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The European Union Emissions Trading System (EU ETS) is designed to promote carbon reduction in a cost-effective and economically efficient manner. To meet their compliance requirements, participants within the EU ETS can either invest in carbon abatement, thereby improving their environmental performances, or purchase carbon allowances from the market. Our analysis aims to investigate the structural features of the EU ETS trade network of allowances and assess its robustness to spillovers propagated from energy commodities. We build the EU ETS trade network of allowances on a daily basis by leveraging a granular dataset comprising over 32k accounts that performed approximately 720k trades, involving more than 127 billion of allowances from 2005 to 2020. Importantly, our analysis is able to cover entirely the first three phases of the EU ETS, thus providing an updated view on the functioning of the EU ETS. We find that the EU ETS trade network of allowances is disassortative and characterized by very active nodes belonging to the energy sector. Moreover, we detect that energy commodity transmission channels can be substantial and may shape the way nodes transfer allowances, with the configuration of the EU ETS often being a net receiver of spillovers. These findings are robust even when we analyze extreme quantiles of the distribution to account for distressed periods. Our study shows how the functioning of the EU ETS can be influenced by spillovers imported from energy commodity markets. We discuss how investors can build portfolios that either hedge or amplify the impact of such spillovers, depending on their risk appetite. Given that energy commodities are significant inputs in the production processes of energy firms—who are highly active participants in the trade network of allowances—analyzing market responses to shocks in energy commodities is crucial for the effective implementation of the policy. [ABSTRACT FROM AUTHOR]

Along most of the 2005–2019 period, Uruguay experienced an impressive economic growth. The country extended its public policies across industrial, technological, and social domains. However, there was a dearth of significant structural changes, constraining Uruguay’s ability to embark on a sustained developmental trajectory. Our findings reveal that the majority of implemented policies were designed to enhance competitiveness and build capabilities horizontally, rather than promoting structural changes. This allows us to discuss the dynamic nature of developmental processes marked by increasing policy challenges. In doing so, we stress the relevance of policy support and coordination to sustain inclusive growth processes. [ABSTRACT FROM AUTHOR]

Fossil fuel subsidies represent a significant and widespread fiscal tool that governments can employ to maintain stability in domestic price levels. We develop and estimate a Bayesian dynamic stochastic general equilibrium (DSGE) model tailored for Malaysia, a net-exporting energy economy that captures this key channel of fiscal policy intervention. Fossil fuel price subsidies are often motivated as a means to stabilize vulnerable households, yet the government of Malaysia does not have a targeting policy. Accordingly, we use the model to address how consumption responds to an increase in inflation driven by a fossil fuel price shock. The results indicate that, while a fossil fuel price subsidy stabilizes consumption, we find that a subsidy can "crowd out" non-energy consumption as fiscal intervention increases. Furthermore, we find that while aggregate welfare increases with fiscal stabilization, the highest level of welfare is achieved with targeted subsidies, a contradiction which questions the merit of the current Malaysian energy policy. • Bayesian DSGE model is estimated for the Malaysian economy. • Model incorporates fossil fuel energy in consumption and production. • Fossil fuel price subsidies reduce inflation, but crowds out non-energy consumption. • Fiscal policy complements monetary policy by trading fossil fuel inflation for debt. [ABSTRACT FROM AUTHOR]

Autoencoders are dimension reduction models in the field of machine learning which can be thought of as a neural network counterpart of principal components analysis (PCA). Due to their flexibility and good performance, autoencoders have been recently used for estimating nonlinear factor models in finance. The main weakness of autoencoders is that the results are less explainable than those obtained with the PCA. In this paper, we propose the adoption of the Shapley value to improve the explainability of autoencoders in the context of nonlinear factor models. In particular, we measure the relevance of nonlinear latent factors using a forecast-based Shapley value approach that measures each latent factor's contributions in determining the out-of-sample accuracy in factor-augmented models. Considering the interesting empirical instance of the commodity market, we identify the most relevant latent factors for each commodity based on their out-of-sample forecasting ability. [ABSTRACT FROM AUTHOR]

To address the ongoing contention surrounding the impact of financialization, this study adopts a ripple‐spreading network model to analyze the transmission of information across 13 globally significant commodity markets. By juxtaposing the pre‐ and postfinancialization periods, notable disparities in spillover magnitude are discerned, with overall effects registering at 58% and 85%, respectively. Moreover, the postfinancialization period exhibits accelerated spillover dynamics, necessitating a reduced timeframe (less than 1000 units) in contrast to the prefinancialization period (approximately 2000 units). Furthermore, a heightened interconnectedness among energy, metal, and agricultural futures is evident in the postfinancialization period. These findings furnish compelling evidence regarding the ramifications of financialization on commodity markets. [ABSTRACT FROM AUTHOR]

