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This study examines the influence of oil shocks on systemic risk spillover among the commodity markets. Specifically, this paper uses the DCC-GARCH approach combined with the TVP-VAR model to calculate risk connectedness and the GARCH-MIDAS model to explore how oil shocks from different sources affect the risk spillover effects among the commodity markets. The results are the following: First, there are significant risk spillovers among the commodity markets with important time-varying characteristics and with sharp changes in times of crisis. The industrial metals, agriculture, precious metals, and light energy commodity markets are risk recipients, and the energy and livestock commodity markets are risk exporters. Second, oil price shocks, particularly oil aggregate demand shocks, prominently affect the total risk connectedness among the commodity markets. In particular, the impact on the net risk spillover effect of different commodity market differs. [ABSTRACT FROM AUTHOR]

The outbreak of the war in Ukraine has had a profound and far-reaching impact on the global economy, with notable repercussions observed in stock markets, and particularly pronounced effects evident in commodities markets. This paper examines the connectedness network among 27 NATO stock markets, Russian stock market and a set of three commodity indices (energy, precious metals, and agricultural commodities) over the period 2017-2023. The empirical strategy consists of time and time-frequency connectedness metrics. The empirical results reveal that the connectedness structure has shifted during the Russian-Ukrainian conflict. Moreover, hit by a series of Western sanctions, Russia's stock market appears to be the most isolated of the considered markets during the war period. Furthermore, the energy, agricultural and precious metals commodities seem to be efficient hedging instruments for investors in the stock markets of the NATO countries during the war period. [ABSTRACT FROM AUTHOR]

The aim of this study is to enhance the understanding of volatility dynamics in commodity returns, such as gold and cocoa, as well as the financial market index S&P500. It provides a comprehensive overview of each model's efficacy in capturing volatility clustering, asymmetry, and long-term memory effects in asset returns. By employing models like sGARCH, eGARCH, gjrGARCH, and FIGARCH, the research offers a nuanced understanding of volatility evolution and its impact on asset returns. Using the Skewed Generalized Error Distribution (SGED) in model optimization shows how important it is to understand asymmetry and fat-tailedness in return distributions, which are common in financial data. Key findings include the sGARCH model being the preferred choice for Gold Futures due to its lower AIC value and favorable parameter estimates, indicating significant volatility clustering and a slight positive skewness in return distribution. For Cocoa Futures, the FIGARCH model demonstrates superior performance in capturing long memory effects, as evidenced by its higher log-likelihood value and lower AIC value. For the S&P500 Index, the eGARCH model stands out for its ability to capture asymmetry in volatility responses, showing superior performance in both log-likelihood and AIC values. Overall, identifying superior modeling approaches like the FIGARCH model for long memory effects can enhance risk management strategies by providing more accurate estimates of Value-at-Risk (VaR) and Expected Shortfall (ES). Additionally, the out-of-sample evaluation reveals that Support Vector Regression (SVR) outperforms traditional GARCH models for short-term forecasting horizons, indicating its potential as an alternative forecasting tool in financial markets. These findings underscore the importance of selecting appropriate modeling techniques tailored to specific asset classes and forecasting horizons. Furthermore, the study highlights the potential of advanced techniques like SVR in enhancing forecasting accuracy, thus offering valuable implications for portfolio management and risk assessment in financial markets. [ABSTRACT FROM AUTHOR]

