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This paper examines the effects of commodity demand and supply shocks as well as international liquidity shocks on the small open economy of Brazil using an SVAR model. The paper highlights the importance of modelling both types of shocks in the commodity sector. Including only commodity prices overstates the effect of commodity price shocks on the output of Brazil. Commodity demand shocks are much larger than commodity supply shocks in the long run. Including commodity demand and international liquidity also reduces the impact of commodity price shocks on the interest rate made available to Brazil in international capital markets. There is little evidence of Dutch disease for Brazil. However, our results show that Brazil is vulnerable to external shocks.

The article considers the methodology of commodity markets research, which includes the assessment of the volume and structure of supply and demand according to a wide range of indicators. The information on formation of system representations of markets on the basis of segmentation of markets, marketing profiling of markets and their clients is generalized. The market profiling approach is discussed in depth. This approach consists in the representation of the market as a multidimensional array of data on the type of goods, price ranges, the distribution of buyers and sellers by region, industry and other characteristics on the basis of available reliable, but incomplete data. The article considers the problem of obtaining reliable data to determine their reliable sources and organization of data collection in the framework of consulting services. The relevance of the task of forming the correct sample for consulting markets research, which provides in conjunction with data from public sources, a representative representation of the commodity market. Specific features of commodity markets, which causes the variability of approaches to the market dependence on imports, the share of direct deliveries, transport channels of supply and other factors have been considered; features of profiling in the express-analysis of commodity markets have been determined. As an example of practical realization of commodity markets profiling methodology, the market of hot-rolled stainless steel sheets with thickness of 2.0–13.0 mm and width up to 1600 mm is considered. This market is characterized by high import dependence and, therefore, the Federal Customs Service database was used as a source of information for it. Market profiling, performed in accordance with the proposed methodology, provided detailed information on the distribution of imports and consumption of individual groups of stainless steel sheets. Estimates of the commodity market obtained as a result of the study using market profiling have been verified by independent consulting studies.

Since the financialization of commodities, portfolio investments have become an important tool for investors to diversify risks. However, due to the nonlinear fluctuations brought about by extreme events, investors face more difficulties in the choice of risk portfolio. We adopt empirical mode decomposition and STVAR model, along with the basis data of optimized original sample interval. In addition, we retain the mature research of multiscale systemic risk under frequency and divide the dimension of systemic risk into two states. When frequency is combined with states, the risk spillover center undergoes subversive changes, particularly in the longest term, and metals become the risk spillover center, substituting the energy commodity, on the condition that the compositions of extreme value add persuasive power to the perspective of long term. We proposed that the joint fluctuation of agricultural commodities and energy commodities makes the former become another important risk spillover point. For investors, holding period and portfolio both need to be considered.

This paper investigates the role of speculative activity in the agricultural commodity futures market in the period 2006-2017. Specifically, the study tests the causal relationship between the prices of fourteen agricultural commodities listed on the US commodity market Chicago Mercantile Exchange (CME) and Chicago Board of Trade (CBT) and the trading activity of commodity index traders (CITs) and swap dealers. The analysis uses the Granger Causality test based on a seemingly unrelated regression (SUR) system. The results show that CIT and swap dealer positions did not significantly influence prices of agricultural commodities, but might explain the increase in their price volatility. The findings disprove Masters’ hypothesis that speculators produced a bubble in the commodity market. In this context, any attempt (such as taxes) by lawmakers to limit speculation should be carefully evaluated. Key words: Commodity index traders, swap dealers, agricultural futures market, Masters’ hypothesis, Granger causality.

