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Despite an attractive interest rate differential between China and foreign countries, existing capital control might prevent currency carry trade strategies to be executed. We focus on the copper market to study if trades are taken in order to execute carry trade strategies. We find that copper value is related to carry trade through the onshore-offshore interest differential, while the pegged nature of the USD/CNY exchange rate makes traders indifferent to the forward risk premium. We rule out the possibility of high average payoff due to peso problems, because risk factors are insignificant, implying that carry traders are either fully hedged on FX risks, or they are unconcerned about FX risks. [ABSTRACT FROM AUTHOR]

In this study, we investigate the heterogeneity in energy and non-energy commodities by analyzing their four realized moments: returns, realized volatility, realized skewness and realized kurtosis. Utilizing monthly data, we examine two commodity categories over various crisis periods. This study examines a comparative approach to descriptive statistics across different crisis periods and the full sample and assesses the out-of-sample significance of heteroscedasticity using GARCH models. The findings reveal significant heterogeneity in both energy and non-energy commodities, with energy commodities exhibiting higher average returns and volatility. Crisis periods markedly influence the statistical properties of these commodities. GARCH models outperform ARMA models in forecasting realized moments, particularly for non-energy commodities. This research contributes to the literature by providing robust evidence of heterogeneity in commodity markets and highlights the importance of considering all four realized moments in such analyses. [ABSTRACT FROM AUTHOR]

This study evaluated the forecasting accuracy of various models over 5-day and 10-day trading horizons to predict the prices of orange juice futures (OJ = F). The analysis included traditional models like Autoregressive Integrated Moving Average (ARIMA) and advanced neural network models such as Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), Backpropagation Neural Network (BPNN), Support Vector Regression (SVR), and Convolutional Long Short-Term Memory (ConvLSTM), incorporating factors like the Commodities Index and the S&P500 Index. We employed loss function metrics and various tests to assess model performance. The results indicated that for the 5-day horizon, the LSTM and ConvLSTM consistently outperformed the other models. LSTM achieved the lowest error rates and demonstrated superior capability in capturing temporal dependencies, especially in single-factor and S&P500 Index predictions. ConvLSTM also performed strongly, effectively modeling spatial and temporal data patterns. In the 10-day horizon, similar trends were observed. LSTM and ConvLSTM models had significantly lower errors and better alignment with actual values. The BPNN model performed well when all factors were included, and the SVR model maintained consistent accuracy, particularly for single-factor predictions. The Diebold–Mariano (DM) test indicated significant differences in forecasting accuracy, favoring advanced neural network models. In addition, incorporating multiple influencing factors further improved predictive performance, enhancing investment outcomes and reducing risk. [ABSTRACT FROM AUTHOR]

We examine the impact of physical climate risks on energy markets in China, distinguishing between traditional energy and new energy stock markets, and the energy commodity market, utilizing a time-varying parameter vector autoregressive model with stochastic volatility (TVP-SV-VAR). Specifically, we investigate the dynamic effects of five specific subtypes of physical climate risks, namely waterlogging by rain, drought, typhoon, cryogenic freezing, and high temperature, on WTI oil prices and coal prices. The findings reveal that these physical climate risks exhibit time-varying similar effects on the returns of traditional energy and new energy stocks, but heterogeneous effects on the returns of WTI oil prices and coal prices. Finally, we categorize and examine the impact of both acute and chronic physical risks on the energy commodity market. [ABSTRACT FROM AUTHOR]

Estimating origin-destination (OD) demand is integral to urban, regional, and national freight transportation planning and modeling systems. However, in developing countries, existing studies reveal significant inconsistencies between OD estimates for domestic and import/export commodities derived from interregional input-output (IO) tables and those from regional IO tables. These discrepancies create a significant challenge for properly forecasting the freight demand of regional/interregional multimodal transportation networks. To this end, this study proposes a novel integrated framework for estimating regional and international (import/export) OD freight flows for a set of key commodities that dominate long-distance transportation. The framework leverages multisource data and follows a three-step process. First, a spatial economic model, PECAS activity allocation, is developed to estimate freight OD demand within a specific region. Second, the international (import and export) freight OD is estimated from different zones to foreign countries, including major import and export nodes such as international seaports, using a gravity model with the zone-pair friction obtained from a multimodal transportation model. Third, the OD matrices are converted from monetary value to tonnage and assigned to the multimodal transportation super network using the incremental freight assignment method. The model is calibrated using traffic counts of the highways, railways, and port throughput data. The proposed framework is tested through a case study of the Province of Jiangxi, which is crucial for forecasting freight demand before the planning, design, and operation of the Ganyue Canal. The predictive analytics of the proposed framework demonstrated high validity, where the goodness-of-fit (R2) between the observed and estimated freight flows on specific links for each of the three transport modes was higher than 0.9. This indirectly confirms the efficacy of the model in predicting freight OD demands. The proposed framework is adaptable to other regions and aids practitioners in providing a comprehensive tool for informed decision-making in freight demand modeling. [ABSTRACT FROM AUTHOR]