Since the last two decades, financial markets have exhibited several transformations owing to recurring crises episodes that has led to the development of alternative assets. Particularly, the commodity market has attracted attention from investors and hedgers. However, the operational research stream has also developed substantially based on the growth of the artificial intelligence field, which includes machine learning and deep learning. The choice of algorithms in both machine learning and deep learning is case-sensitive. Hence, AI practitioners should first attempt solutions related to machine learning algorithms, and if such solutions are unsatisfactory, they must apply deep learning algorithms. Using this perspective, this study aims to investigate the potential of various deep learning basic algorithms for forecasting selected commodity prices. Formally, we use the Bloomberg Commodity Index (noted by the Global Aggregate Index) and its five component indices: Bloomberg Agriculture Subindex, Bloomberg Precious Metals Subindex, Bloomberg Livestock Subindex, Bloomberg Industrial Metals Subindex, and Bloomberg Energy Subindex. Based on daily data from January 2002 (the beginning wave of commodity markets' financialization) to December 2020, results show the effectiveness of the Long Short-Term Memory method as a forecasting tool and the superiority of the Bloomberg Livestock Subindex and Bloomberg Industrial Metals Subindex for assessing other commodities' indices. These findings is important in term for investors in term of risk management as well as policymakers in adjusting public policy, especially during Russian-Ukrainian war. [ABSTRACT FROM AUTHOR]

This study investigates the time-varying return spillovers among the gold and oil markets and the Chinese equity subsectors using a network system representation. The results of the statics analysis show that crude oil and the majority of equity sectors are the net transmitters of spillovers in the network, whereas gold is the net receiver of spillovers from the network system. Additionally, negative return spillovers are greater than positive return spillovers. The results of time-varying analysis based on the moving window technique show that the dynamic symmetric and asymmetric return propagation rise during several periods of financial difficulties, suggesting the existence of market contagion. This pattern of market spillover effects is particularly notable during the market stress period marked by the COVID-19 crisis. The analysis of dynamic network linkages uncovers the crude oil market and the banking sector as the biggest risk sources in the network system. Finally, we construct and compare the risk performance of portfolios under four different strategies, showing that gold is an effective hedging tool for risk in China’s stock market.

The relevance of the study is due to the fact that in the conditions of the financialization of the economy, shocks arising in one market can spread rapidly and intensively to other markets, generating the effects of financial contagion. This fully applies to the commodity markets, which occupy a large share of exchange trading. The resulting excess volatility risks should be taken into account both by financial market players when developing optimal portfolio strategies, and by the state when adjusting anti-crisis policy. The purpose of the study is to identify financial contagion in commodity markets during periods of financial stress caused by the pandemic and sanctions, to determine the direction and extent of intermarket contagion. The novelty of the study lies in the construction of stress indices to separate periods of increased volatility in commodity markets, in the application of statistical tests for the co-moments of the return distribution to identify the financial contagion between the markets of energy (oil and gas), precious and non-ferrous metals during the pandemic and sanctions. The result of the study is the identification of a period of increased volatility in commodity markets and its division into two sub-periods based on turning points in the stress index, establishing the direction and extent of financial contagion between commodity markets during these periods. It is concluded that stress in commodity markets is accompanied by intense financial contagion. Moreover, volatility contagion turns out to be higher than return contagion and even higher than contagion caused by anomalies in the return distribution. The main sources and receivers of contagion in different periods are the markets of precious and some non-ferrous metals, and in the period from February 2018 to December 2020, also the oil market. At the same time, the gas market before SMO has demonstrated relative independence from other commodity markets, which made it possible to recommend gas futures as a tool for hedging investment portfolios during a period of increased financial stress.

This study aims to employ a causal network model based on transfer entropy for the early warning of systemic risk in commodity markets. We analyzed the dynamic causal relationships of prices for 25 commodities related to China (including futures and spot prices of energy, industrial metals, precious metals, and agricultural products), validating the effect of the causal network structure among commodity markets on systemic risk. Our research results identified commodities and categories playing significant roles, revealing that industry and precious metal markets possess stronger market information transmission capabilities, with price fluctuations impacting a broader range and with greater force on other commodity markets. Under the influence of different types of crisis events, such as economic crises and the Russia–Ukraine conflict, the causal network structure among commodity markets exhibited distinct characteristics. The results of the effect of external shocks to the causal network structure of commodity markets on the entropy of systemic risk suggest that network structure indicators can warn of systemic risk. This article can assist investors and policymakers in managing systemic risk to avoid unexpected losses. [ABSTRACT FROM AUTHOR]