This paper presents an empirical analysis of the significance of the long memory and asymmetry effects for forecasting conditional volatility and market risk on the commodity market based on the example of gold and silver. The analysis involved testing a wide range of linear and non-linear GARCH-type models. The aim of studying dependencies between rates of return and volatility was to select the optimum model. In-sample and out-of-sample analysis indicated that volatility of returns on gold and silver is better described with non-linear volatility models accommodating long memory and asymmetry effects. In particular, the FIAPARCH model proved to be the best for estimating VaR forecasts for long and short trading positions. Also, this model generated the lowest number of violations of Basel II regulations at the confidence level of 99%. Among the models studied, the FIAPARCH has the most elastic news impact curve, which translates into more possibilities to adjust to data. The results of the analyses suggest that within the period studied, the FIAPARCH model was the best predictive tool compared to the other models. This stems from the model’s ability to satisfactorily capture the effects accompanying price volatility of precious metals, i.e. asymmetry and long memory. The FIAPARCH model produced the lowest number of VaR violations (lowest risk of the model) for all series, which means that it seems to be the most advantageous predictive model with respect to gold and silver from the point of view of financial institutions. Attention was also paid to the prevalence and significance of long memory and asymmetry effects, which should be taken into account when using GARCH-class models.

This paper focused on studying the agricultural commodity prices in India and it's extreme volatility due to many reasons such as government interference, growth, market forces factors, regular floods and droughts, transport and warehousing problems, etc. These are contributing factors to demand fluctuations. In this case, the future market plays an important role in the economy. The demand for commodity futures has three particular economic functions: price discovery, price risk management, and price volatility. The future market plays a key role in the process of price discovery. The main aim of this system is to regulate prices to minimize uncertainty, to provide price signals to market traders for futures spot prices through the price discovery phase. So, this study emphasized the role of the derivative market in reducing the volatility of agricultural commodity prices in the Indian market. Keywords: volatility, future market, derivatives

The article aims to find quantitative characteristics of the mechanism of enterprise risk sustenance from the point of view of rivalry between commodity market participants. Our analysis of publications revealed the root cause of the lack of known enterprise control systems, including the “Industry-4”technology: this is the lack of their activities formalization and the lack of resources’ quantity for such a description. The method of this mechanism and rivalry quantitative description based on previously devised enterprise model and published data on the market participants’ activities; using the enterprise math model, math models of various sizes markets with cyclical declines in consumption created and further vector model of rivalry in the market created. A quantitative definition of risks in the enterprise activities is given; owing to what fundamental expression for the moment of the crisis risk appearance found. The obtained quantitative ratios are necessary, significant, and repeatable for various sizes markets, due to which uncertainties in the enterprises control of various infrastructures reduced.

The commodity futures curve is viewed as a market-based path forecast, a term structure, optimizing multivariate loss preferences. Based on the forecast decision setting, we apply estimation of flexible multivariate loss functions, which reveal the preference term structure along the futures curve, which can be flat, smoothly sloping or oscillating, rotating among optimism, pessimism and symmetry. Evidence from the thirty main world commodities around the global crisis period, accommodates the futures curve forecast rationality questioned in the literature, suggesting the presence of joint preference asymmetries for longer maturities and symmetries for shorter ones. This reveals joint optimistic preferences for most commodities until 2004, evolving into oscillating preferences rotating within the term structure from symmetry to pessimism and optimism in 2005-2008 and finally back to weaker optimism until 2013.

Trend following strategy is a popular strategy that investors often use in trading around the world. Stocks are bought during an upswing and sold during a decline, the two main phases of the trend-following trading technique. This research evaluates the performance of the trend-following strategy in the Chinese commodity market by systematically employing quantitative methods to trade and get back test results for performance evaluation. The main trading indicator for this research is DMAC (Dual Moving Average Crossover) with a trend indicator called ADX for adjustment. As a kind of technical analysis, so-called “Dual Moving Average Crossovers” are often cited as providing reliable signs for discerning future stock price movements. By employing these indicators and systematically backtesting on 21 commodity futures for ten years, the research discovers that DMAC does not perform well (negative annualized return and sharpe ratio) from 2011 to 2021. By refining the strategy which is to replace DMAC with MACD (Moving Average Convergence/Divergence) and abandon ADX, the backtest result performs much better. The refinement suggests that the utilization of different trading signals/indicators will lead to a different performance of the trend following strategy in the Chinese commodity market. The research concludes that the trend following strategy is worthy of exploring in the Chinese commodity market in terms of using different trading indicators.