When considering agricultural commodity transaction data, long sampling intervals or data sparsity may lead to small samples. Furthermore, training on small samples can lead to overfitting and makes it hard to capture the fine-grained fluctuations in the data. In this study, a multi-scale forecasting approach combined with a Generative Adversarial Network (GAN) and Temporal Convolutional Network (TCN) is proposed to address the problems related to small sample prediction. First, a Time-series Generative Adversarial Network (TimeGAN) is used to expand the multi-dimensional data and t-SNE is utilized to evaluate the similarity between the original and synthetic data. Second, a greedy algorithm is exploited to calculate the information gain, in order to obtain important features, based on XGBoost. Meanwhile, TCN residual blocks and dilated convolutions are used to tackle the issue of gradient disappearance. Finally, an attention mechanism is added to the TCN, which is beneficial in terms of improving the forecasting accuracy. Experiments are conducted on three products, garlic, ginger and chili. Taking garlic as an example, the RMSE of the proposed method was reduced by 1.7% and 1% when compared to the SVR and RF models, respectively. Its R 2 accuracy was also improved (by 4.3% and 3.4%, respectively). Furthermore, TCN-attention and TCN were found to require less time compared to GRU and LSTM. The accuracy of the proposed method increased by about 5% when compared to that without TimeGAN in the ablation study. Moreover, compared with TCN, the Gated Recurrent Unit (GRU), and the Long Short-term Memory (LSTM) model in the multi-scale price forecasting task, the proposed method can better utilize small samples and high-dimensional data, leading to improved performance. Additionally, the proposed model is compared to the Transformer and TimesNet models in terms of its accuracy, deployment cost, and other metrics. [ABSTRACT FROM AUTHOR]

Abstract 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 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 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]

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]

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]

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]

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.

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.

Copyright of GeSec: Revista de Gestao e Secretariado is the property of Sindicato das Secretarias e Secretarios do Estado de Sao Paulo (SINSESP) 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.)

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]

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]

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]

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]

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.

In this study, we investigate the heterogeneity in energy and non-energy commodities by analyzing their four realized moments: returns, realized volatility, realized skewness and realized kurtosis. Utilizing monthly data, we examine two commodity categories over various crisis periods. This study examines a comparative approach to descriptive statistics across different crisis periods and the full sample and assesses the out-of-sample significance of heteroscedasticity using GARCH models. The findings reveal significant heterogeneity in both energy and non-energy commodities, with energy commodities exhibiting higher average returns and volatility. Crisis periods markedly influence the statistical properties of these commodities. GARCH models outperform ARMA models in forecasting realized moments, particularly for non-energy commodities. This research contributes to the literature by providing robust evidence of heterogeneity in commodity markets and highlights the importance of considering all four realized moments in such analyses.

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]

Copyright of Revista Musical Chilena is the property of Universidad de Chile 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 Russia–Ukrainian war, which began in 2014 and exploded with the invasion of the Russian army on 24 February 2022, has profoundly destabilized the political, economic and financial balance of Europe and beyond. To the humanitarian emergency associated with every war has been added the deep crisis generated by the strong energy and food dependence that many European countries, and not only European, have developed over decades on Ukraine (especially for wheat) and Russia (especially for natural gas). The aim of this article is to verify the existence of a link between the performance of the Eurostoxx index and the price of wheat futures and TTF natural gas, from 25 February 2019 to 28 September 2023. Through a quantile VAR analysis, a link is sought between the Eurostoxx 50 index, and wheat and TTF gas futures prices. Furthermore, the analysis intends to understand whether the presence of such relationship only manifested itself following the war events, or whether it was already present in the market. The analysis carried out also shows that the relationship between the stock market and raw material prices was present even before the conflict. [ABSTRACT FROM AUTHOR]