Cattle farming is a vital sector in the livestock industry, but it is also faced with various complex business risks. This literature review aims to provide an in-depth understanding of the risks associated with cattle farming. Through a thorough analysis of various literature sources, we explore the main aspects that influence risk in the cattle industry, including external factors such as fluctuations in feed prices, changes in government policies, and market uncertainty, as well as internal factors such as livestock health, financial management, and operational efficiency. By thoroughly understanding these risks, cattle farmers can develop effective mitigation strategies to increase the resilience of their business amidst the challenges facing the cattle farming industry. [ABSTRACT FROM AUTHOR]

This study confirms gold's role as a reliable inflation hedge while introducing new insights into lesser-explored assets like art and wheat. Using advanced methodologies such as the ARDL framework and LSTM deep learning, it conducts a detailed analysis of inflation-hedging dynamics, exploring non-linear relationships and unexpected inflation impacts across various asset classes. The findings reveal complex dynamics. Gold demonstrates strong long-term inflation hedging potential. The negative coefficient for the US dollar index suggests that gold acts as a hedge against currency depreciation. Furthermore, a positive relationship between gold returns and inflation during high inflation periods highlights its effectiveness in protecting purchasing power. Art presents a more intricate picture. Long-term analysis suggests a weak mean-reverting tendency, but a negative relationship with inflation, potentially linked to economic downturns. Interestingly, unexpected inflation positively correlates with art returns in the long run, hinting at its potential inflation-hedging abilities. No statistically significant connection between wheat prices and overall inflation was observed; the short-run analysis reveals a dynamic interplay between inflation, real GDP growth, and wheat prices at different time points. [ABSTRACT FROM AUTHOR]

This study analyzes the risk‐adjusted performance of commodity exchange‐traded funds (ETFs) across diverse market conditions. Examining monthly returns from December 2004 to June 2022, our findings suggest that commodity ETFs underperformed US stocks, indicating limited diversification benefits. However, we document positive α during turbulent market periods like the COVID‐19 crisis, signifying potential resilience. Furthermore, our factor regressions reveal that shifts in the global commodity price index and disposable personal income significantly influence commodity ETFs' excess returns, pointing to broader economic and income‐related trends. Commodity ETFs exhibit lower Value‐at‐Risk and Expected Shortfall compared to stock market indices, indicating reduced downside risk exposure for investors. Given the increasing popularity of commodity ETFs, these insights hold substantial significance for market participants. [ABSTRACT FROM AUTHOR]

The present article aims to demonstrate the intersected relations between Indigenous communities and designers in the fashion industry. These interrelations are explained through a case study between Dotnit, an Otomí embroidery cooperative, and the Mexican designer Carla Fernández. An extensive multi-sited ethnography was carried out between 2013 and 2017 in Tenango de Doria, Hidalgo, and Mexico City. Both places were explored with the purpose of understanding the complex relationships between the local and the global through the introduction of tenango embroidery into the fashion world. This research aims to understand the consumption of Indigenous textiles in a glocalized world by following the paths of diversion that tenango embroidery navigates through artisans, designers and consumers. Through the article, interrelationships among different agents will be examined in an effort to understand the complexities within artisan–designer dynamics. [ABSTRACT FROM AUTHOR]

We study the process by which an organisation creates a historical narrative about itself as a strategy to legitimise the role it plays in a particular society. By using the concept of 'public good' as our analytical lens, we show that when the organisation creates a narrative that coincides with that of the nation-state, this poses enormous challenges to the organisation's efforts to control how and by whom this narrative is used. This is because anyone belonging to the nation-state can legitimately make use of that narrative. Therefore, the boundary conditions that permit other actors to use these historical narratives are delimited by those able to define who belongs to the nation-state and who does not. We illustrate our argument with the rhetorical strategies developed by Colombia's Coffee Growers Federation (FNCC) between 1927 and 2013. [ABSTRACT FROM AUTHOR]

Abstract Autoencoders are dimension reduction models in the field of machine learning which can be thought of as a neural network counterpart of principal components analysis (PCA). Due to their flexibility and good performance, autoencoders have been recently used for estimating nonlinear factor models in finance. The main weakness of autoencoders is that the results are less explainable than those obtained with the PCA. In this paper, we propose the adoption of the Shapley value to improve the explainability of autoencoders in the context of nonlinear factor models. In particular, we measure the relevance of nonlinear latent factors using a forecast-based Shapley value approach that measures each latent factor’s contributions in determining the out-of-sample accuracy in factor-augmented models. Considering the interesting empirical instance of the commodity market, we identify the most relevant latent factors for each commodity based on their out-of-sample forecasting ability.

The study investigates the connectedness between commodities and ESG stocks of India using the extended joint connectedness approach. The study found a time-varying relationship between commodities and ESG stocks. It also discovered that there is a low spillover between the two. However, the total connectedness increased during the Russia-Ukraine war but remained low. The study found that crude oil and natural gas act as net transmitters, while ESG stocks and gold act as net receivers. ESG stocks are negatively connected with gold and have a low degree of positive correlation with crude oil and natural gas. Therefore, portfolio diversification opportunities exist between commodities and ESG stocks. The study exhibits that investors may derive significant benefits by adjusting their portfolios based on the optimum weights provided by the portfolio construction technique. The study provides valuable insights for portfolio managers, investors, and policymakers.