Copyright of Contaduría y Administración is the property of Facultad de Contaduria y Administracion-Universidad Nacional Autonoma de Mexico and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The Russian invasion of Ukraine contributed to soaring world market prices of many commodities with severe repercussions for many African countries. This study examines the implications of the 2022 world market price increases for wheat, fuels, and fertilizers for Ethiopia. Using a computable general equilibrium (CGE) model, the study shows negative impacts on GDP, wage rates, and households' consumption in the country. The effects of fertilizer and petroleum price changes are notable and unequal across production sectors. With increasing import prices of inorganic fertilizers, crop growing activities substitute inorganic fertilizers with animal manure reducing the use of manure as cooking fuel. The effects on urban households are more severe than the effects on rural households. Policies supporting biofuels and biogas digesters may dampen the adverse effects stemming from petroleum price surges. [ABSTRACT FROM AUTHOR]

In this paper, we introduce the concept of statistical arbitrage through the definition of a mean-reverting trading strategy that captures persistent anomalies in long-run relationships among assets. We model the statistical arbitrage proceeding in three steps: (1) to identify mispricings in the chosen market, (2) to test mean-reverting statistical arbitrage, and (3) to develop statistical arbitrage trading strategies. We empirically investigate the existence of statistical arbitrage opportunities in crude oil markets. In particular, we focus on long-term pricing relationships between the West Texas Intermediate crude oil futures and a so-called statistical portfolio, composed by other two crude oils, Brent and Dubai. Firstly, the cointegration regression is used to track the persistent pricing equilibrium between the West Texas Intermediate crude oil price and the statistical portfolio value, and to identify mispricings between the two. Secondly, we verify that mispricing dynamics revert back to equilibrium with a predictable behaviour, and we exploit this stylized fact by applying the trading rules commonly used in equity markets to the crude oil market. The trading performance is then measured by three specific profit indicators on out-of-sample data. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the economic impact of the COVID-19 pandemic on European and Chinese unsustainable and non-recyclable plastic markets, specifically those used for the production of Personal Protective Equipment (PPE). We explore exogenous economic and commodity price impacts on polypropylene, acrylonitrile and polyvinyl-chloride, via VECM and Granger causality analysis, with the results remaining robust under testing. We find that price shocks from rubber and EUROSTOXX are significantly correlated with PPE materials, to a greater extent than crude oil, unexpectedly relating price declines in PPE materials to factors beyond medical demand. This will aid a policymakers and industry understand the factors that affect the price of unsustainable and non-recyclable PPE materials, respond to the need for pandemic PPE provision and reduce the potential environmental impact of future pandemics. [ABSTRACT FROM AUTHOR]

Driven by its healthy and nutritious attributes, the US avocado market has experienced tremendous growth over the past two decades. This publication provides a comprehensive overview of US avocado production and trade. The US avocado market, marked by the decline of already limited US domestic production, is increasingly reliant on imports, especially those from Mexico. This overview aims to inform industry stakeholders and policymakers of the current state of the industry and offer insights into its sustainability.