This paper examines the diversification benefits of commodity indices during the COVID-19 pandemic by analyzing both static and dynamic risk spillovers for the period from January 2, 1998 to September 16, 2020. Using variance decomposition forecasting, we employed static and dynamic analyses based on the estimation of 50-day moving window spillover indices. Globally, the results show significant spillovers between markets during the COVID-19 pandemic crisis. The results show that stock markets are highly interdependent with other financial markets (in both directions), and that commodity markets (except energy) and the bond market are recipients of shocks emanating from stock markets. The main contribution of this paper is to study the return and volatility spillovers between stock and commodity indices before and during the pandemic. This study of shock transmission mechanisms will enable investors to develop optimal diversification and hedging strategies during the crisis. In this context, we found that commodities and US government bonds could offer diversification benefits to investors. In addition, some of these assets may serve as hedging instruments or safe havens during the COVID-19 crisis.

Abstract Since the onset of the COVID-19 pandemic, financial and commodity markets have exhibited significant volatility and displayed fat tail properties, deviating from the normal probability curve. The recent Russia-Ukraine war has further disrupted these markets, attracting considerable attention from both researchers and practitioners due to the occurrence of consecutive black swan events within a short timeframe. In this study, we utilized the Quantile-VAR technique to examine the interconnectedness and spillover effects between African equity markets and international financial/commodity assets. Daily data spanning from January 3, 2020, to September 6, 2022, was analyzed to capture tail risks. Our main findings can be summarized as follows. Firstly, the level of connectedness in returns is more pronounced in the lower and upper tails compared to the median. Secondly, during times of crisis, African equity markets primarily serve as recipients of systemic shocks. Lastly, assets such as Silver, Gold, and Natural Gas exhibit greater resilience to systemic shocks, validating their suitability as hedging instruments for African equities, in contrast to cryptocurrencies and international exchange rates. These findings carry significant implications for policymakers and investors in Africa equities.

Estimating origin-destination (OD) demand is integral to urban, regional, and national freight transportation planning and modeling systems. However, in developing countries, existing studies reveal significant inconsistencies between OD estimates for domestic and import/export commodities derived from interregional input-output (IO) tables and those from regional IO tables. These discrepancies create a significant challenge for properly forecasting the freight demand of regional/interregional multimodal transportation networks. To this end, this study proposes a novel integrated framework for estimating regional and international (import/export) OD freight flows for a set of key commodities that dominate long-distance transportation. The framework leverages multisource data and follows a three-step process. First, a spatial economic model, PECAS activity allocation, is developed to estimate freight OD demand within a specific region. Second, the international (import and export) freight OD is estimated from different zones to foreign countries, including major import and export nodes such as international seaports, using a gravity model with the zone-pair friction obtained from a multimodal transportation model. Third, the OD matrices are converted from monetary value to tonnage and assigned to the multimodal transportation super network using the incremental freight assignment method. The model is calibrated using traffic counts of the highways, railways, and port throughput data. The proposed framework is tested through a case study of the Province of Jiangxi, which is crucial for forecasting freight demand before the planning, design, and operation of the Ganyue Canal. The predictive analytics of the proposed framework demonstrated high validity, where the goodness-of-fit (R2) between the observed and estimated freight flows on specific links for each of the three transport modes was higher than 0.9. This indirectly confirms the efficacy of the model in predicting freight OD demands. The proposed framework is adaptable to other regions and aids practitioners in providing a comprehensive tool for informed decision-making in freight demand modeling.