Abstract We explore the relationship between the returns of 45 dry bulk shipping company stock prices and the main 15 commodities that bulk carriers transport. Using a principal component analysis to reduce the dimensionality of the commodities dataset and a panel methodology, we find that a change in the commodity price principal component would result in a 0.6% change in the returns of the shipping stock prices. Minerals appear to have a stronger impact, as a 1% change in the minerals principal component results in a 1.1% change in the returns. This is mainly due to the fact that minerals account for larger trade volumes in the dry bulk market and they employ mostly bigger vessels, while the price of Brent oil is also an important factor affecting shipping stock prices.

Since its public introduction in 2009, Bitcoin has grown to be the most well-known cryptocurrency worldwide. There is still debate as to whether Bitcoin may be used as a hedge against other assets. The purpose of this study is to investigate the correlation between Bitcoin and conventional commodity markets such as gold, crude oil, stock markets, and investor interest (quantified via Google Trends). In addition, the paper also tests Bitcoin’s safe haven role compared to other commodity markets. The Vector Autoregression model using daily database collected during the period 2013–2021 is employed to investigate the relationship between Bitcoin and traditional commodity markets. The impulse response function is used to analyze Bitcoin price movements against economic shocks from gold, oil prices, and the Dow Jones Industrial Average. In addition, the value-at-risk (VaR) model is used to test Bitcoin’s safe-haven property compared to other conventional commodity markets. The research results show that Bitcoin has negative impacts on gold, crude oil prices, and the stock market. Besides, Bitcoin responds negatively to a sharp decline in investor interest. Furthermore, the results of the VaR model show that Bitcoin is the second most volatile and risky asset, only after the crude oil market, and much riskier than gold. This result proves that Bitcoin cannot yet be considered a safe-haven instrument. These findings have several implications for investors and policymakers to minimize the risks associated with this cryptocurrency. AcknowledgmentThe authors would like to send their sincere thanks to the Reviewers and Editorial Board of the Journal. Their valuable comments and helpful support helped improve the paper’s quality. No funding was granted for this study.

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We explore the relationship between the returns of 45 dry bulk shipping company stock prices and the main 15 commodities that bulk carriers transport. Using a principal component analysis to reduce the dimensionality of the commodities dataset and a panel methodology, we find that a change in the commodity price principal component would result in a 0.6% change in the returns of the shipping stock prices. Minerals appear to have a stronger impact, as a 1% change in the minerals principal component results in a 1.1% change in the returns. This is mainly due to the fact that minerals account for larger trade volumes in the dry bulk market and they employ mostly bigger vessels, while the price of Brent oil is also an important factor affecting shipping stock prices. [ABSTRACT FROM AUTHOR]

AbstractFaced with a more multipolar world, scholars of International Political Economy are sharpening their tools to make sense of the longue durée of post-colonial institutions, international financial subordination and the quest for self-determination. This article develops the notion of ‘earnest struggles’ in Senegal’s postcolonial history and shows that successive governments have indeed tried to move their country forward against the odds. The focus is on three struggles: First, the attempts at transforming the Senegalese economy away from colonial cash crops and the influence of the French from 1960 to 1980. Second, the struggle of grappling with Global South debt crisis and the devaluation of the Franc CFA by 50% between 1980 to 2004. Third, the struggle to expand the Senegalese economy with newfound fiscal space and novel forms of external debt since international debt relief in 2004 until today. Based on financial data and interviews in Dakar and Paris, I argue that these struggles have led to some structural transformation. However, the danger of debt crisis has not gone, and economic self-determination has remained precarious. Dependence on foreign finance has stayed and reached record levels in recent years. Relative delinking and the search for regional complementarities offers a more promising avenue to break out of the structural condition of international financial subordination. [ABSTRACT FROM AUTHOR]

We present a new term-structure model for commodity futures prices based on Trolle and Schwartz (2009), which we extend by incorporating seasonal stochastic volatility represented with two different sinusoidal expressions. We obtain a quasi-analytical representation of the characteristic function of the futures log-prices and closed-form expressions for standard European options' prices using the fast Fourier transform algorithm. We price plain vanilla options on the Henry Hub natural gas futures contracts, using our model and extant models. We obtain higher accuracy levels with our model than with the extant models. [ABSTRACT FROM AUTHOR]