Topicality. In market conditions, business entities apply new methods and approaches for effective functioning and increase the competitiveness of their products, strive to increase added value and reduce costs, including logistic. The formation and implementation of logistics development at the micro- and macro-level is an urgent task for both individual business entities and the state as a whole. The correct definition of the internal content, essence and nature of logistics development serves as the first stage for the development of mechanisms for the formation of effective logistics of commodity markets, state policy in the market dimension and substantiation of a set of methods and tools for regulating commodity movement processes both between business entities and between sectors of commodity markets. Therefore, logistics development is an effective and purposeful process for solving the above-mentioned tasks.Aim and tasks. The purpose of the article is to generalize theoretical approaches to the definition of the concepts of «logistics» and «development» in order to optimize logistics activities and further logistics development of the country.The main tasks that the author sets for himself: to conduct a content analysis of the concepts of logistics and development; define approaches to the category «development», define the concept of «logistics development».Research results. Conducted content analysis of the «development» and «logistics» categories, four approaches to defining the concept of «development» are highlighted - philosophical, natural, economic and logistical, and their essence is revealed, the proposed author's definition categories «logistic development of the commodity market». It was established that there is no definition of development, the object of which is the logistics chain.Conclusion. According to the results of the content analysis of the categories «development» and «logistics», approaches to the definition of the concept of «development», the author's definition is proposed categories «logistic development of the commodity market», namely: the process of forming an effective logistics chain of the F&B market, transforming its structure in the direction of developing (activating) links of processing and storage of products, which make it possible to ensure the expanded reproduction of goods movement in the market and to optimize the chain added value.The essence of such scientific approaches to the concept of «development» as philosophical, natural, economic and logistical is revealed.

This article critiques the application of Karl Polanyi's port of trade model to the development of Livorno, which has often been ascribed to commercial brokerage across cultural, political, and ecological frontiers. Livorno's neutrality during times of war and its position in the corsair and privateering economies would appear to support just such an interpretation of Livorno's growth. Nevertheless, while such interstitial roles were real, by the 1640s they were subordinate to the larger currents of regional and long-distance trade. Livorno's development is better explained with reference to the rise of commodity markets as entrepôts for managing far-flung distribution networks. The Tuscan port's rapid rise should be understood as an integral phenomenon of early modern capitalism, more akin to places such as London or Amsterdam than to the ports of trade studied by Polanyi.

Rapid development of the Internet financial industry has brought great positive impact on the innovation of the financial industry, but it also faces the test of high risk. Only the effective innovation of Internet financial commodity economy market management can effectively improve the operation level of financial supervision system. Therefore, many people in the financial industry regard the management of commodity market of Internet economy as the focus of construction. Therefore, this paper puts forward the research on the commodity market management system of Internet Finance virtual network economy. Through the investigation and analysis of this paper, we can see that Internet finance can enrich the structure of the financial industry and improve the efficiency and competitiveness of financial work. Based on the current situation of Internet financial market management system, this paper studies the satisfaction of Internet Finance in different people's daily life. Through comparative analysis, it shows that both the employees and the students are more than 75% satisfied with the Internet financial market management, which provides a guarantee for the specific implementation of the Internet financial market management.

The article deals with the basis and conditions of administrative liability for abuse of a dominant position in the commodity market, provided for in article 14.31 of the Code of Administrative Offences of the Russian Federation (CAO RF). It is concluded that in establishing the dominant position of an economic entity in the commodity market, the Antimonopoly authority must establish the fulfillment of all the necessary conditions specified directly in the Federal Law on protection of competition (2006), and not only the conditions determined on the basis of the Order of the FAS of Russia dated 28.04.2010 № 220. The admissibility of qualification under art. 14.31 CAO RF of actions (inaction) of an economic entity only after receiving notification of the Antimonopoly authority of dominance is proved. Proposed and substantiated measures for increasing preventive orientation of antitrust regulation to all kinds of abuse by economic entities dominant position including fixing in antitrust Institute request on the proper behavior of the entity.