When considering agricultural commodity transaction data, long sampling intervals or data sparsity may lead to small samples. Furthermore, training on small samples can lead to overfitting and makes it hard to capture the fine-grained fluctuations in the data. In this study, a multi-scale forecasting approach combined with a Generative Adversarial Network (GAN) and Temporal Convolutional Network (TCN) is proposed to address the problems related to small sample prediction. First, a Time-series Generative Adversarial Network (TimeGAN) is used to expand the multi-dimensional data and t-SNE is utilized to evaluate the similarity between the original and synthetic data. Second, a greedy algorithm is exploited to calculate the information gain, in order to obtain important features, based on XGBoost. Meanwhile, TCN residual blocks and dilated convolutions are used to tackle the issue of gradient disappearance. Finally, an attention mechanism is added to the TCN, which is beneficial in terms of improving the forecasting accuracy. Experiments are conducted on three products, garlic, ginger and chili. Taking garlic as an example, the RMSE of the proposed method was reduced by 1.7% and 1% when compared to the SVR and RF models, respectively. Its R2 accuracy was also improved (by 4.3% and 3.4%, respectively). Furthermore, TCN-attention and TCN were found to require less time compared to GRU and LSTM. The accuracy of the proposed method increased by about 5% when compared to that without TimeGAN in the ablation study. Moreover, compared with TCN, the Gated Recurrent Unit (GRU), and the Long Short-term Memory (LSTM) model in the multi-scale price forecasting task, the proposed method can better utilize small samples and high-dimensional data, leading to improved performance. Additionally, the proposed model is compared to the Transformer and TimesNet models in terms of its accuracy, deployment cost, and other metrics.

A caminho!

Abstract Background Over the last two decades, global stakeholders and the Nigerian government have invested approximately $2 billion in malaria control, reducing parasite prevalence to 23% from 42% to 2010. However, there is a risk that the modest gains will be reversed due to unmet resource gaps. Backward integration is presented in this paper as a viable option for sustainable funding of malaria intervention commodities in Nigeria. Methods Following a critical appraisal of the resource profile and malaria expenditure, a conceptual framework on backward integration as a means of ensuring long-term supply of malaria intervention commodities was developed. The study analysed secondary annual data from the National Malaria Elimination Programme to estimate commodity needs for the period 2018–2020, as well as total resources committed and the financial gap. Results The funds needed to implement national malaria interventions from 2018 to 2020 totaled US$ 1,122,332,318, of which US$ 531,228,984 (47.3%) were funded. The Nigerian government contributed 2.5%, the Global Fund (26.7%), the President’s Malaria Initiative (16.5%), and the UK Department for International Development (6.2%). The funding shortfall was $591,103,335, or 52.7% of the needs. Various funding scenarios were evaluated for their relative merits and limitations, including advocacy for more external funding, bank borrowing, increased domestic resources, and backward integration. Conclusions The study concluded that backward integration should be used, based on a government-led public-private partnership that will increase local production of malaria intervention commodities that are accessible and affordable through market-based demand and supply arrangements.

The study analyses the comovement between agricultural commodities (cocoa, coffee, corn, cotton, and soybeans) and sub-Saharan African equities (BRVM, Ghana, Kenya, Mauritius, and Uganda) heavily exposed to shocks from the COVID-19 pandemic spanning from January 2017 to December 2022. Through bivariate and multivariate wavelet analysis, the study identifies the adaptive and asymmetrical nature of the sampled markets both in the pre-pandemic and pandemic periods. The results highlight commodities as the main driving force behind equities, with few exceptions, and reveal these markets' safe-haven and hedge potential during the pandemic and the normal periods, except for the multivariate case. The bivariate findings suggest that global investors can consider investing in agricultural commodities and the sampled equities for enhanced portfolio diversification. By examining the interconnectedness of the sampled market across different timeframes and investment horizons, the study has uncovered significant implications for policy-making, portfolio diversification strategies, and risk management approaches.

The objective of this study is to analyze the spatial dynamics of vegetation cover and land use and the rate of deforestation in the Upper Paraguay River Basin, located in the Brazilian state of Mato Grosso, so that the information can contribute to the development of territorial planning strategies aimed at environmental conservation. Analyses of the dynamics of changes in vegetation cover and land use and deforestation rates were carried out through procedures carried out via geoprocessing (cutting, reclassification, resizing and quantification) of the annual mapping data (1985-2020) generated by the Annual Mapping Project of Land Cover and Use in Brazil (MapBiomas). In the investigated hydrographic unit, the Natural Vegetation Areas decreased by 19.82%, while the Agricultural Anthropogenic Areas increased by 76.74%. In the basin, the average deforestation rate was 1.57%/year, while in the Amazon, Cerrado and Pantanal biomes in the basin it was 4.09%/year, 1.58%/year and 0.75%/year, respectively. In the Pantanal, the average deforestation rate went from 0.77%/year to 0.88%/year from the initial five-year period (1986-1990) to the end (2016-2020), while in the same period there was a reduction in the Amazon (5.12%/year to 2.22%/year) and in the Cerrado (1.68%/year to 1.21%/year). It can be seen that deforestation occurs heterogeneously in the basin with greater intensity in the biomes located in the plateau region (Amazon and Cerrado), where anthropogenic areas have surpassed areas of natural vegetation. Although the average rate of deforestation is low in the Pantanal, compared to the other biomes, a recent upward trend (2016-2020) in the average was observed. [ABSTRACT FROM AUTHOR]