The seasonal risk of wheat, corn, and soybean is modeled by a novel seasonality filter based on a generalized ridge regression. Then, using a component GARCH model, seasonal risk is combined with event risk and a short‐term risk dynamics. The resulting model is robust, generates seasonal patterns related to the crop cycle, and significantly outperforms the standard GARCH(1,1) in terms of out‐of‐sample risk prediction. Results are relevant for risk management and portfolio construction. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to examine the dynamic conditional correlation (DCC) and hedging ratios between Dow Jones markets and oil, gold and bitcoin. Using daily data, including the COVID-19 pandemic and the Russia–Ukraine war. We employ the DCC-generalized autoregressive conditional heteroskedasticity (GARCH) and asymmetric DCC (ADCC)-GARCH models. Design/methodology/approach: DCC-GARCH and ADCC-GARCH models. Findings: The most of DCCs among market pairs are positive during COVID-19 period, implying the existence of volatility spillovers (Contagion-effects). This implies the lack of additional economic gains of diversification. So, COVID-19 represents a systematic risk that resists diversification. However, during the Russia–Ukraine war the DCCs are negative for most pairs that include Oil and Gold, implying investors may benefit from portfolio-diversification. Our hedging analysis carries significant implications for investors seeking higher returns while hedging their Dow Jones portfolios: keeping their portfolios unhedged is better than hedging them. This is because Islamic stocks have the ability to mitigate risks. Originality/value: Our paper may make a valuable contribution to the existing literature by examining the hedging of financial assets, including both conventional and Islamic assets, during periods of stability and crisis, such as the COVID-19 pandemic and the Russia–Ukraine war. [ABSTRACT FROM AUTHOR]

This article studies exchange-traded funds' (ETFs) price impact in the most ETF-dominated asset classes: volatility (VIX) and commodities. I propose a new way to measure ETF-related price distortions based on the specifics of futures contracts. This allows me to isolate a component in VIX futures prices that is strongly related to the rebalancing of ETFs. I derive a novel decomposition of ETF trading demand into leverage rebalancing, calendar rebalancing, and flow rebalancing, and show that trading against ETFs is risky. Leverage rebalancing has the largest effects on the ETF-related price component. This rebalancing amplifies price changes and exposes ETF counterparties to variance. [ABSTRACT FROM AUTHOR]

Fredric Jameson's 1984 essay 'Postmodernism, or the Cultural Logic of Late Capitalism' famously calls for new forms of representation that can provide a better notion of the sublime world space of multinational capital. Jameson states that such aesthetic scale models are yet unrealised, but this essay argues that a number of contemporary novels, including Emily St. John Mandel's Station Eleven, Ben Lerner's 10:04 and works by Matias Faldbakken, Sally Rooney and William Gibson, present us with global figurations that are both more banal and more sublime than Jameson could have imagined. These novels all contain scenes where ordinary commodities are turned inside out in staggering leaps of scale, which constitute original figurations of the global infrastructures of late capitalism. Drawing on Bill Brown's thing theory, Jennifer Wenzel's notion of commodity biographies, and different theories of scale in the Anthropocene, I analyse different examples of this figure, which I term transparent commodities. In the concluding section, I show how these figurations prefigure the current global supply chain crisis, and I return to Jameson's original demand for representations of the global totality, which I discuss in dialogue with my analyses as well as theories of planetarity by Gayatri Spivak and Dipesh Chakrabarty. [ABSTRACT FROM AUTHOR]

In the early 2010s, a spectacular fall in prices for a class of mineral commodities called the rare earth elements (REEs) and the collapse of hundreds of new exploration companies made clear the fragility of the high-risk markets around these companies and the strategies of legitimation that supported them. New regulatory processes built around technical disclosures generated vast stores of geotechnical data. Rather than generating trust among market actors, however, these processes dramatically altered the temporalities of global extraction and energized unruly narrative spaces. In their efforts to keep mineral claims active and companies afloat, REE-focused exploration experts have struggled to navigate different arenas of discussion while holding their respective logics in tension. Drawing on ethnographic fieldwork with exploration geologists and promoters, this article examines how experts federate flows of 'serious' and 'speculative' information in both carefully regulated reports and rumor-filled online forums. Such spaces are organized by aesthetic conventions and social criteria for establishing persuasiveness—forms that STS scholars have long analyzed as literary technologies. Rather than helping to secure experts' authority, however, I argue that the diverse literary technologies that now dominate exploration promotion and finance work have radically redistributed interpretive roles. In their struggles to mediate senses of 'crisis' endemic within venture markets, exploration experts must enact the ideals undergirding new regulatory requirements even as they learn to defer to the speculative musings of others. [ABSTRACT FROM AUTHOR]

We examine the hedge and safe‐haven properties of four commodity classes (precious metals, energy, agriculture and livestock) for the overall and sectoral equity markets of the US and China. In doing so, we employ two quantiles‐based approaches, quantile regression and cross‐quantilogram, using daily data from 25 September 2014 to 06 July 2020. The hedging effectiveness (HE) and time‐varying conditional diversification benefits (CDB) are estimated. Our findings indicate that precious metals and agricultural commodities are weak safe havens for all equity sectors of China and the United States. In contrast, energy and livestock commodities are weak safe havens only for the information technology and healthcare sectors. Precious metals show better HE, whereas all commodities offer strong CDB. Our findings may be helpful for sectoral investors offsetting equity losses by investing in various commodity classes. [ABSTRACT FROM AUTHOR]