This article provides new insights on the changes in the dynamics of price correlations and spillover effects in the commodity market. Using US-traded futures price data at a 1-min frequency over the 2002–2017 period, we consider the interaction within soft and grain commodities and between these commodities and oil. We rely on a recently introduced volatility model – the realized Beta GARCH model of Hansen et al. (2014). Our results reveal that soft commodities were segmented prior to 2008 and became correlated thereafter. The nature of the increase in correlation is only temporary. The correlations within grains – already significant and positive – increased only marginally, indicating that this group has been less affected by recent events. The correlation between oil and agricultural commodities, which reached its peak in 2008, has also reverted to pre-crisis level. Spillover effects between oil and commodities have become more prominent prior to the commodity price crash. However, this increase in volatility transmission tends to precede the increase in correlations. Finally, the impact of these findings on the performance of hedging strategies and optimal portfolio weights is discussed. Our results are important for investors exposed to the commodity market as they show that while the diversification benefits of investing in this market have decreased, volatility transmission risk and hedging costs have increased.

It is generally believed that the international commodity markets’ downturn is attributed to China's stock markets collapse. To verify this, this paper assesses the influence of Shanghai (securities) stock exchange composite (SSEC) index on S&P GSCI total returns, a leading commodity composite index from August 31, 2006, to September 19, 2016. We implement wavelet transform to extract the approximations of SSEC and GSCI. Furthermore, we use the state space model to determine the margin between SSEC and GSCI as well as display the margin dynamic evolution over time. The margin denotes the units of GSCI change when SSEC increases a unit, which can display China's stock (equity) influence (CEI) on the international commodity market. We found that CEI reached its peak in 2007, then experienced two steep declines and an immediate mild rebound, and eventually hovered at a historically low level. Moreover, each inflection point of CEI could correspond to a specific international economic event, which indicates that the change of CEI is influenced by exogenous variables, especially the absolute increment of GDP and the U.S. dollar index. The influence of China's stock market on the international commodity market is not as great as believed and the diversification benefit still exists for the construction of portfolio. In addition, the change of China's stock market influence is closely related to exogenous factors, which deserves more attention for policy making to avoid unnecessary trade and monetary friction owing to misjudgment.

The study aims to examine the existence of a correlation between the stock prices of the energy sector, commodities prices of the energy sector, and market indices. The study uses an empirical approach to develop various VAR (Vector Autoregression) with Variance Decomposition Models for each company under the energy sector indexed in NIFTY50 by considering daily prices for 3 years. For a comparative study, the data have been divided into two parts. The first part is considered pre-COVID era, i.e., from July 1, 2018, to December 31, 2019, and the second part is considered post-COVID era, i.e., from January 1, 2020, to June 30, 2021. While observing the estimates of VAR of different companies, it can be said that crude oil is significant in most of the models during pre-COVID whereas, during post COVID, lag term of crude oil and NIFTYENGERGY are significant. On the other hand, while observing the estimates of variance decomposition in all the VAR models, the first lag term of the particular company's share price is strongly endogenous. In comparison, the other independent variable, i.e., lag term of the price of crude oil and natural gas, values of NIFTY50 and NIFTY ENERGY are strongly exogenous to the stock prices of the energy sector.

This study performs a joint analysis of jump activity for commodities and their respective volatility indexes; it also compares the results thereof to those for equities. Exploiting a property of affine jump-diffusion models (i.e., that a volatility index quoted on the market is an affine function of the instantaneous volatility state variable), we perform a test of common jumps for multidimensional processes to assess whether an asset and its volatility jump together. Applying this test to the crude oil pair USO/OVX, the gold pair GLD/GVZ, the S&P500/VIX, and three stock/volatility index pairs, we find strong evidence that these assets and their respective volatility indexes do not jump together. However, a copula analysis shows that for the equity index and individual stocks, there is a dependency between the jump sizes in the asset and in the volatility index. In contrast, for the commodity market, this dependency occurs only after decomposing the jump sizes that affect the asset into positive and negative components.

This study examines the volatility and seasonality influence on the price discovery of agricultural commodities. The present study intends to review price volatility that has been extensively researched over the last decade. The Web of Science is chosen to extract the data. The PRISMA 2020 flowchart is an eligibility criterion used in the data selection and rejection procedure. Following a thorough evaluation, the final fifty-four studies have been chosen for the review. The data classification is mainly discussing price discovery of agricultural commodities, volatility spillover, and its impact on the agricultural commodity market. Findings of the study signifies agri commodities like corn and soybean’s future marketplace is more competent in price discovery of agri commodities compared to the cash market.

