competitiveness requires the application of modern agricultural technology, product diversification, as well as stronger collaboration among all stakeholders.

Systemic risk and contagion in the commodity market: identifying volatility transmission during crisis periods

Systemic risk and contagion in the commodity market: identifying volatility transmission during crisis periods

Energy Trading in Local Energy Markets: A Comprehensive Review of Models, Solution Strategies, and Machine Learning Approaches

The increasing adoption of renewable energy sources and the emergence of distributed generation have significantly transformed the traditional energy landscape, leading to the rise of local energy markets. These markets facilitate decentralized energy trading among different market participants at the community level, fostering greater energy autonomy and sustainability. As local energy markets gain momentum, the application of artificial intelligence techniques, particularly reinforcement learning, has gained substantial interest in optimizing energy trading strategies by interacting with the environment and maximizing the rewards by addressing the decision complexities by learning. This paper comprehensively reviews the different energy trading projects initiated at the global level and machine learning approaches and solution strategies for local energy markets. State-of-the-art reinforcement learning algorithms are classified into model-free and model-based methods. This classification examines various algorithms for energy transactions considering the agent type, learning methods, policy, state space, action space, and action selection for state, action, and reward function outputs. The findings of this work will serve as a valuable resource for researchers, stakeholders, and policymakers to accelerate the adoption of the local energy market for a more efficient, sustainable, and resilient energy future.

Actors, spatiotemporal changes, and (un)sustainable practices in community peatland management: A case study from West Kalimantan, Indonesia

This research explores the village community that manages large, degraded, abandoned, and ex‐logged peatlands in light of overlapping national development policy, global commodity market forces, and international conservation mandates. The transformation of peatland ownership from public to private owners has led to a predominant shift towards oil palm plantations, facilitated by village elites through partnerships, migration, and smallholder schemes. This conversion has resulted in extensive peatland degradation, drainage impacts, and frequent peat fires. In response, the local community has committed to reducing the use of fire for land clearance and water management. As an alternative, machinery is used to manage the peatland. However, employing excavators for land clearance and constructing peat water‐draining canals poses other threats, of peatland depletion, increased fire occurrences, degradation and compaction of peat. This study seeks to explore community‐level degraded peatland management dynamics by understanding (i) the actors’ conflicting intentions and continuous negotiation (between conservation, exploitation, and development), (ii) considering the impact of tenure insecurity and the power struggle surrounding further peatland exploitation, and (iii) the need for broader discussions about community peatland management beyond fires.

Cost Pass‐Through in Commodity Markets With Capacity Constraints and International Linkages

ABSTRACTWe examine how regional cost shocks are passed through into the prices of globally traded energy‐intensive commodities. We find that the pass‐through of local costs is influenced by production capacity in the short run. When capacity constraints become binding, the pass‐through of cost shocks to the constrained region diminishes to zero. Our application to the market for ammonia, a commodity largely produced from natural gas, demonstrates the significance of capacity constraints and international market linkages for empirical pass‐through models.

A Study on the Influence of Behavioral Factors on Women Investors' Decision-Making Regarding the Commodity Market

An exhaustive literature search confirms that psychological and sociological variables affect women investors' choices. This study groups behavior into allusion, regret, reluctance, belief, self-reliance, risk aversion, rational choice, and constructive. Technical analysis, fundamental analysis, and market psychology are the three components of decision-making. They randomly selected 151 women investors in the commodities market to participate in the research. The findings strongly connect technical analysis, belief, and rational choice. Self-reliance and the psychology of the market are closely related. The study shows that women investors are risk-averse and prefer to look at historical data. As a result of their extensive background and training in investment activities, they can monitor the fluctuating commodity market prices and make informed decisions.

Связь мотивированности работников с уровнем безопасности труда на горнодобывающем предприятии

Dynamically changing market conditions require increasing workflow efficiency at a mining enterprise, which enhances its stability in the commodity market. Restricted resources at the enterprise lead to a situation when production volumes become most prioritized, often to the detriment of production safety. At the same time, production safety is the basis on which enterprise stability is built. The enterprise safety must be controlled via the reduction of risk of negative events. Increasing control efficiency is based on the desire of employees to implement the control, which, in turn, is ensured by employee’s motivation for such activities. Based on Maslow’s human needs pyramid, it is possible to determine the needs of personnel; meeting these needs will motivate employees for occupational safety activities. According to the pyramid, physiological human needs are basic. In conditions of production, these needs are met by establishing optimal or acceptable working conditions characterized by the class of working conditions. The need for safety is classified as a higher-level need; in conditions of production, it is ensured by establishing safe working conditions. The safety level can be evaluated using the safety index. Social needs are on the third level of the hierarchy. Evaluation of the level of motivating factors enables a potential satisfaction of employee’s needs, assessment of his/her motivation for the implementation of occupational safety measures, and forecasting the level of safety to be ensured.

Natural disaster shocks and commodity market volatility: A machine learning approach

Natural disaster shocks and commodity market volatility: A machine learning approach

Modelling Volatility in the Commodity Market Amidst Russia- Ukraine War

We pursue two noteworthy effects of the Russia-Ukraine war on commodity market volatility. First, we construct a Russia-Ukraine war index while modelling commodity market volatility. Second, we explore the relationship between the Russia-Ukraine war and commodity market volatility using various estimators. We find consistent patterns in the volatility models of the selected estimators. Our results show that the Russia-Ukraine war triggered commodity market volatility.