We analyse time‐varying risk spillover and dependence to assess the systemic risk benefits of ethical, sustainable, and green investments. Our data comprise sustainable investments from ethical, environmental, social and governance (ESG), and green bonds. We investigate the link to major asset classes, including equity, commodity, and currency markets. We find evidence of close connection between the major asset classes and sustainable assets, except green bonds. We also explore the improvement in hedging efficiency from combining ethical and ESG investments with commodities and currencies over investment horizons. Our analysis based on systemic risk measures indicates that there is evidence of lower time‐scale systemic risk connectedness in the case of commodities and currencies combined with ethical and ESG assets. These findings have significant implications for portfolio managers, policymakers, and market participants. [ABSTRACT FROM AUTHOR]

In the context of the COVID‐19's outbreak and its implications for the financial sector, this study analyses the aspect of hedging and safe‐haven under the pandemic. Drawing on the daily data from 02 August 2019 to 17 April 2020, our key findings suggest that the contagious effects in financial assets' returns significantly increased under COVID‐19, indicating exacerbated market risk. The connectedness spiked in the middle of March, consistent with lockdown timings in major economies. The effect became severe with the WHO's declaration of a pandemic, confirming negative news effects. The return connectedness suggests that COVID‐19 has been a catalyst of contagious effects on the financial markets. The crude oil and the government bonds are however not as much affected by the spillovers as their endogenous innovation. In terms of spillovers, we do find the safe‐haven function of Gold and Bitcoin. Comparatively, the safe‐haven effectiveness of Bitcoin is unstable over the pandemic. Whereas, GOLD is the most promising hedge and safe‐haven asset, as it remains robust during the current crisis of COVID‐19 and thus exhibits superiority over Bitcoin and Tether. Our findings are useful for investors, portfolio managers and policymakers interested in spillovers and safe havens during the current pandemic. [ABSTRACT FROM AUTHOR]

Commodities can be treated as an alternative investment, a hedging strategy, or a diversification opportunity. Various types of commodities, especially metals, usually are not strongly affected by inflation, and the trends of their prices are not correlated with other investment instruments. To participate in the metals area of the commodity market, an investor can buy shares of companies from the basic materials sector. Such companies are involved in discovering, developing, and processing raw materials. However, as not all the companies from the basic materials sector deal with precious metals, it is worth adding gold futures to such a portfolio. The aim of this paper is to compare a portfolio of the basic materials sector stocks against a similar portfolio hedged with gold. Our findings revealed that hedging a commodities' portfolio with gold minimizes both profits and losses and can be suitable for risk-averse investors. The research results can be applied by individual investors and investment managers to choose the most appropriate investment approach. [ABSTRACT FROM AUTHOR]

Purpose: In this paper, the authors investigate the impact of the confirmation bias on returns, expectations and hedging of optimistic and pessimistic traders in the cryptocurrencies, commodities and stock markets before and during COVID-19 periods. Design/methodology/approach: The authors investigate the impact of the confirmation bias on the estimated returns and the expectations of optimistic and pessimistic traders by employing the financial stochastic model with confirmation bias. Indeed, the authors compute the optimal portfolio weights, the optimal hedge ratios and the hedging effectiveness. Findings: The authors find that without confirmation bias, during the two sub periods, the expectations of optimistic and pessimistic trader's seem to convergence toward zero. However, when confirmation bias is particularly strong, the average distance between these two expectations are farer. The authors further show that, with and without confirmation bias, the optimal weights (the optimal hedge ratios) are found to be lower (higher) for all pairs of financial market during the COVID-19 period as compared to the pre-COVID-19 period. The authors also document that the stronger the confirmation bias is, the lower the optimal weight and the higher the optimal hedge ratio. Moreover, results reveal that the values of the optimal hedge ratio for optimistic and pessimistic traders affected or not by the confirmation bias are higher during the COVID-19 period compared to the estimates for the pre-COVID period and inversely for the optimal hedge ratios and the hedging effectiveness index. Indeed, either for optimists or pessimists, the presence of confirmation bias leads to higher optimal hedge ratio, higher optimal weights and higher hedging effectiveness index. Practical implications: The findings of the study provided additional evidence for investors, portfolio managers and financial analysts to exploit confirmation bias to make an optimal portfolio allocation especially during COVID-19 and non-COVID-19 periods. Moreover, the findings of this study might be useful for investors as they help them to make successful investment decision in potential hedging strategies. Originality/value: First, this is the first scientific work that conducts a stochastic analysis about the impact of emotional biases on the estimated returns and the expectations of optimists and pessimists in cryptocurrency and commodity markets. Second, the originality of this study stems from the fact that the authors make a comparative analysis of hedging behavior across different markets and different periods with and without the impact of confirmation bias. Third, this paper pays attention to the impact of confirmation bias on the expectations and hedging behavior in cryptocurrencies and commodities markets in extremely stressful periods such as the recent COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