As a consequence of the COVID-19 pandemic outbreak, most commodities experienced significant price drops, which were expected to continue well into 2020. As a result, the Markov switching model is used to study the influence of policy uncertainty and the COVID-19 pandemic on commodity prices in the USA. Commodity markets are stimulated by economic policy uncertainty, according to results from a two-state Markov switching model. In both high and low regimes, economic policy uncertainty (EPU) influences the commodity market, according to the study's findings. However, in the high regime, EPU has a greater influence on the energy and metal sectors. EPU has different influences on commodity markets in high- and low-volatility regimes, according to this study. There is a wide range of correlations between COVID-19 outcomes and EPU and how the prices of natural gas, oil, corn, silver, soybean, copper, gold, and steel respond to these tremors, in both high- and low-volatility tenure. Oil and natural gas, on the other hand, are unaffected by shifts in COVID-19 death rates under either regime. Results show that in both high- and low-volatility regimes, the demand and supply for most commodities are responsive to historical prices.

SARS-Cov-2 was first reported in Wuhan, a town in Hubei Province of China with a population of 11 million in December 2019, following an outbreak of non-pneumonia a clear cause. The virus has now spread across the globe to more than 200 countries and territories, and the world health organization (WHO) described it as a pandemic on 11 March 2020. On the economic front, COVID-19 has led more than 200 countries partial or totally lockdown, disrupted global supply chain, and induced a significant fall in both economic activity and financial asset prices. United States, being one of the highest country affected by the Virus with 80.9M Cases, 45.8M Infected, and 1.77M Deaths as at the 28th of December 2020. Recently vaccines have been developed but the contamination wave keeps on increasing every day. On the socioeconomic front, COVID-19 has led more than 200 countries into partial or total lockdown, disrupted global supply chains, and induced a fall in both economic activity and financial asset prices. It’s of prime importance to study how COVID-19 impact’s U.S commodity market. The commercial and financial effect of COVID-19 The impact of COVID-19 are unclear because the public health crisis is still unfolding. There is limited amount of studies concerning this topic, because the crisis is still unfolding. The objective of this study is to investigate the impact of COVID-19 on U.S commodity market. The author uses extreme bound analysis for market interpretation and sourced data from the Federal Reserve Economic Database (FRED) and Our World in Data COVID-19, from January 2 to November 16 2020. The results of the commodity market show that, Foreign Exchange and Stringency Index are the only determinants of commodity Prices from Leamer EBA approach. Furthermore, The results of the normal model show that, TED spread (i.e. the difference between the 3 months Treasury bill and the 3-month LIBOR based in U.S. dollars), and Total Death are robust determinants of Oil Prices while the generic model highlights the importance of TED spread, Total Deaths. In addition, the correlation matrix show that, Foreign exchange and the price of Bitcoin has and impact on Gold Prices, a significant negative relationship with Stringency index. On the other hand, Total Deaths of COVID-19 has a significant negative relationship on Oil prices.

The article is devoted to the issues of increasing the efficiency of economic policy of the state and the region through the mechanism of counteraction to systemic risks that arise as a result of shadowing of market operations. The commodity market is the object of study. Consumer behavior of households is the subject of research. The purpose of the study is to identify systemic threats from the shadowing of the commodity market of Ukraine for the economic policy of the state and the region. Shadow activity in the commodity market is characterized as an institutional trap that has a high ability to reproduce. It significantly reduces the effectiveness of economic policy and undermines public confidence in government at the central and regional levels. The authors estimated the volume of the shadow segment of the commodity market for the regions of Ukraine in 2019. For this purpose, the method of comparing household monetary expenditures of households and the volume of total retail turnover was used. As a result, a significant unevenness of the regions of Ukraine was revealed according to the criterion of the scale of shadow operations in the goods market. The concentration of shadow operations in some regions is hidden, so it requires the central government to make significant efforts to minimize the negative consequences by improving regional and sectoral policies. The hidden nature of this threat over time leads to the formation of a distorted perception of shadow households as a correct social norm. The hidden nature of this threat over time leads to the formation of a distorted perception of shadow households as a correct social norm. This is the highest level of risk, so the government should pay close attention to this issue. Measures to counter the spread of the shadow economy are proposed for the economic policy of the region. Among them are: targeting the volume of shadow transactions in the goods market in the process of program-targeted planning; simplification of permitting and approval procedures for the organization of legal retail trade; development of infrastructure for fairs; systemic measures to de-shadow network marketing and e-commerce; strengthening control over the mechanism of financing social transfers; popularization of tax culture among young people.

