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This study empirically investigates the return and volatility spillover among equity markets, Agro and non-Agro commodity markets. The study has used daily return data of BSE SENSEX as equity index, MCX iCOMEDEX, including Gold, Silver, and Crude oil, as non-agro indices, and NCDEX (AGRIDEX) as agro commodity indices to analyze volatility transmission between assets and markets using Generalized Autoregressive Conditional Heteroscedasticity (GARCH) and Exponential Generalized Autoregressive Conditional Heteroscedastic (EGARCH) model. The study has used dummy variables in the variance equation to identify the structural breaks during the COVID 19 pandemic. Further, Vector Auto Regression (VAR) pair-wise Granger causality test has been used to understand returns' causality. The study has found a bidirectional and unidirectional causal relationship between variables. The study indicates that NCDEX could influence all the variables except MCX Gold. In structural breaks with deterministic dummy variables during COVID-19, we have found mixed evidence of asymmetric impact on conditional volatility. The study also indicates a positive and significant asymmetric volatility effect in the case of MCX Gold and MCX Silver during the COVID-19 period. The study has specified that SENSEX dominates the inflow of information to all commodity markets. Also, the findings signpost that information flows from NCDEX to MCX Gold, MCX Silver, and MCX Crude Oil. JEL Codes: C32, E30, E44, F41, G15, Q43 [ABSTRACT FROM AUTHOR]

This study aims to analyse the future–spot pricing relationship in the context of most actively traded commodities traded on Multi Commodity Exchange of India, which is the leading commodity exchange in the country today. The study intends to compare the pricing relations of three popular commodity segments, that is, bullion and metal, energy and agricultural segments, to explore whether future–spot pricing relationships are similar for all commodity segments or not. The study has applied vector autoregressive (VAR) model on a sample of 13 commodities, categorized as 7 commodities from bullion and metal segment, 2 commodities from energy segment and 4 commodities from agricultural segment. The results of VAR model show that in India, the non-agricultural commodity sector constituting of bullion, metal and energy commodities behave close to each other, but the behaviour of agricultural commodities is quite different from non-agricultural commodities. As regards non-agricultural commodities, the futures market plays a quite dominant and significant role as compared to the spot markets. However, in case of agricultural commodities, the futures market has a comparatively less dominant and strong role, and their spot market also plays an equally strong role. A comparative study of the pricing relationships existing among the three commodity segments will be extremely helpful for farmers, traders, investors as well as the portfolio managers in choosing the most profitable commodity classes for trading, investment and portfolio optimization. Regulators and policymakers can plan and implement an appropriate regulatory framework to strengthen these markets, especially agricultural commodity market, to promote economic growth. [ABSTRACT FROM AUTHOR]

The article shows the impact of external economic sanctions on the Russian financial market. Special attention is paid to the currency, commodity and derivative markets of the Moscow Stock Exchange. The main focus of the work is the analysis of changes in market dynamics and pricing under the influence of political and economic constraints. Methodologically, the study is based on a comprehensive analysis of trade session data, including statistical processing and modeling of market reactions. The use of actual analytical methods made it possible to identify nonlinear dependencies and hidden patterns of behavior of market participants. The results of the study demonstrate that sanctions have had a significant impact on volatility, liquidity and market structure. The authors show the influence of political factors on financial instruments in a crisis, as well as develop new methodological approaches to analyzing financial market data. The results of the work can be used to coordinate trading strategies, develop market regulation policies and assess risks in conditions of political instability. The study also provides a basis for further analysis of the consequences of economic sanctions and the development of strategies to adapt to changing market conditions.

This study presents a comprehensive bibliometric analysis conducted in R Studio of the scientific landscape regarding commodity markets, trading strategies, sustainable production, integration of technologies such as machine learning, and their economic impacts, covering publications from 1974 to 2023. Employing a sophisticated query in Scopus, we meticulously compiled and analyzed data, revealing an annual growth rate of 10.46% in related scientific publications, with an average citation rate of 6.60 per document. The results indicate sustained interest in commodity research over time, with a significant increase observed in scientific production, particularly since the early 2008s. International collaboration is prominent, reflecting the global nature of research in commodity markets. Key themes such as “futures markets”, “commodity prices”, and “energy commodities” emerge from the analysis of keywords and bigrams, highlighting areas of interest within the field. Additionally, thematic mapping highlights emerging and niche themes in commodity research, providing insight into evolving trends and areas of specialization. Factorial analysis of keywords reveals the underlying structures of association between key concepts, shedding light on the intricate dynamics of research in the field of commodities. This research delineates the complex interplay between commodity markets and global economic dynamics, offering invaluable insights for academics, policymakers, and market participants aiming to navigate the intricate world of commodities in the digital age.