Global pandemics are known to disturb livelihoods. The coronavirus disease 2019 (COVID-19) is an example of such pandemic in the recent past. Its outbreak prompted a global response characterised by unprecedented measures to mitigate its spread. Several preventative measures were recommended by the World Health Organization (WHO) such as lockdowns to curtail the transmission of the virus and manage the crisis it caused. These measures hampered the movement and distribution of basic commodities inadvertently triggering a series of socio-economic consequences particularly in rural areas. This study delves into the intricate interplay between the COVID-19 lockdown and its impact on the accessibility, affordability and availability of basic commodities within the context of the Mahikeng Local Municipality, in a rural setting. Quantitative survey data were collected from 260 households, which were randomly selected. The data analysis was performed using chi-square, with a significance level of p 0.05. The results showed that basic commodities were largely available (99%) in the market during lockdown. There was no significant association between background variables and availability of basic commodities. Accessibility of basic commodities was affected by many factors such as concerns of COVID-19 outbreak. The study demonstrated that prices of basic commodities increased during lockdown, thereby having a ripple effect on accessibility and affordability of basic commodities. However, the availability of basic commodities was less affected. Contribution: The case study approach, focusing on Mahikeng Local Municipality, is essential for capturing localised nuances and providing actionable insights to policymakers, researchers and community leaders seeking to mitigate the negative effects of lockdowns on rural populations.

Commodity futures constitute an attractive asset class for portfolio managers. Propelled by their low correlation with other assets, commodities begin gaining popularity among investors, as they allow to capture diversification benefits. This comprehensive study examines the time and frequency spillovers between the Economic Policy Uncertainty [1] and a broad set of commodities encompassing ferrous, non-ferrous, and precious metals, food, and energy commodities over a period from December 1997 to April 2022, which includes various political, economic and health crises.The novelty of this research lies in its extensive temporal and categorical coverage, providing an understanding of how different types of commodities respond to various crises. Furthermore, our study breaks new ground by employing wavelet analysis to gain detailed insights in both time and frequency domains in the financial time series of interest, providing a deeper understanding of the co-movements and lead-lag relationships. Specifically, we introduce the Cross Wavelet Transform (XWT) and Wavelet Coherence (WTC) analysis.Our findings demonstrate that not all crises uniformly impact commodities. Notably, during the global financial crisis and the COVID-19 pandemic, co-movements between commodities became significantly stronger. These results highlight the heterogeneity within the commodity asset class, where individual commodities exhibit diverse underlying dynamics. Importantly, the proposed methodology facilitates the extraction of robust results even when dealing with nonlinearities and nonstationary time series data. Consequently, our work offers valuable insights for policymakers (including regulatory bodies), investors, and fund managers.

El objetivo principal de este estudio es evaluar la rentabilidad y los riesgos asociados al portafolio del nexo agua, energía y alimentos (AEA) en el escenario pospandemia. Para ello, se empleó una metodología cuantitativa y trans- versal, basada en el análisis de una variedad de commodities que representan estos sectores, tales como bonos de agua de California; petróleo crudo Brent, gas natural, maíz, soja y café. El análisis se realizó mediante un algoritmo desarrollado en Python, enfocado en evaluar la rentabilidad, el riesgo del portafolio y la Frontera de Eficiencia de Markowitz. Los resultados mostraron que, aunque existen oportunidades de rentabilidad en el portafolio del nexo AEA, también se presentan riesgos significativos para la obtención de dicha rentabilidad. Un hallazgo clave del es- tudio es que, a pesar de la creciente demanda de recursos eficientes y sostenibles, la inversión en estos sectores requiere una gestión estratégica y considerada. Se sugiere la necesidad de una cuidadosa selección y diversifi- cación del portafolio, alineando las inversiones con principios de sostenibilidad para garantizar su viabilidad a largo plazo. El estudio concluye destacando la importancia de desarrollar modelos predictivos más robustos y aboga por investigaciones futuras para un análisis más integral de las dinámicas entre agua, energía y alimentos y su impacto en los mercados financieros.

Gold prices have been of major interest for a lot of investors, analysts, and economists. Accordingly, a number of different modeling approaches have been used to forecast gold prices. In this manuscript, the geometric Brownian motion approach, used in the pricing of numerous types of assets, is used to forecast the prices of gold at yearly, monthly, and quarterly frequencies. This approach allows for simulating one-period-ahead prices and the associated probabilities. The expected prices obtained from the simulated prices and probabilities are found to provide reliable forecasts when compared with the observed yearly, monthly, and quarterly prices.

Neste trabalho foram analisadas as mudanças no uso e cobertura da terra em uma bacia hidrográfica que se encontra em zona de expansão da soja, na transição entre o planalto e a escarpa de erosão, na região central do estado de Mato Grosso do Sul. A análise foi conduzida utilizando a série histórica da Rede MapBiomas, coleção 7.1 (1985-2021). Nos anos de 2021, marcado por uma situação de seca, e 2023, com distribuição de chuvas dentro da média histórica, foram calculados índices radiométricos de vegetação a partir de imagens Sentinel-2. Esse refinamento permite observar as mudanças interanuais na cobertura e uso da terra e os efeitos da seca sobre a vegetação. Em 1985 a vegetação nativa composta por formações florestais, savânicas e campo alagado, ocupava 29,16% da área da Bacia Hidrográfica do Córrego Limpo (BHCL). Em 2021, a vegetação nativa aumentou para 30,02%, principalmente devido à regeneração natural em áreas de pastagens abandonadas em terrenos declivosos. Durante o período analisado, a classe de uso predominante foi pastagem, ocupando 50,04% da área em 1985 e 37,41% em 2021. A redução na área de pastagem é consequência do avanço da cultura da soja, que em 2021 correspondia a 11,81% da área total da BHCL. Apesar da área estar inserida em uma Unidade de Conservação de uso sustentável, os processos de degradação observados são os mesmos que os de áreas não protegidas.