Economic influences of agriculture production and import mechanisms through commodity market in the nepalese context

Agriculture production not only manage food and domestic needs rather enhance nation’s economy. Nepalese production is relying from neighbours through import due to insufficiency. This study design to analyse the impact of agricultural cereal production and import scenario in the Nepalese context. A mixed method approach, both Qualitative and Quantitative data utilized for this Doctoral study-based paper, Farmers of all Seven Provinces samples with HHs interview and secondary data of agriculture and Custom data used. The study was conducted from January to June 2023. The findings revealed the major cereal crop production from own like paddy, maize, wheat and millet are limited for some season. In addition, import from India shifting large economy to India through import. The agriculture production meeting domestic needs like seasonal food and market stock. Whereas seeds, input and mechanization dependency largely depending on neighbours due to no manufacturing of own. The study concluded as agriculture production is a major source of income and food for Nepal. Limited month of sufficiency at the local production bounded Nepal depends on import. The cause was found as lack of sufficient input system, low priority of agriculture from all sectors, diversity and low-political priority. Food Diversity and local production enhancement is the ultimately way out to deal with this issue.

Time-Varying Spillover Effects of Carbon Prices on China’s Financial Risks

As China’s financial markets become increasingly integrated and the carbon market undergoes financialization, the impact of carbon emission price fluctuations on financial markets has emerged as a key area of systemic risk research. This study employs the Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) model and the optimal Copula function to investigate the dynamic correlation between carbon prices and China’s financial markets. Building on this, the Monte Carlo simulation and Copula CoVaR models are used to explore the spillover effects of carbon price volatility on China’s financial markets. The findings reveal the following: (1) Carbon price fluctuations generate spillover effects on all financial markets, but the intensity varies across different markets. The foreign exchange market experiences the strongest spillover effect, followed by the bond market, while the stock and money markets are relatively less affected. (2) The optimal Copula functions differ between the carbon market and China’s financial markets, indicating heterogeneous characteristics across regional markets. (3) There is a degree of interdependence between the carbon market and various sub-markets in China’s financial system. The carbon market has the strongest positive correlation with the commodity market and a relatively high negative correlation with the real estate market. These findings underscore the importance of integrating carbon price volatility into financial risk management frameworks. For policymakers, it highlights the need to consider market stability measures when crafting carbon emission regulations. Market managers can leverage these insights to develop strategies that mitigate risk spillover effects, while investors can use this analysis to inform their portfolio diversification and risk assessment processes.

Commodity Market Segmentation Hinders Carbon Productivity Gains in Chinese Agriculture: Moderating Effects of Economic and Informational Development

Commodity Market Segmentation Hinders Carbon Productivity Gains in Chinese Agriculture: Moderating Effects of Economic and Informational Development

Commodity market downturn: Systemic risk and spillovers during left tail events

We investigate systemic risk and spillovers in the commodity network during left-tail events using state-of-the-art methodologies: the Component Exponent Shortfall (CES), Quantile-Vector Autoregression (QVAR) and Causality-in-Risk. Our analysis focuses on five commodity groups: Energy (Crude Oil, Heating Oil, Natural Gas, Coal), Base Metals (Aluminum, Copper, Nickel, Zinc), Ferrous Metals (Iron, Steel), Precious Metals (Gold, Palladium, Platinum, Silver), and Others (Rubber). Across the models utilized, we consistently find that energy commodities and precious metals, along with copper as a standalone commodity, represent the most systemically risky group. Thus, portfolios incorporating these commodities are advised to implement more careful diversification to mitigate risks stemming from systemic factors. This may require additional attention to precious metals, as they are often considered safe-haven assets. Expediting the implementation of regulations that promote the replacement of fossil energy sources with green alternatives could be instrumental in managing systemic risk in the commodity market while also facilitating global sustainability. Finally, the results show that the impact of the Israeli-Palestinian conflict on both systemic risk and spillovers has been limited compared to the effects of COVID-19 and the Russia-Ukraine war.

INVESTMENT ANALYSIS IN SOYBEAN UBS FOR OWN USE: A CASE STUDY IN ALTO GARÇA – MT

Soybean (Glycine max) is the most widely produced legume in the world, playing an important economic, social and environmental role, in addition to influencing the commodities market in Brazil and worldwide. This has attracted many national producers to its cultivation, making it a highly competitive market with investment in research, innovation and development in all links of the chain. Therefore, the objective of this study was to evaluate the feasibility of investing in the installation of a Seed Processing Unit (UBS) for soybeans, on private property, with the purpose of producing the crop's own seed. To this end, a case study was carried out on a property in the municipality of Alto Garça - MT, using the descriptive method of exploratory nature, in which the economic impact of soybean seed production on the property was evaluated to the detriment of the reduction in production costs obtained in years following its implementation. Thus, the 2016/2017 to 2020/2021 harvests were analyzed in their cost composition and investment result from the 2017/2018 harvest. It was possible to verify the reduction in the seed cost per hectare by 38.33% in the first cycle, and by 66.87% compared to the last cycle analyzed, concluding that the investment was viable.