• Commodity investments promote sustainable markets. • Climate-transition strategies have bi-directional causality with sustainable markets. • Long-only commodity strategies mitigate market risk for regenerative markets. • Global sustainability leaders connect with climate-transition strategies in commodities. • Integrating climate-transition strategies fosters sustainable investment practices. By allocating investments towards commodities that align with climate-transition goals, environmentally conscious commodity investment strategies serve to promote and support sustainable markets, channeling capital towards sectors that prioritize environmental sustainability. Through the application of a quantile causality test, which examines the relationship between commodity-based strategies with a climate-transition focus and eco-friendly markets, over the period spanning from May 1, 2013, to May 25, 2023, our findings reveal a bi-directional causality relationship between different themes of sustainable markets and long-only climate-transition strategies in the commodity market across various market conditions. Furthermore, employing a quantile time-frequency connectedness approach allows us to discern that long-only climate-transition strategies in the commodity market exhibit lower long-run total connectedness with responsible and conscious markets compared to the short term. Consequently, these results suggest that transition-oriented strategies for commodities in a climate-conscious world not only mitigate market risk for regenerative markets in the long run but also indicate that different types of global sustainability leaders demonstrate a stronger connectedness with climate-transition strategies in commodities when compared to the Islamic sustainable market across a majority of quantiles and time horizons. In light of these findings, policymakers are urged to prioritize the long-term dimensions of climate-transition strategies in commodity markets by implementing new emission standards and environmental benchmarks. Additionally, the design and implementation of similar long-only climate-transition strategies in other markets would further enhance the long-term effectiveness of climate-conscious markets and foster stronger connections with responsible markets. our study underscores the significance of integrating climate-transition strategies into commodity markets and highlights their role in promoting sustainable and environmentally conscious investment practices. By directing investments towards climate-aligned commodities, policymakers and market participants can contribute to the long-term sustainability of global markets while fostering stronger connections between sustainable markets and climate-transition strategies in commodities. [ABSTRACT FROM AUTHOR]

This study presents a comprehensive bibliometric analysis conducted in R Studio of the scientific landscape regarding commodity markets, trading strategies, sustainable production, integration of technologies such as machine learning, and their economic impacts, covering publications from 1974 to 2023. Employing a sophisticated query in Scopus, we meticulously compiled and analyzed data, revealing an annual growth rate of 10.46% in related scientific publications, with an average citation rate of 6.60 per document. The results indicate sustained interest in commodity research over time, with a significant increase observed in scientific production, particularly since the early 2008s. International collaboration is prominent, reflecting the global nature of research in commodity markets. Key themes such as "futures markets", "commodity prices", and "energy commodities" emerge from the analysis of keywords and bigrams, highlighting areas of interest within the field. Additionally, thematic mapping highlights emerging and niche themes in commodity research, providing insight into evolving trends and areas of specialization. Factorial analysis of keywords reveals the underlying structures of association between key concepts, shedding light on the intricate dynamics of research in the field of commodities. This research delineates the complex interplay between commodity markets and global economic dynamics, offering invaluable insights for academics, policymakers, and market participants aiming to navigate the intricate world of commodities in the digital age. [ABSTRACT FROM AUTHOR]

This study employs minimum spanning tree and generalized forecast error variance decomposition methods to investigate the connectedness and risk spillovers across China's commodity sectors from January 2016 to December 2021. The results show that total connectedness within the commodity system is time varying. Chemical is the main risk driver, while other sectors occasionally dominate the system. These two methods achieve consistent results in identifying the systemically important sector and dynamic connectedness. In addition, we find that Chinese economic policy uncertainty and the investor sentiment index have significant impacts on total connectedness. Our findings have implications for preventing systemic risk for policymakers and managing commodity portfolio risk for investors. [ABSTRACT FROM AUTHOR]