AbstractIn this study, we investigate the dynamic relationship between return and liquidity in the Brent and the West Texas Intermediate (WTI) oil markets. The research utilises daily oil price and volume data and monthly macroeconomic data from January 1, 1996 to April 28, 2023 obtained from the Energy Information Association (EIA), the Organisation for Economic Co-operation and Development (OECD), the Federal Reserve Economic Data (FRED), investing.com, and the International Monetary Fund (IMF). We employ the ARMAX(1,1)-aDCC-GARCH-t(1,1) model to capture time-varying associations between return and liquidity. Our findings reveal a significant impact of speculation on the return-liquidity relationship, which is more persistent in the WTI market. Furthermore, we observe a pattern between the Brent and WTI markets during the study period, which the heterogeneous trader hypothesis can explain. These insights hold implications for policymakers aiming to enhance the crude oil market’s stability, as well as for market traders in developing trading and risk management strategies.

Walter Benjamin's criticisms of dwelling and the modern interior are well known. The middle classes retreated from the alienation of nineteenth-century city life into domestic seclusion, surrounding themselves with soft furnishings in a search for comfort. In Living Rooms (2022), Sam Johnson-Schlee uses objects typically found in the living room or lounge as his starting point for the development of Benjamin's ideas. Recognising the labour invested in domestic commodities would allow us to realise our dreams of connecting with others and escaping the confines of capitalism like roots breaking out of a plant pot. The personal observations used to support the arguments reflect the experience of Generation Rent. They inform an attempt to relate IKEA's 1996 'Chuck out that chintz!' advert to crucial changes in the British housing market. The emphasis on labour largely sidelines household labour and its gendering, with the exception of the activities of 'cleanfluencer' Mrs. Hinch. The link between domestic commodities and our longing for intimacy is effectively depicted using a combination of historical research, cultural references and family stories. Plants and flowers are used throughout to illustrate specific arguments and to serve as a symbol of a different, and better, life. [ABSTRACT FROM AUTHOR]

This paper studies the volatility dynamics of futures contracts on crude oil, natural gas, and gasoline. An appropriate Bayesian model comparison exercise between seven stochastic volatility (SV) models is estimated using daily prices for our futures contracts between 2005 and 2023. Moreover, to assess the impacts of COVID‐19 and the Russia–Ukraine conflict on volatility, we analyze these two subsamples. Overall, we find that: (i) the Bayes factor shows that the SV model with t $t$‐distributed innovations outperforms the competing models; (ii) crude oil contracts with different expiry dates may require the introduction of leverage effects; (iii) the t $t$‐distributed innovations remain the appropriate model for the COVID‐19 subsample, while jumps are needed in the conflict period; and (iv) other Bayesian criteria more appropriate to short‐term predictive ability—such as the conditional and the observed‐date deviance information criterion—suggest other rank order to model our futures contracts, despite the agreements for the best models. [ABSTRACT FROM AUTHOR]

We assessed volatility transmissions that occurred (inter and intra) commodities of metals and energy from October 16, 1998 to October 17, 2018. With a total of 5220 price observations for each commodity, we estimate spillover indexes. We measure such transmissions among twelve commodities and computed the parcels of shock's effects in each pairwise of assets. We also compute how much of these volatilities transmissions was absorbed by each asset in these markets. And using complex network statistics metrics, we describe statistically and graphically these transmissions. Our results pointed that total connectivity is 53% and, through the network analysis, we realize that the greatest interactions occur involving oil and nickel and gold and silver. We confirm the findings of Arouri, et al. (2013) that most precious metals can be a good hedge option in stock portfolios and other assets, especially when there are crises or increased uncertainties in international financial markets. Indeed, we also attested to the conclusion of Papenfuß et al. (2021) that the value of metals was what, among the factors analyzed by them, had a relevant negative effect on the pricing and forecasting. We believe that these evidence are useful for the literatures of financial networks and metallic/energetic commodity markets; as well for investors, risk managers, fund managers, policy makers and metals and energy's producers and buyers. [ABSTRACT FROM AUTHOR]

We propose a reinforcement learning (RL) algorithm for generating a trading strategy in a realistic setting, that includes transaction costs and factors driving the asset dynamics. We benchmark our algorithm against the analytical optimal solution, available when factors are linear and transaction costs are quadratic, showing that RL is able to mimic the optimal strategy. Then we consider a more realistic setting, including non‐linear dynamics, that better describes the WTI spot prices time series. For these more general dynamics, an optimal strategy is not known and RL becomes a viable alternative. We show that on synthetic data generated from WTI spot prices, the RL agent outperforms a trader that linearizes the model to apply the theoretical optimal strategy. [ABSTRACT FROM AUTHOR]