Emtialar, yatırımları çeşitlendirmek ve enflasyona karşı korunmak için alternatif bir yol olarak görülmüştür. Bu nedenle yatırımcıların bir piyasanın düşüşü veya yükselişi sonrasında diğer piyasaların veya finansal varlıkların hangi yöne doğru hareket edeceğini öngörmesi, hızlı ve etkili kararlar almasında kritik öneme sahiptir. Bu çalışmada, emtia piyasalarının birlikte hareketi veri madenciliğinde yer alan birliktelik kuralı ile analiz edilmiştir. Bu doğrultuda çalışmada 20 adet emtianın 01.01.2010-01.08.2023 tarihleri arasındaki 3216 işlem günündeki birlikte hareketleri analiz edilmiştir. Çalışmada birliktelik kuralı analizleri, Apriori ve FP-Growth algoritmaları kullanılarak gerçekleştirilmiştir. Hem Apriori hem de FP-Growth algoritmaları ile üretilen birliktelik kurallarının tümünde Brent petrolün diğer emtialara eşlik ettiği gözlemlenmiştir. Bu sonuç, Brent petrol fiyatlarının yukarı veya aşağı yönde hareketinin, Brent petrol fiyatlarını yakından takip eden yatırımcılara, karar vericilere ve politika yapıcılara, diğer emtiaların hareketi ile ilgili yol gösterici olabileceğini göstermektedir. Petrolün ekonomik sistemi etkileyen stratejik bir enerji kaynağı olduğu gerçeği göz önüne alındığında, bu sonucun şaşırtıcı olmadığı ifade edilebilir.

Understanding agricultural commodity futures is crucial for efficient business operations. This study employs textual machine learning on 290,271 articles (2009–2020) focusing on corn markets, aiming to model the impact of news on corn futures pricing. Our novel approach enables the identification of seven distinct topics within corn news, offering a comprehensive view of the news coverage spectrum. Soybean biofuel news notably influences corn prices, while exports, weather and wheat news significantly impact pricing uncertainty. These insights deepen our understanding of factors shaping corn futures and highlight machine learning's potential in agricultural economic analysis, enabling more accurate market predictions and policy decisions. [ABSTRACT FROM AUTHOR]

The transition to a low-carbon economy is imperative to reduce reliance on fossil fuels and mitigate pollution emissions. This preposition also aligns with the United Nations Sustainable Development Goals (SDGs-13), which highlight the climate change action. In this vein, Brazil has implemented the Decarbonization Credit (CBIOS) program to incentivize biofuel production and promote environmental sustainability through carbon credit emissions. To this end, the present study evaluates the effectiveness of the CBIO contract as a hedging tool for investors in the face of energy price fluctuations and decarbonization efforts. Specifically, we employ conditional dynamic correlation (DCC-GARCH) and optimal hedge ratio (HR) techniques to assess the relationship between CBIO and the futures and spot prices of sugar, oil, and ethanol. Our findings suggest that the current CBIO contract is not an effective hedge against energy spot and future prices. However, our analysis identifies a strengthening correlation between ethanol traded in Chicago and CBIO over time, highlighting the potential for an underlying contract to serve as an effective hedging tool in the future. Our study adds to the existing literature on carbon pricing mechanisms and their impact on financial markets, emphasizing the importance of sustainable energy policies and their potential to mitigate the risks associated with energy price volatility and decarbonization efforts. [ABSTRACT FROM AUTHOR]

After a brief synopsis of the main avenues of research on the industrial organization of food and agriculture, I introduce and discuss the six articles that make up this Special Issue of the Review. The articles draw on novel databases to explore price transmission and supply response in processing tomatoes; concentration and competition in the meatpacking industries; communication and collusion in broiler production; concentration and research competition in genetically engineered seeds; price transmission from agricultural commodities through to retail prices; and the effect of store entry on prices for nearby retailers. [ABSTRACT FROM AUTHOR]