Theoretical background: Silver has a great importance in the real economy, i.e. in photography, medicine, electrical, electronic and chemical industry. The growing importance of renewable energy sources and the development of solar panels, accumulators and batteries means that the role of silver is significantly strengthened. At the same time, it is also used as an investment asset providing variety in portfolios, on numerous occasions being a cheaper substitute for gold. Researchers are studying connections between those two resources and providing different perspectives on their future based on real economy and the financial market. Purpose of the article: The purpose of this article is to assess the current role of silver in the economy and answer the question of its investment prospective. Research methods: The research methods include elements of descriptive statistics and Pearson’s linear correlation between the prices of silver, gold and copper of the EUR/USD exchange rate and the Dollar Index. The research period is from 2000 to 2023. The analyzed data are daily. A measure of the amount of money in circulation in the United States is the M2 index, which includes cash in circulation and deposits. The inflation will be shown by the Consumer Price Index. The research method is also critical analysis of the subject literature. Main findings: We found that the change in the price of silver is strongly dependent on the USD, and specifically on its supply. A significant increase in the price of silver in the analysed period is related to the ongoing process of quantitative easing and financialization, understood as a growing volume of futures contracts. The occurrence of silver and copper in nature makes them dependent on each other, not only in determining prices, but also currently constitutes a barometer of industrial development. Thanks to the growing demand from industry, shortages of silver can result in serious consequences for production, for which reason they may decide to aggressively improve metal recycling efficiency or hoarding supply, which might push prices further up. However, investment use will not disappear due to its anti-inflation characteristics and may cause an increase in demand among investors who see gold as too expensive. [ABSTRACT FROM AUTHOR]

We propose a method for characterizing the local structure of weighted multivariate time series networks. We draw intensity and coherence of network motifs, i.e. statistically recurrent subgraphs, to characterize the system behavior via higher-order structures derived upon effective transfer entropy networks. The latter consists of a model-free methodology enabling to correct for small sample biases affecting Shannon transfer entropy, other than conducting inference on the estimated directional time series information flows. We demonstrate the usefulness of our proposed method with an application to a set of global commodity prices. Our main result shows that, despite simple triadic structures are the most intense, coherent and statistically recurrent over time, their intensity suddenly decreases after the Global Financial Crisis, in favor of most complex triadic structures, while all types of subgraphs tend to become more coherent thereafter. [ABSTRACT FROM AUTHOR]

Precious metals, and especially gold, have been inextricably linked to the history of man- kind since time immemorial. In modern times, gold has become a key factor in interna tional trade and the global economy. Therefore, for example, oil price fluctuations can be a predictor of gold price movements. Today, gold is seen as an independent financial asset that has become a popular tool for portfolio diversification, as well as a hedge against inflation. For this reason, this topic is considered to be very important and topical world- wide, attracting the attention of a wide range of people, both from the ranks of experts and the lay public. The article's objectives are to determine the position of gold in the commodity market, measure and compare the metal's historical price development from 2000 to March 2023, and, last but not least, assess how well artificial neural networks can forecast the movement of gold's market price. Evaluating whether gold is a good financial resource multiplier or custodian is one of the partial goals. In order to achieve this goal, time series analysis will be performed using artificial neural networks. It is feasible to track how accurately artificial neural networks can forecast changes in market prices by using this technique. The results show that the most common elements influencing the historical development of the price of gold were political, economic, technological, and social aspects. Additionally, it was shown that artificial neural networks are capable of accurately predicting the course of the price of gold and, thus, mimicking the genuine price development curve. Based on the obtained results, future studies might be carried out to compare a more extensive portfolio of commodities in order to build on this work. The contribution is beneficial both on a theoretical and practical level. It can serve inves- tors, analysts, economists, and other authors dealing with this issue. [ABSTRACT FROM AUTHOR]