Summary: With the availability of social networks, specialized forums, and online news, sentiment analysis has become a common and useful technique for the analysis of economic and financial scenarios. Several data‐providers have also started computing proprietary sentiment indexes on financial assets to be delivered together with market price and trading volume. We develop a modified version of the mean‐reverting 4/2 stochastic volatility model introduced in Escobar‐Anel & Gong (2020) to describe the dynamics of commodities. In our specification, jumps are allowed in the asset price dynamics, and the drift coefficient may also switch between regimes related to a sentiment indicator. In this framework, we discuss the distributional characteristics of asset returns, provide a numerical procedure for model estimation, and give some preliminary results on the pricing of European‐style derivatives. Finally, the model is fitted to the market data for Gold and Crude Oil. [ABSTRACT FROM AUTHOR]

We develop two improvements over our previously proposed spectral subtraction with voice activity detection and minimum mean square error spectrum power estimator based on zero crossing (SS-VAD + MMSE-SPZC) enhancement for a real-time spoken query system (SQS). Firstly, we introduce a time delay neural network (TDNN) based modeling technique. Secondly, to properly train the models, we increase the size of the database by collecting the Kannada speech data from an additional 500 farmers under real-time conditions. The proposed combined enhancement technique effectively removes background noise and improves speech quality. When evaluated on the updated degraded speech corpus, our proposed automatic speech recognition (ASR) system achieves better performance compared to previous framework. Moreover, experimental results demonstrate an improvement of 1.32% and 1.48% in terms of speech recognition accuracy for noisy and enhanced speech data respectively, compared to our earlier work. [ABSTRACT FROM AUTHOR]

This paper analyzes the connectedness between gold, wheat, and crude oil futures, Bitcoin, carbon emission futures, and international stock markets in the G7, BRICS, and Gulf regions with the outbreak of exogenous and unexpected shocks related to health, banking, and political crises. To this end, we use a wavelet-based method on the returns of different assets during the period 2 January 2019, to 21 April 2023. The empirical findings show that the existence of time-varying linkages between markets is well documented and appears stronger during the COVID-19 pandemic. However, it seems to diminish for some associations with the advent of the Russia-Ukraine War. The empirical results also show that investor risk perceptions measured by the VIX are negatively and substantially linked to stock markets in different regions. Other interesting findings emerge from the connectedness analysis with the outbreak of Silicon Valley bankruptcy. In particular, Bitcoin tends to regain its role as a safe-haven asset against some G7 stock markets during the bank crisis. Such findings can provide valuable insights for investors and policymakers concerning the relationship between different markets during different crises. [ABSTRACT FROM AUTHOR]

Gold prices have been of major interest for a lot of investors, analysts, and economists. Accordingly, a number of different modeling approaches have been used to forecast gold prices. In this manuscript, the geometric Brownian motion approach, used in the pricing of numerous types of assets, is used to forecast the prices of gold at yearly, monthly, and quarterly frequencies. This approach allows for simulating one-period-ahead prices and the associated probabilities. The expected prices obtained from the simulated prices and probabilities are found to provide reliable forecasts when compared with the observed yearly, monthly, and quarterly prices. [ABSTRACT FROM AUTHOR]

Using daily data since 2017, we disentangle China-specific structural shocks driving Chinese financial markets and examine spillovers across global markets. The novelty of this paper consists of simultaneously identifying China shocks with shocks emanating from the United States and shocks to global risk sentiment - two major forces driving global financial markets - to ensure that China spillover estimates do not reflect common factors. Our results show that shocks originating in China have material impacts on global equity markets, although spillovers are much smaller than those following shocks in the United States, or those triggered by shifts in global risk sentiment. By contrast, shocks from China account for a significant proportion of variation in global commodity prices, more on a par with those of the United States. Nevertheless, spillovers from China can be significantly amplified in an environment of heightened global volatility, or when the shocks are large. [ABSTRACT FROM AUTHOR]

In this study, we develop a framework, based on a global vector autoregression (GVAR) model, to unite two perspectives on commodity markets, the commodity-specific, single-market-centered approach, investigating the micro- and macroeconomic drivers of commodity prices, and the market perspective, which observes joint movements of commodity prices on exchanges. Thereby, the GVAR model disentangles single market from inter-market effects, while simultaneously accounting for the impact of macroeconomic factors. We apply the framework to the six industrial metals markets, reflecting their interdependencies via their co-production, co-consumption, or co-trading relation. In particular, the numerous significant spillover effects in the cross-commodity dimension underline the importance of jointly modeling commodity markets. While the strong co-movement between industrial metal prices is represented exceptionally well by our framework, the microeconomic supply and demand attributes of the commodities have significant impact, within and across markets, even on price variables, highlighting their relevance in modern commodity market models. Moreover, we detect global shocks, e.g., an increase in global demand, affect each commodity market to a similar extent. [ABSTRACT FROM AUTHOR]