The low correlation between commodities and traditional assets, particularly after the crash of the equity market in the year 2000, is seemingly a major factor influencing global investors' appetite to embrace commodities as a profitable alternative financial asset. In this paper, we critically and selectively provide the knowledge map of the connectedness of commodity markets based on the scientific articles published on the Web of Science (WoS). In doing this, we group the literature survey based on notable commodity markets and provide an overview of the empirical literature based on single‐ and cross‐commodity markets. The key finding of the literature survey is that there is connectedness within and across commodity markets, with evidence of time variations triggered largely by global financial crises. In addition, from 144 articles over the last two decades (1990–2021), significant conceptual clusters and networks arise, which suggest a close density of networks in terms of the keyword clusters, keyword plus co‐occurrences, country collaborations, and journal co‐citations. Furthermore, there are significant conceptual clusters that cover the association of connectedness type, commodity market, type of statistical analysis, association of major energy shocks, futures market, co‐movement, and association of transmission in stock and gold markets. Our analysis, therefore, suggests, among other things, the need for future research to analyze the pricing of pollution credits as the newest commodity market. This helps economic actors, investors, and policymakers have a better understanding of the dynamic behavior of commodity prices. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the interconnections among and within the Energy, Agricultural, and Metal commodities, operating in a risk management framework with a twofold goal. First, we estimate the Value-at-Risk (VaR) employing GARCH and Markov-switching GARCH models with different error term distributions. The use of such models allows us to take into account well-known stylized facts shown in the time series of commodities as well as possible regime changes in their conditional variance dynamics. We rely on backtesting procedures to select the best model for each commodity. Second, we estimate the sparse Gaussian Graphical model of commodities exploiting the Graphical LASSO (GLASSO) methodology to detect the most relevant conditional dependence structure among and within the sectors. A novel feature of our framework is that GLASSO estimation is achieved exploring the precision matrix of the multivariate Gaussian distribution obtained using a Gaussian copula with marginals given by the residuals of the aforementioned selected models. We apply our approach to the sample of twenty-four series of commodity futures prices over the years 2005–2022. We find that Soybean Oil, Cotton, and Coffee represent the major sources of propagation of financial distress in commodity markets while Gold, Natural Gas UK, and Heating Oil are depicted as safe-haven commodities. The impact of Covid-19 is reflected in increased heterogeneity, as captured by the strongest relationships between commodities belonging to the same commodity sector and by weakened inter-sectorial connections. This finding suggests that connectedness does not always increase in response to crisis events. [ABSTRACT FROM AUTHOR]

Purpose: This study was started on the main macroeconomic data (2010-2019), coming from the countries of the BRICS-plus group. While a significant contribution of these emerging economies to global economic output has generally been observed, this has also been accompanied by persistent domestic imbalance. Design/Methodology/Approach: Regarding the model, we state and contextualize the (long-term) risk co-incidence of the set of macroeconomic variables for this coalition of countries. Particular attention was paid to a valuation, description and forecasting model based on the calculation of the Dynamic Conditional Correlation (DCC) in a Generalized AutoRegressive Conditional Heteroscedasticity (GARCH) process on the time series of credit default swaps (CDS). Findings: The empirical analysis and calculations carried out have verified the significance of the parameters and confirmed the conditional dynamic correlation between the economies of the Brics “expanded”, especially in the presence of shocks, which also involve mutual contagion (temporary increase in DCC) and even interdependence (increasing DCC leads to new, less unstable levels). Implications/Originality/Value: The research offers a look at the most current trends in terms of international economic balances and the entire global system, focusing on commodity markets, financial derivatives and the impacts of trade at the dawn of the new BRICS-plus coalition.

The paper explores the structures of interregional economic interactions of two countries, Japan and China, that are different in terms of the scale of physical space and, accordingly, the density of economic activity. The methodological feature of the conducted cross-country study is the focus on the question of how far we can get starting from the position ‘all objects are unique’. The assumption of the existence of functional hierarchies is tested for the national economic spaces under consideration; the backbone of spatial economic interactions that form commodity markets homogeneous in terms of locational characteristics of supply and demand are constructed; system effects generated by the established structures of interactions are determined. As part of the search for evidence of the existence of functional hierarchies, real interregional balances are compared with theoretical balances built in accordance with the assumptions of the models of successively inclusive hierarchy of W. Christaller (without counter commodity flows between hierarchical levels) and A. Losch’s economic landscape (taking into account counter commodity flows between hierarchical levels). For comparison purposes, the centrality coefficient is used, the critical value of which in W. Christaller’s model is 100%, whereas in A. Losch’s model it is about 40%. The parameters of the existing functional hierarchies (mainly, the degree of homogeneity of national economic spaces with respect to interregional links and the ‘list’ of central functions) are determined using the method of dyadic factor analysis, and the system effects at different hierarchical levels – using the method of localized division of composite blocks of multipliers of interregional balances. The study has shown that the properties of economic spaces, indeed, depend on the parameters of physical spaces, but the degree of similarity of the structures of interregional interactions in countries with different parameters of physical spaces is not zero. In particular, the estimates indicate the existence of functional hierarchy in both Japan and China (the centrality coefficient in the two cases is about 40%), general multiplicative effects and the degree of self-sufficiency of the regions of the highest levels of functional hierarchies in Japan and China are comparable, whereas the values of system effects absorbed by these regions are different