The emergence of new computer technologies has made it possible to study (visualize) virtually any complex phenomena and processes literally on the display screen. The development of economic and mathematical modeling is influenced by the latest mathematical methods of nonlinear dynamics applied to any field and subject of research. The methods of classical statistics for forecasting economic time series are based on the mathematical apparatus of econometrics. This basing is carried out under the assumption that the observations making up the predicted time series are independent, due to which the necessary subordination to the normal law is satisfied. The latter, however, is the exception rather than the rule for financial and economic time series that have so-called long-term memory. The problem of forecasting and the closely related problem of assessing future economic risks in the conditions of turbulence in the development of the Russian economy, which has manifested itself in recent years, have become especially acute. In the conditions of observed turbulence, economic dynamics become poorly predictable by traditional methods and nonlinear. The direction (growth or decline) of indicators often changes. The research presented in the paper was carried considering the fact that by now there are no complete theories of forecasting time series with memory, which determines the relevance and need to develop new mathematical methods and algorithms to identify the possible predictability of time series with memory and build adequate predictive models. All of the above points to the relevance of developing a qualitatively new methodological approach that ensures the formation of informed management decisions in conditions of uncertainty and risk. The object of the study is time series of economic indicators. The subject of the study is the mathematical, statistical and instrumental apparatus of systems to support management decision-making and forecasting in the economy. The article describes the procedure for constructing models and presents the results of their testing for a linear cellular automaton, the exponential smoothing method, and the Holt–Winters method. During the study, large volumes of data were processed. At the same time, daily, weekly and seasonal indicators are considered. This made it possible to adequately describe and predict the nonlinear dynamics of time series. Using all three levels of analysis simultaneously allows you to get a more complete understanding of the dynamics of time series indicators. In the article, the proposed tools are tested using the example of platinum prices. The developed economic and mathematical apparatus is also applicable to other time series characterizing certain economic variables.

Bu çalışmanın amacı Amerikan Dow Jones indeksi emtia piyasasında yer alan Emtia Fiyatları kullanılarak, Amerikan emtia piyasasında spekülatif balonlarının var olup olmadığı, varsa toplam balon süreleri ve incelenen dönemde kaç kez spekülatif balon oluştuğunun belirlenmesidir. Çalışmada Amerikan Dow Jones indeksi emtia piyasasında yer alan 23 adet gerçek zamanlı Emtia fiyatları kullanılmış, 01.01.2012 tarihinden 01.08.2022 tarihine kadar 10 yıllık dönemde, aylık veriler üzerinden Phillips, Shi ve Yu (2015) tarafından literatüre kazandırılan genelleştirilmiş Dickey Fuller (GSADF) testi kullanılmıştır. Çalışma sonucunda incelenen zaman aralığında ve emtiaların tamamında fiyat balonu veya balonlarının çeşitli defa ve sürelerde oluştuğu belirlenmiştir. Emtia piyasasında bir balonun varlığı ulusal ve uluslararası ekonomi üzerinde yıkıcı etkiler yaratabilir. GSADF testlerinin gerçek zamanlı bir kabarcık detektörü olarak kullanılabileceği göz önüne alındığında bu yaklaşımın uygulanması ve politika yapıcılar tarafından yapılan zamanla değişen nedensellik testleri, dışsal veya dış kaynaklı risklerin değerlendirilmesi ve ölçülmesi açısından önemli faydalar sağlayabilir.

The system of civil contracts in the Russian Federation is of great importance for practice and is the subject of many years of detailed study of Russian legal science. The classification of contracts in civil law still causes controversy and debate among scientists, and so far no one has been able to propose a universal classification. The situation with the classification of contracts in the trade turnover of the Russian Federation is also difficult. Even the identification of such a category and this name raises questions and creates various opposing points of view among scientists. Indeed, in any textbook on corporate and trade law of the Russian Federation it is very difficult to find the term agreement in trade turnover, as well as to distinguish the types of such agreements. More and more often we come across concepts such as a commercial agreement or a trade agreement, however, such concepts should not be fully identified, although they have similar features. Perhaps there cannot be a uniform and accurate classification due to the fact that contract law is a very dynamically developing industry, which is constantly changing from a practical point of view, and practice, as we know, acts and does not think about fitting into the usual format, theories and classifications. In this article, a team of authors provides a unique systematization of the types of contracts used in trade turnover on the territory of the Russian Federation, analyzes and characterizes and assesses the relevance of various forms of concluding commercial contracts, and also highlights the prospects for the development of national contract law, which functions and is the most important component of trade turnover.