This paper investigates the effect of economic policy uncertainty (EPU) on China’s agricultural and metal commodity futures returns across quantiles. We address this issue using the panel quantile ...

Global commodity and capital markets have undergone dramatic structural changes associated with financialization. But, in spite of this, the literature on financialization neglects questions about ...

The literature on market integration explores the development of the commodity market with data on prices, which is a useful complement to analysis of trade and the only feasible approach when data on trade are not available. Data on prices and quantity can help in understanding when markets developed, why, and the degree to which their development increased welfare and economic growth. Integration progressed slowly throughout the early modern period, with significant acceleration in the first half of the 19th century. Causes of integration include development of transportation infrastructure, changes in barriers to trade, and short-term shocks, such as wars. Literature on the effects of market integration is limited and strategies for estimating the effects of market integration are must be developed.

SARS-Cov-2 was first reported in Wuhan, a town in Hubei Province of China with a population of 11 million in December 2019, following an outbreak of non-pneumonia of a clear cause. The virus has now spread across the globe considerably more than 200 countries and territories, and the world health organization (WHO) described it as a pandemic on 11 March 2020. On the economic front, COVID-19 has led more than 200 countries partially or totally lockdown. This situation disrupted global supply chain, and induced a significant fall in both economic activity and financial asset prices. Canada, being one of the country affected by the Virus with 731 000 Cases, and 18 622 Deaths as at the 21 of January 2020. Recently vaccines have been developed but the contamination wave keeps on increasing every day. On the socioeconomic front, COVID-19 has led more than 200 countries into partial or total lockdown, disrupted global supply chains, and induced a fall in both economic activity and financial asset prices. It’s of prime importance to study how COVID-19 impact’s on Canada’s commodity market. The commercial and financial effect of COVID-19. The impact of COVID-19 is unclear because the public health crisis is still unfolding. There is limited amount of studies concerning this topic, because the crisis is still unfolding. The objective of this study is to investigate the impact of Covid-19 on Canada’s Commodity markets. The author uses extreme bound analysis for market interpretation and sourced data from Bank of Canada and Our World in Data COVID-19, from January 1 to December 31 2020. The result of the study show that, Total deaths per million have an impact on the monthly Bank of Canada’s commodity price index of energy because of the robust relationship between them. Furthermore, the result also show that, commodity price index of energy and total deaths per million are determinants to total commodity price index. Furthermore, commodity price index excluding energy, commodity price index of energy, commodity price index of minerals and metals, commodity price index of forestry and total deaths per million are robust determinants of monthly Bank of Canada total commodity Prices. M.BCNE, M.ENER, M.MTLS, M.MTLS, M.FOPR, M.FOPR, M.AGRI, M.FISH, TC, TD have a positive relationship with MBCPI. M.BCPI, M.BCNE, M.MTLS, M.AGRI and M.FISH.

The agricultural commodity market has two sections—the futures and spot market. Even though these two markets operate in a different way, they are interlinked. In this study, we focus on the lead–lag relationship between spot and futures prices of Agri Index. We have used the AGRI futures index and AGRI spot index, and have employed Augmented Dickey-Fuller test to examine a unit root in the data. To analyse the lead-lag relationship between the log of futures price and log of spot price, we employ the Johansen co-integration test, the error correction model and the Granger causality tests. The study has taken the 632 observation of daily data from April 2016 to September 2018. The results suggest that the futures market leads to the spot market.