Since the onset of the COVID-19 pandemic, financial and commodity markets have exhibited significant volatility and displayed fat tail properties, deviating from the normal probability curve. The recent Russia-Ukraine war has further disrupted these markets, attracting considerable attention from both researchers and practitioners due to the occurrence of consecutive black swan events within a short timeframe. In this study, we utilized the Quantile-VAR technique to examine the interconnectedness and spillover effects between African equity markets and international financial/commodity assets. Daily data spanning from January 3, 2020, to September 6, 2022, was analyzed to capture tail risks. Our main findings can be summarized as follows. Firstly, the level of connectedness in returns is more pronounced in the lower and upper tails compared to the median. Secondly, during times of crisis, African equity markets primarily serve as recipients of systemic shocks. Lastly, assets such as Silver, Gold, and Natural Gas exhibit greater resilience to systemic shocks, validating their suitability as hedging instruments for African equities, in contrast to cryptocurrencies and international exchange rates. These findings carry significant implications for policymakers and investors in Africa equities. [ABSTRACT FROM AUTHOR]

This article explores the relationship between commodity price cycles and the US business cycle. Commodity price cycles are known to foster capricious macroeconomic activity, and understanding their behaviour offers valuable economic insight. The US business cycle is a key indicator of the broader economic conditions, reflecting changes in economic activity, consumer spending, and overall market conditions. By examining the dynamics and interplay between these two cycles, this study provides insights into the potential synchronisation, lag, or lead between commodity price cycles and the US business cycle. The study employs a Fourier analysis of commodity price cycles and the US business cycle. In addition, the same empirical method will be used to analyse historical rainfall patterns in the US as a means of furthering the role of historical rainfall patterns in shaping agricultural productivity and subsequent price movements. Results show dominant cycles of 14.2 years throughout the commodity price dataset, 3.8 years within the US business cycle, and 14.2 years in US historical rainfall patterns. The study also identifies several factors that influence the relationship between these two cycles, including global demand, trade policies, and financial market fluctuations. [ABSTRACT FROM AUTHOR]

C ommodity products such as gold, silver, and metal have been seen as safe havens in past economic crises. This situation increases the interest in commodity products. Due to the COVID-19 pandemic, quarantine decisions and precautions have caused an economic slowdown in stock markets and consumer activities. This inactivity in the economy has led to the COVID-19 recession that started in February 2020. Because of the increase in the number of COVID-19 cases, the difficulty of physical buying-selling transactions has shown that commodity products can be a safe investment tool. Based on the fact that machine learning approaches gained importance in commodity price prediction, the main goal of this study is to understand whether machine learning methods are meaningful for commodity price prediction even in extraordinary situations. To measure commodities' price volatility, a data set obtained from Borsa İstanbul is separated into pre-COVID-19 and COVID-19 periods. Daily prices for gold and silver commodities, from July 2018, which is before the ongoing COVID-19 recession, to October 2021 are used. The performances of the machine learning models were compared with MAE, MAPE, and RMSE metrics. The findings of this study point out that the LSTM model has more accurate predictions, especially in the pre-COVID-19 period. When considering the COVID-19 period only, SVR produces the best prediction results for the gold commodity and LSTM has the best prediction results for the silver commodity. [ABSTRACT FROM AUTHOR]

The Aur Serumpun farmer group is one of the growing farmer groups with their corn farming business. This farmer group is in Simpang Alahan Mati District, Kab. Pasaman, West Sumatra Province. So far, farmers only sell corn that has been dried directly. The processing of corn is a basic need for members of farmer groups in order to have product diversification which can ultimately increase the economy of these farmers. In the community service activities that were carried out previously (in 2019), processed products from corn were introduced, namely tortilla chips. In the 2020-2022 activities, business development will be carried out from Aur Serumpun farmer groups in accordance with the policy directions set by the members of the farmer groups themselves. Therefore, a discussion group forum was held and the signing of a cooperation agreement regarding the study of the prospects for producing tortilla chips which would be carried out by the aur allied farmer groups. The development of food product production of trotilla chips is an independent initiative from farmer groups that can collaborate with academics in the Department of Agricultural Product Technology, Andalas University. It was agreed from the discussion group forum that the head of the farmer group would conduct a study and socialize to other members of the farmer group regarding alternative product diversification of the commonly produced corn commodity. Further evaluation of the prospects for the implementation of the tortilla chips production process will be carried out next year.