Copyright of Journal of Economics & Administrative Sciences / Afyon Kocatepe Üniversitesi Iktisadi ve Idari Bilimler Fakültesi Dergisi is the property of Afyon Kocatepe University, Faculty of Business Administration 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.)

Using the method of, this study constructs a tail risk predictor of the international commodity market to forecast US stock volatility. The in-sample results show that tail risk contains significant interpretive ability for stock volatility. Being of our interest, the tail risk predictor can successfully predict the US stock volatility from both statistical and economic viewpoints. The results of controlling 12 popular macroeconomic variables suggest that tail risk contains incremental information for stock volatility. To further confirm our findings, we examine the forecasting performance of the tail risk predictor for 12 industrial portfolios. [ABSTRACT FROM AUTHOR]

COVID-19 adversely affected all market mechanisms, shocked economies, halted business operations, and disrupted supply chains. This study examines macroeconomic instability by assessing the commodities market in highly traded regimes using descriptive statistical analysis, the Economic Policy Uncertainty (EPU) Index, and Markov Regime Swapping (MRS) for data covering February 26 to November 30, 2020. The findings indicate that most commodities respond to stock values in both high and low-volatility regimes of supply and demand. Additionally, commodity prices are still in a low volatility regime due to COVID-19-related market uncertainty. The returns for oil, corn, gold, copper, natural gas, silver, and soybeans adapt to low macroeconomic uncertainty and variable rankings for COVID-19. In contrast, changes in COVID-19 deaths had no impact on natural gas or oil under any regime. As agricultural commodities and valuable metals have less negative feedback to exogenous variables than any other commodities, this model is useful for regulators and stockholders. The results suggest that in a global pandemic, portfolio managers and other stakeholders should use priceless metals like gold as a short-term hedge against systemic market risks to achieve macroeconomic stability and facilitate a rapid recovery in the green economy. [ABSTRACT FROM AUTHOR]

Uncertain price movement in staple food commodities puts agrarian economies at risk if not monitored and managed consistently. Hence, an attempt has been made to analyze the price behavior and integration across major wholesale and retail markets for rice and wheat in India. Monthly data (July 2000 to June 2022) on prices viz. wholesale and retail were sourced from the Food and Agriculture Organization and analyzed using growth rate, instability index, seasonal price index, Bai-Perron’s test for structural breaks, Johansen’s test on cointegration, Granger causality test, and impulse response function. Findings indicated strong evidence of price dynamics in the selected markets in terms of spatial and temporal variation, clear-cut seasonality linking to production, and price divergence between wholesale and retail markets. Johansen’s test indicated a strong cointegration between wholesale and retail prices after accounting for structural breaks, exhibiting unidirectional-, bidirectional- and no causality. Impulse response analysis revealed that the selected wheat and rice markets are efficient in terms of ‘price discovery’ which takes place initially in the wholesale market, and is then transmitted to the retail market. The study advocates decision-making information to the producers, traders, and consumers who are interested in taking advantage of the price movement. It is concluded that strengthening the market intelligence and reducing the distortion in markets will improve the existing overall performance.