In this paper, two comprehensive mathematical approaches: cubic piecewise polynomial function (CPPF) model and the Fourier Flexible Form (FFF) model are built into asset pricing models to explore the stock market risk, commodity market risk and overall business conditions in relation to US stock returns as a modelling experiment. A selection of knots and orders are applied on the models to determine the best fit coefficients, respectively, based on Akaike Information Criteria (AIC). The classic risk coefficient along with downside and upside counterparts are estimated in a non-linear time-weighted fashion and are subsequently adopted as risk factors to investigate the explanatory and predictive power to stock returns. It is found that time-weighted classic, downside and upside risk coefficients of all three domains provide significant explanatory power to current stock returns, while the predictive power appears to be weak. The findings fill the gap in literature, specifically on both investigating and pricing the time-weighted risk. This paper innovatively employs the Aruoba-Diebold-Scotti (ADS) real business index to measure the business conditions in macroeconomics context. The methodology proposed in this paper embeds advanced mathematical approaches to provide robust regression estimation. The application of proposed models enriches the dimension in pricing risk in stock market and wider financial market.

The scientific publication considers the state and development of elements of the organizational and technical component of the infrastructure of the commodity market in terms of strengthening the introduction of information technology at all stages of the market process and the achievements of the scientific and technical process. Such tendencies justify the need for further research in the chosen direction and determine their relevance and timeliness. The purpose of this publication is to determine the strategic prospects for the development of organizational and technical component of the infrastructure of the commodity market given the trends in the commodity market. Organizational and technical infrastructure is one of the three components of the overall market infrastructure of the commodity market. Attention is paid to the definition of components of this type of infrastructure and some of them are considered in detail. In particular, commodity exchanges and the functioning of the exchange market, transport infrastructure and the importance of creating logistics chains at the regional level and identified other areas of development, warehousing and areas for improvement. It is determined that the organizational and technical support of the market process is the introduction of technologies that constantly require enhanced information exchange, ie depends on the level of volume and complexity of data processing. Therefore, the interaction of commodity market participants is informative and this trend will intensify in the strategic perspective. Information technology determines the special nature of modern competition, influencing the speed of economic processes in the commodity market and the speed of response to the actions of a competitor. In the world of informatized economy, the ability to constantly and continuously adapt is a prerequisite for successful business. Research has shown that the current development of market processes is influenced by two trends - the rapid intensification of activities related to direct marketing and the use of information technology as a means of improving market processes, including all organizations in the infrastructure of the commodity market. To ensure consistency in the formation of the infrastructure, the commodity market must also take into account the development of financial and credit and information and consulting component, which also requires further in-depth research. Keywords: infrastructure, commodity market, information technologies, strategic development, innovation, exchanges, warehousing, logistics.

In market conditions, entering a new foreign market is always an urgent problem for all types of commodity market units, which puts forward special requirements for the effectiveness of the economic management system, and also requires solving marketing research tasks, differentiating and segmenting the commodity market. The article gives a general methodology for marketing segmentation and positioning in the product market on the basis of which you can really assess the condition of the product market, the potential of the enterprise and the formation of its future activities. The issues of marketing segmentation and forecasting of various units of the commodity market using neural and neuro-fuzzy adaptive systems with the aim of exploring opportunities to enter a new foreign market are considered.

This paper presents a conceptual model for an online agricultural commodity market that empowers farmers with a better price determination mechanism through collective marketing and Blockchain smart contracts. The model makes collective marketing possible by generating Many-one-Many relationships between farmers, farmers' groups, and buyers. While collective marketing improves farmers' bargaining position leading to higher rates, trust enabled by Blockchain smart contracts facilitates farmers to establish deals with buyers who offer the best rate transforming the commodity market into a sustainable market.