Modern global economy is witnessing the creation of brand-new processes which were not seen before. Although the forecasts of economic growth in 2023 are being revised in the direction of improvement, nevertheless, a number of new problems require a lot of attention. Much of what is happening now is unprecedented – in particular, large-scale sanctions of the Western countries against Russia and China, changing processes in commodity markets, increased fragmentation of world economic relations, increased activity of regional associations of countries, etc. All this is complemented by high uncertainty in the geopolitical arena, which makes the problems much more acute and their solution even more difficult. Regulators and businesses in all countries are forced to shape their approaches taking into account the ongoing changes, largely changing the long-standing traditional algorithms for their solutions. The article shows a number of new problematic nodes that have been forming in the world in recent years. The sanctions policy of the Western countries against Russia, which has intensified since 2022, has directly or indirectly affected almost the entire world, increasing trends towards fragmentation. In Russia, under such conditions, significant changes are also required in domestic economic policy. The most important role in this could be played by monetary policy, changes in which may contribute to increasing the strength of the internal foundations of growth and transformation of the economy. At the same time, the changes may lead to a reduction in dependence on external financial markets and the external environment in general

AbstractWe study a new kind of nonzero-sum stochastic differential game with mixed impulse/switching controls, motivated by strategic competition in commodity markets. A representative upstream firm produces a commodity that is used by a representative downstream firm to produce a final consumption good. Both firms can influence the price of the commodity. By shutting down or increasing generation capacities, the upstream firm influences the price with impulses. By switching (or not) to a substitute, the downstream firm influences the drift of the commodity price process. We study the resulting impulse-regime switching game between the two firms, focusing on explicit threshold-type equilibria. Remarkably, this class of games naturally gives rise to multiple potential Nash equilibria, which we obtain thanks to a verification-based approach. We exhibit three candidate types of equilibria depending on the ultimate number of switches by the downstream firm (zero, one or an infinite number of switches). We illustrate the diversification effect provided by vertical integration in the specific case of the crude oil market. Our analysis shows that the diversification gains strongly depend on the pass-through from the crude price to the gasoline price.

Copyright of Journal of Research in Economics, Politics & Finance / Ekonomi, Politika & Finans Arastirmalari Dergisi is the property of Journal of Research in Economics, Politics & Finance and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study investigates the time-varying return spillovers among the gold and oil markets and the Chinese equity subsectors using a network system representation. The results of the statics analysis show that crude oil and the majority of equity sectors are the net transmitters of spillovers in the network, whereas gold is the net receiver of spillovers from the network system. Additionally, negative return spillovers are greater than positive return spillovers. The results of time-varying analysis based on the moving window technique show that the dynamic symmetric and asymmetric return propagation rise during several periods of financial difficulties, suggesting the existence of market contagion. This pattern of market spillover effects is particularly notable during the market stress period marked by the COVID-19 crisis. The analysis of dynamic network linkages uncovers the crude oil market and the banking sector as the biggest risk sources in the network system. Finally, we construct and compare the risk performance of portfolios under four different strategies, showing that gold is an effective hedging tool for risk in China's stock market. IMPACT STATEMENT: This paper analyzes return spillovers between crude oil, gold, and Chinese equity sectors using the Diebold-Yilmaz framework. Key findings reveal asymmetric spillovers, with negative returns dominating. It highlights gold's role in risk diversification and demonstrates that optimal portfolio strategies can significantly reduce downside risk, especially during financial crises. [ABSTRACT FROM AUTHOR]