1- INTRODUCTION Considering that cryptocurrencies exhibit commodity characteristics such as demand shocks, high price fluctuations, etc., cryptocurrencies can be compared with the behavior of the gold and oil markets (except when there is uncertainty about the supply conditions of gold and oil. There is no such uncertainty in the cryptocurrency market). Therefore, due to the commodity nature of Bitcoin, the price of oil and gold can affect the price fluctuations of cryptocurrencies. It seems that cryptocurrencies can play the role of a safe haven for commodity market investors, so the cryptocurrency market can cover the fluctuations in gold and oil prices. Commodity markets, which this study focuses on gold, oil and cryptocurrencies, have a series of characteristics. It seems that the gold market has surpassed the cryptocurrency and oil market in absorbing information, while the cryptocurrency market has higher price fluctuations than the gold and oil markets. Empirical evidence shows that Bitcoin can have a close relationship with the commodity market. 2- THEORETICAL FRAMEWORK Every person in the financial markets who has an asset portfolio tries to increase or maintain the value of his asset portfolio. The position of each asset in the portfolio has two characteristics: return (price change) and risk (price volatility). The behavior of asset portfolio owners is such that they try to increase returns and reduce risk. In this framework, they buy or sell assets in their portfolio in order not only to prevent the value of their asset portfolio from decreasing, but also to increase the value of their wealth. This behavior of the capital owners leads to the creation of connections between the global markets, including oil, gold, and cryptocurrencies, so that their yield fluctuations are connected to each other through the risk spillover effect. The asset allocation models have been investigated in a practical way for about half a century. The most well-known asset allocation model is the mean-variance strategy (modern portfolio theory), which was first developed by Markowitz (1952) to describe the process of optimal capital allocation, assuming a fixed investment opportunity set, between different asset groups over a period. 3- METHODOLOGY In this study, the 𝑉𝐴𝑅− 𝑀𝐺𝐴𝑅𝐶𝐻 − 𝐺𝐽𝑅 – 𝐵𝐸𝐾𝐾 model has been used in order to investigate the asymmetric effects of turbulence spillover between the oil, gold and bitcoin markets because of the following advantages. First, this model has high flexibility. Second, in this model, unlike constant conditional correlation (CCC) models, the conditional correlation changes over time. In addition, it is possible to check several markets at the same time. The existence of the covariance equation makes it possible to examine the simultaneous relationship between two markets. In the BEKK model, the fluctuations of a market are affected by the fluctuations and shocks of other markets, the shocks of that market and the covariance of the markets. In other words, the effects of the markets on each other, which is reflected in the delayed covariance, have an effect on the fluctuations of the markets. These effects can be symmetrical or asymmetrical. Also, this model makes it possible to have a dynamic dependence between the fluctuations of the variables. The only disadvantage of this model is that it is not suitable for examining more than three or four markets due to the increase in parameters. 4- RESULTS & DISCUSSION The results indicate that the contribution of the memory of turbulence in explaining the current turbulence is greater than the impact of past shocks. The impact of past impulses and the memory of the turbulences of cryptocurrencies is high on the turbulences of this market. In other words, it can be said that fluctuations in the cryptocurrency market are significantly explained by the past impulses of this market. The results show that there is one-way turbulence spillover from the Bitcoin market to the gold market and the oil market, but the opposite is not true. The results of the study also indicate leverage effects in the markets. The leverage effects of the gold market shock along with the oil and bitcoin market shocks on the gold market are significant. The leverage effects of the oil market shock along with the gold and bitcoin market shocks are also significant on the oil market and the leverage effects are also significant for the bitcoin market. [ABSTRACT FROM AUTHOR]

This paper investigates whether the commodities market in India follows the random walk process and whether they deviate from the random walk theory (mean reversion). Daily log returns of commodity future index (MCXCOMDEX) and three sectoral indices are examined using the Augmented Dickey-Fuller test (ADF), GARCH, and E-GARCH model to test the random walk theory. This study confirms the existence of random walk for commodity index prices over the period January 2009 to December 2020. ADF test is used for investigating random walk behaviour. From the ADF test we conclude that the series has unit root problem and the index series is nonstationary. The results clearly show that the data is non-stationary possessing a unit root. The GARCH model was used to cater for volatility clustering, which is more pronounced in commodity prices. From the results obtained, it is observed that futures prices are showing volatility clustering and non-stationarity behaviour, which is in line with our research objective of finding the random walk in commodity index; there is a positive result of the existence of random walk. [ABSTRACT FROM AUTHOR]

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.

Uncertain price movement in staple food commodities puts agrarian economies at risk if not monitored and managed consistently. Hence, an attempt has been made to analyze the price behavior and integration across major wholesale and retail markets for rice and wheat in India. Monthly data (July 2000 to June 2022) on prices viz. wholesale and retail were sourced from the Food and Agriculture Organization and analyzed using growth rate, instability index, seasonal price index, Bai-Perron's test for structural breaks, Johansen's test on cointegration, Granger causality test, and impulse response function. Findings indicated strong evidence of price dynamics in the selected markets in terms of spatial and temporal variation, clear-cut seasonality linking to production, and price divergence between wholesale and retail markets. Johansen's test indicated a strong cointegration between wholesale and retail prices after accounting for structural breaks, exhibiting unidirectional-, bidirectional- and no causality. Impulse response analysis revealed that the selected wheat and rice markets are efficient in terms of 'price discovery' which takes place initially in the wholesale market, and is then transmitted to the retail market. The study advocates decision-making information to the producers, traders, and consumers who are interested in taking advantage of the price movement. It is concluded that strengthening the market intelligence and reducing the distortion in markets will improve the existing overall performance. [ABSTRACT FROM AUTHOR]