Commodity market is one of the most important element of the global economy as a global mechanism of valuation and distribution of goods, it has additionally become a kind of barometer of investor attitudes. This applies to companies whose operations are strongly related to the commodity market. The impact of market risk of commodities on the value of a company and its solvency is possible, when there is a correlation between the prices of commodities and share prices of the company. These types of relationships are not yet fully understood. This is due to the complexity of the processes that shapes the relationship between commodity markets and stock market. The purpose of this article is to assess the credit risk of listed companies whose activities are related to commodity markets, which means they are exposed to the market risk of commodities. Two stock markets have been selected for this purpose: the Italian market with a mature and well-established position in the global capital trading system and the stock market in Poland as representatives of developed economy, characterized by different stage of institutional development. In the opinion of the authors, companies with a significant difference in the probability of default for the baseline and crisis scenario are more exposed to the impact of commodity market risk.

PurposeThis study aims to systematically reveal the complex interaction between uncertainty and the international commodity market (CRB).Design/methodology/approachA 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.FindingsFirst, 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.Originality/valueFirst, 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.

Winter storm Uri impacted large swaths of the Continental United States, including the State of Texas, over the period Feb. 13 -- Feb. 17, 2021. This research attempts to discern what the natgas and power commodity markets are anticipating for Texas for the First Quarter of 2022. As of this writing, natgas prices for Feb. 2022 are 7.49% (33 cents/MMBtu) higher, and power prices 37.8% ($18.67/MWh) higher, than their respective non-freeze benchmarks. While these numbers appear large, they should be set in perspective: If the spike in prices is as pronounced in duration and intensity as last year, markets are indicating a probability between 1.3% and 2.5% of a Winter 2022 recurrence of the events of Feb. 2021.

Trade and division of labour have been a key source of economic growth throughout human history and they have been widely researched, within the constraints of the available data. This chapter surveys the twin literatures on trade (based on quantity data) and market integration (based on price data). The first section outlines the development of commodity markets in the very long run, as measured by growth of trade and convergence of prices. Then, it discusses the contribution of technical progress in transportation and of changes (downward but also upward) in barriers to trade to these changes. The last section reviews the literature on effects od the development of markets. Scholars have made good progress in estimating the static benefits on welfare but the work on the dynamic effects is just beginning.

The paper highlights the main instruments of protection against price fluctuations on the agricultural exchange market of corn and analyses the use of hedging to minimize risks. Seasonality, fluctuations in production volumes, and natural and climatic conditions have a significant influence on the formation of prices for agricultural products. The combined influence of these factors increases the risks of agricultural business. Overcoming their negative impact on entrepreneurial activity in agriculture necessitates the use of progressive elements of market regulation, including hedging instruments. The agricultural sector of Ukraine is also marked with the inconsistency of regulatory mechanisms with the needs of subjects of market relations. According to the study, it was caused by the problem of price formation for agricultural products. The paper proves the necessity of using a scientific approach to solving problems and improving the national commodity exchange market to ensure its adequacy in the integration processes of the world economic space. The authors highlighted the main tools for protecting against price fluctuations in the agricultural exchange market of corn. They also analyzed ways to use hedging to minimize risks.

Accepting the price association and dependencies for agricultural products is one of the biggest challenges mainly when there is an outbreak of Covid-19. As per this issue, the present paper considers few selected food crops, i.e., corn, wheat, rice, and soy for three different countries, i.e., USA, India, and China to identify price movements and price associations in the short and long run. After understanding the nature of data, the cross-country causal effect analysis is conducted through Granger causality for all the crops that state a unidirectional relationship between countries. This output supports the evaluation of measuring the long-run and short-run association through the vector error correction model that states the soy price of the USA can be tracked from the movement of India soy price in long run with a high degree of positive relation as India is the highest producer of soy with a cheaper rate that is widely imported by the USA for its organic poultry and dairy feed. Even the price of corn China can be followed in the long run from the price of USA corn but it establishes a negative relation. Similarly, the USA price of rice can be tracked from the China price as it is the major rice producer and exporter globally. While the prediction of wheat prices for India can be well-known from the price movement of the USA in both the long run and short run with a negative effect. This outcome is well accepted as the data series has mean reverse throughout the period. This outcome is beneficial mainly to the farmers in price fixation and decision on trading at the national or international platform, and this is also useful for the investors to invest in agro sectors in post-Covid-19 eras rather than investing in an unpredictable stock market or indices.