Objective/Context: In the contemporary world film landscape (the 1990s to the 21st century), the film media allude to a new great “Latino” furor in Hollywood. This article proposes to stimulate reflection and debate on how Hollywood has cultivated this Latino “rage” from the 1920s to the present. Methodology: Based on the historiography of the process of building, industrializing and exporting “Latino-ness,” combined with diplomatic documentation and recent press information, and from a political economy of cinema point of view in particular, this article establishes the correlation between the supposed “Latinization” of Hollywood and the historical-economic reasons for the inclusion of Ibero-American artistic elements in “the Mecca of cinema.” Originality: Most studies on Hollywood’s “Latinization” emphasize the industry’s racialization, stereotypes and biases in its view of the “others” it has incorporated. The originality of this study lies in drawing on the less usual theoretical perspectives of the political economy of cinema (in conjunction with information on strategies and economic data currently circulating in the press) to explain more clearly the reasons for the myth of the “Latinization” of American cinema and its ultimate ends. The article posits a “Latinization” of economic, and not just ideological, causation and its explanation from theoretical perspectives on otherness and its implications. Conclusions: The analysis of this phenomenon evidences that the Ibero-American artistic elements incorporated by Hollywood are estimated by their “market value” as necessary and attractive “inputs” in front of Ibero-American audiences rather than by the reality of their talent. Additionally, incorporating Ibero-American talent is helpful to avoid eventual competition with other cinematographies in an industry that, like Hollywood, is, above all, an economic institution and a business.

Despite their importance in the global economy, the complex competitive dynamics of natural resource industries and their implications for business performance remain largely understudied in strategic management. This article identifies major traits that are highly relevant in natural resource industries, including the standardized nature of their products, their emphasis on process-based innovations, the presence of dual physical and financial derivative markets, and the importance of nonmarket forces that affect the creation and appropriation of rents from natural resources. We propose a general framework that guides our observations, and we discuss research opportunities for the study of firm strategy in natural resource industries. [ABSTRACT FROM AUTHOR]

O objetivo deste estudo é analisar a dinâmica espacial da cobertura vegetal e uso da terra e a taxa de desmatamento na Bacia Hidrográfica do Alto Paraguai, localizada no estado brasileiro de Mato Grosso, visando que as informações possam contribuir no desenvolvimento de estratégias de planejamento territorial voltadas à conservação ambiental. Foram realizadas análises da dinâmica de mudanças da cobertura vegetal e uso da terra e das taxas de desmatamento por meio de procedimentos efetuados via geoprocessamento (recorte, reclassificação, redimensionamento e quantificação) dos dados anuais de mapeamentos (1985-2020) gerados pelo Projeto de Mapeamento Anual da Cobertura e Uso do Solo no Brasil (MapBiomas). Na unidade hidrográfica investigada as Áreas de Vegetação Natural sofreram redução de 19,82%, ao passo que as Áreas Antrópicas Agrícolas aumentaram 76,74%. Na bacia a taxa média de desmatamento foi de 1,57%/ano, enquanto que nos biomas Amazônia, Cerrado e Pantanal na bacia foi de 4,09%/ano, 1,58%/ano e 0,75%/ano, respectivamente. No Pantanal a taxa média de desmatamento foi de 0,77%/ano para 0,88%/ano do quinquênio inicial (1986-1990) para o final (2016-2020), enquanto no mesmo período houve redução na Amazônia (5,12%/ano para 2,22%/ano) e no Cerrado (1,68%/ano para 1,21%/ano). Constata-se que na bacia o desmatamento ocorre de forma heterogênea, com maior intensidade nos biomas localizados na região de planalto (Amazônia e Cerrado), onde as áreas antrópicas superaram as áreas de vegetação natural. Embora a taxa média de desmatamento seja baixa no Pantanal, comparado aos demais biomas, foi observada uma tendência de aumento recente (2016-2020) na média