The paper seeks to answer the question of how price forecasting can contribute to which techniques gives the most accurate results in the futures commodity market. A total of two families of models (decision trees, artificial intelligence) were used to produce estimates for 2018 and 2022 for 21- and 125-day periods. The main findings of the study are that in a calm economic environment, the estimation accuracy is higher (1.5% vs. 4%), and that the AI-based estimation methods provide the most accurate estimates for both time horizons. These models provide the most accurate forecasts over short and medium time periods. Incorporating these forecasts into the ERM can significantly help to hedge purchase prices. Artificial intelligence-based models are becoming increasingly widely available, and can achieve significantly better accuracy than other approximations. [ABSTRACT FROM AUTHOR]

This article investigates the predictability of dry bulk shipments’ physical shipping costs while testing the predictive significance of derivative products. Accordingly, a comprehensive grid search procedure is needed to simulate combinations of model structures subject to a cross-validation process. In this regard, the intelligent model search engine (IMSE) is implemented as the search algorithm. The IMSE algorithm is selected due to its broad model space naturally consisting most of the traditional econometric model structures and advanced features, such as sample size optimization (also detects structural breaks), lag structure optimization (also reflects seasonality), among others. In contrast to the previous studies in the shipping market research, IMSE executes a relaxation of statistical significance criteria to target the out of sample predictive accuracy (instead of the goodness of fit). Sparsification is retained by utilizing a dropout procedure in line with the cross-validation process. The empirical results indicate the fact that predictive accuracy can be achieved or improved with a shorter sample period. More than half of the entire dataset has not been utilized in the final selections for most simulations. One-month and two-month maturity derivative contracts are tested for predictive features, while none of them is selected for the sparse model selections. [ABSTRACT FROM AUTHOR]

Copyright of Bulletin of The Mineral & Energy Economy Research Institute of the Polish Academy of Sciences / Zeszyty Naukowe Instytutu Gospodarki Surowcami Mineralnymi Polskiej Akademii Nauk is the property of Mineral & Energy Economy Research Institute of the Polish Academy of Sciences 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 article discusses the problem of reducing and replacing markets in the past, present and future. The author assumes that the market collapses when a new market takes its place or is another already existing one is being expanded. Two reasons for the replacement of markets are identified — technical innovations and the deterioration of business conditions in the established markets. In the first case, the replacement of the market comes from the supply side, in the second — from the demand side. When the conditions of activity worsen in one market, buyers’ demand flows from the collapsing market to another, which increases due to this. The author also divides market substitution processes into spontaneous and organized ones. It is noted that the coronavirus epidemic puts on the agenda the problems of curtailing three markets that make up a significant part of consumption in the modern economy: mass gatherings (entertainment events), public transport, and restaurants. It is indicated that economic science in these conditions should reorient its research from the problems of economic growth to the problems of changing the structure and composition of markets to achieve balanced development, taking into account current and future restrictions on activities in the areas mentioned above.