Empirical research exploring the relationship between capital markets and energy prices plays a crucial role in shaping policies for the growth of the Islamic financial system. This study aims to investigate potential shock transmission and volatility spillover effects among Islamic stock indices from selected Middle East and Northern Africa countries as well as crude oil prices and natural gas, over the period from August 2007 to September 2020. Applying VAR-BEKK-GARCH representation, the results reveal the evidence of bidirectional cross-market shock and volatility spillover effects between Kuwait and Qatar Islamic stock indexes, crude oil prices, and natural gas. Moreover, the results indicate the existence of bidirectional/unidirectional shock and volatility spillovers between Islamic indexes and all other variables, meaning there are information flows between these variables in all four countries except Turkey. Regarding the results of volatility spillovers, there is no spillover effect between Turkey’s MSCI Islamic index and Brent crude oil. These findings bear significant implications for portfolio management, offering valuable insights to financial market participants for making improved portfolio allocation decisions. Also, comprehending the volatility transmission mechanism across these markets is vital to provide policymakers and regulatory authorities with insight into the impact of energy prices on Islamic stock markets. [ABSTRACT FROM AUTHOR]

African Aubergine is an indigenous tropical crop cultivated in Nigeria. Agricultural marketing creates incentives that accelerate the promotion of further production and consumption of harvested produce. Therefore, this study analysed African Aubergine marketing and income disparity among traders in Owerri, Imo State, Nigeria. Primary data collected via multistage sampling from ninety-five (95) respondents was analysed using descriptive statistics, marketing margin and efficiency analysis, Ordinary Least Square regression and Gini Coefficient index. The results show that the mean age, years spent in school, household size, trading experience and quantity of bags sold were 37 years, 8 years, 7 people, 9 years and 8 bags per month, respectively. The estimated marketing margin and efficiency index were 1,250 and 0.36, respectively. The estimated coefficients of Ordinary Least Square regression (R2) were 0.773. Thus, the variables (marketing experience, cost price, quantity sold and marketing cost) in the regression model accounted for 77% of the variation in the marketing margin of African Aubergine traders in the study area. The estimated Gini Coefficient was 0.59, indicating a moderate level of income disparity (inequality) in the sales income of the respondents. Several marketing constraints were observed in the study area. Therefore, this study recommends improved credit access and market information dissemination, commodity cost subsidisation, storage technology adoption, market infrastructure development and interventions, commodity price control and policy modification that regulates market activities. [ABSTRACT FROM AUTHOR]

In this study, the problem of hedging the risk of price slumping for a given volume of high-liquid assets is considered. One such possible hedging strategy may be the management of a portfolio constituted by the futures contracts holding a short position with respect to the basic asset. Besides that, the amount of having been sold futures is to satisfy the restrictions on the admissible minimal and maximum volume at each instant of time. The goal of such portfolio management is the increase of the weighted average price of futures sales through the speculative transactions realized in the real-time regime. Moreover, as feedback in the process of such management realization, one may use the prices of being executed market bargains only. The efficiency of the proposed hedging scheme is demonstrated by its comparison with the financial results achieved by the static hedging in the process of their both practical realizations based on the historical market prices. In this paper by the "dynamic hedging" concept, one means the execution of speculative transactions with futures taking a short position to provide the additional value of basic asset taking a long position. This notion is introduced in counterweight to the "static hedging" concept when the futures quantity does not vary in time and which goal is to preserve the basic asset value only. [ABSTRACT FROM AUTHOR]