This paper proposes to identify Agricultural futures’ roles (Safe-haven, Hedge, and Diversifier) in the Thai stock market during 2000-2020 by applying a bivariate Cross-Quantilogram (CQ) approach. The CQ approach can examine the cross-quantile correlation between assets, while the traditional approaches (such as GARCH, DCC, and MSV) examine only mean-to-mean dependency structures. The CQ methodology can estimate the tail dependencies and directional predictability between financial assets more accurately than traditional methods since financial assets typically have a skewed and asymmetric distribution. The correlation between assets during the extreme market condition (tail dependencies) is important to classify a financial asset as a Safe-haven role. The agricultural commodities considered in this study are the most active asset categories in the markets (cereals, oilseeds, other soft commodities, and miscellaneous commodities). The results show that the agricultural assets are more explicitly correlated with the Thai stock market in crisis periods, such as a negative result in canola during COVID-19. Agricultural commodities, including wheat, oats, and canola, can play a strong safe-haven role in the Thai stock market, according to the lowest cross-quantiles (bearish market) data. According to the results of overall quantiles (normal situations), wheat, corn, canola, soybean, and sugar can all be used as hedges. The rolling windows for directional predictability, which show the time-varying CQ, confirm that these agricultural commodities can be served as Safe-havens throughout the study periods. Therefore, including these specific agricultural commodities (Safe-haven or Hedge) in a portfolio of Thai stocks will help lower risk and boost performance under normal and extreme downturn situations. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This empirical exercise explores different aspects of the time‐varying linkage between the commodity and equity markets in India, focusing on base metal futures. The Dynamic Conditional Correlation model (2002) and Diebold–Yilmaz spillover index (2012) are employed to ascertain the presence, pattern, direction, and magnitude of the connectedness between the returns of base metal futures and related equity indices over the period of 2006–2019. The study builds on a less‐studied "input" channel of linkage between the two, reckoning with the economic fundamentals of demand–supply interaction. Our results show that 36% of the return forecast error variance originates from spillovers, to which the contribution of equity indices is minimal. It indicates segmentation between the two markets which offer the scope for potential diversification. Further, base metal futures are the net transmitters and equity indices are the net recipients of spillovers. Positive correlations outweigh the negative correlations, in both frequency and magnitude. [ABSTRACT FROM AUTHOR]

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.

In this paper, we discuss the case of strong path dependency in asset prices from the theoretical and empirical standpoints. We demonstrate persistence of excess volatility in the gold spot price data that engenders excessive path dependence, whereas it is not the same with silver. We use the extreme value estimator (Rogers & Satchell, 1991) and the VRatio (Maheswaran, Balasubramanian, & Yoonus, 2011). The data for the study is for the period January 2001 – December 2016. We observe that the strong mean-reverting characteristic in gold makes it a better investment choice than silver, in general.

The commodity market plays a vital role in boosting the economy. Investors make decisions based on market knowledge and ignore cognitive biases. These cognitive biases or judgment errors have a significant effect on investment decisions. Therefore, this study aimed to investigate the effect of emotional intelligence on decision-making. In addition, optimism bias and risk perception are the intervening variables between emotional intelligence and decision-making. So, this study contributes to the body of knowledge by examining the mediating role of optimism bias and risk perception. The data were collected from the respondents of the commodity market and the 370 questionnaires were distributed among the investors, of which 337 respondents gave their feedback. The convenience base sampling technique is used due to the easy access of respondents, time factor, and cost factor. Data entered into the SPSS Statistics Version 26 and PROCESS macro model 6 were used for serial mediation. AMOS was used for the validity and model-fit analysis. The results of this study aligned with the literature that there is a significant effect of emotional intelligence on decision-making. It also observed that optimism bias has a positive effect on decision-making. The finding of this study will be helpful for the brokers, the government, and especially the investors. This study also proposed that future studies on the stock exchange and real estate market comparative analysis can be conducted. [ABSTRACT FROM AUTHOR]

The widespread integration and growing systemic dependence among currency, stock, and commodity markets render these markets often very vulnerable to shocks and at risk of collapse at the same time. As a result, these trends threaten the sustainability of the entire financial system. In this study, we aim to explore the spillovers and nonlinear dependencies between the seven major foreign exchange rates, crude oil and gold prices, a global stock price index, and oil and stock implied volatility indices as proxy variables for global risk factors by employing a directional spillover network approach. We also use a multi-scale decomposition method and nonlinear causality test between these variables to capture multi-level relationships at short and long horizons. The major findings are summarized as follows. First, from the multi-scale decomposition analysis, we identify that Granger causality test results and the direction and strength of return spillovers change with the level of decomposition. Second, the results of nonlinear causality tests show variation in both the significance and direction of Granger causality relationships between the decomposed currency and other series at different timescales, especially for the decomposed oil, gold, and OVX series. Third, the measured directional spillover indices identify the Euro–Dollar exchange rate as the largest contributor of connectedness to the other series. [ABSTRACT FROM AUTHOR]