Commodities

This study analyzes the market efficiency of crude oil markets, namely Brent and West Texas Intermediate (WTI), during three different periods: pre-COVID-19, during the COVID-19 pandemic, and during the ongoing Russia–Ukraine military conflict. To evaluate the efficiency of crude oil markets, wavelet entropy is computed from price, return, and volatility series. Our empirical results show that WTI prices are predictable during the Russia–Ukraine military conflict, but Brent prices are difficult to predict during the same period. The prices of Brent and WTI were difficult to predict during the COVID-19 pandemic. Returns in Brent and WTI are more difficult to predict during the military conflict than they were during the pandemic. Finally, volatility in Brent and WTI carried more information during the pandemic compared to the military conflict. Also, volatility series for Brent and WTI are difficult to predict during the military conflict. These findings offer insightful information for investors, traders, and policy makers in relation to crude oil energy under various extreme market conditions. Full article

Secondhand clothing has undergone a significant transformation from a vital household asset in the pre-industrial era to a dynamic segment of global trade in the 21st century. However, the advent of fast fashion has led to overproduction and mass consumption of inexpensive garments, fueling a surge in the secondhand clothing trade. Between 2002 and 2022, the market value of this industry quadrupled, with exports accounting for 1.17% of total global clothing exports in 2022. This study examines global secondhand clothing exports using export competitiveness tools such as revealed comparative advantage (RCA), the index of export market penetration (IEMP), the trade intensity index (TII), unit values, market shares, and the compound annual growth rate (CAGR). The top eleven secondhand clothing exporting countries are analyzed for a ten year period (2013 to 2022) using the United Nations Commodity Trade Statistics Database. The analysis reveals notable trends: the United States and China dominate the market, while Pakistan and the UAE exhibit the highest growth rates. The study also reaffirmed that trade patterns for secondhand clothing continue to flow from the Global North—including China—to the Global South, a trend observed since the early 2000s. This research provides a comprehensive, current analysis of the ever growing secondhand clothing export market within the sustainable management of the secondhand clothing context. Full article

The article analyzes the Granger-based causal relationship between two major crude oil markets, namely Brent and West Texas Intermediate (WTI), by using the standard vector autoregression (VAR) framework. In this regard, the effects of the COVID-19 pandemic and the Russia–Ukraine war on causality between Brent and WTI are examined. The empirical results from Granger-causality tests show (a) strong causality from Brent to WTI during the period prior to the COVID-19 pandemic and Russia–Ukraine war, (b) no causality from WTI to Brent during the period prior to the COVID-19 pandemic and Russia–Ukraine war, (c) no causality from Brent to WTI during the COVID-19 pandemic, (d) evidence of causality from WTI to Brent during the COVID-19 pandemic, and (e) no evidence of causality from both markets during the period of Russia–Ukraine war. In addition, causality tests in quantiles support results from the linear Granger causality tests in general. However, contrary to the standard linear causality test, the quantile-in-regression causality test shows that Brent returns cause WTI returns during the pandemic period and WTI returns cause Brent returns before the pandemic. Furthermore, the results from the time-varying Granger causality tests support all conclusions from the standard linear (and static) Granger causality test, except the hypothesis that Brent causes WTI during the pandemic. Moreover, the time-varying Granger tests show evidence that causality between Brent and WTI clearly varies across the pandemic and war periods. Revealing the causalities between Brent and WTI across periods of economic and political stability, pandemic, and war would help policymakers develop appropriate energy policy and help investors determine appropriate risk management actions. Full article

The focus of this investigation was to examine the effects of energy consumption, agricultural commerce, and productivity on CO₂ emissions in Nigeria using quantile regression. Time series data from 1960 to 2021 were used. The findings revealed that the impact of agricultural raw materials imports (AGRIMs) and exports on carbon footprints is positive. There is a prevalence of a set of notable percentile differences in the conditional distribution of the variables on CO₂ emissions. Initially, the coefficient of energy consumption (EnCons) was high, but constantly nosedived from the 25th quantile until it reached the 90th quantile when it picked up again, and the same was true in the case of AGRIM. Thus, a 1% increase in agricultural imports will bring about 0.0047—a significant unit increase in CO₂ emissions in Nigeria from the 0.382946 coefficient in the 10th quantile to the 0.264392 coefficient in the 50th quantile, and thereafter, the effects become insignificant. Profound significant variance across disparate percentiles in the conditional spread of AGRIM, food production index (FPI), CPI, and FDI was found. It further showed that the effects of the regressors on carbon emissions differ over the quantiles. Overall, AGRIM and EnCons have positive and significant effects on carbon emission. However, the agricultural raw material export has significant negative effects on CO₂ emissions as the movement (transportation) of goods within a country prior to export involves a huge level of carbon release. This study provides recommendations and policy implications. Full article

Brazil is a resource-rich economy that relies heavily on the exports of several important commodities. The variability of commodity prices affects both the economy and the stock market. This study investigates the relationship between commodity price shocks and stock returns in Brazil using a structural vector autoregressive (SVAR) model. This study uses monthly data on prices of five major export commodities, stock returns, and several control variables, covering the period from January 2010 to December 2022. To account for the Brazilian economic crisis between 2014 and 2016, we have analyzed the effects of commodity prices on stock prices in three different time periods, namely, before the economic crisis (January 2010–March 2014), during the economic crisis (April 2014–December 2016), and after the economic crisis (January 2017–December 2022). The empirical results of this study provide evidence to conclude that stock returns increase following a positive global commodity price shock or a positive exchange rate shock. The effects are more noticeable during the economic crisis in Brazil. The results also show that the volatility of Brazilian stock returns is mostly explained by global oil prices and exchange rate movements in the long run. Full article

We examine how LNG carrier port congestion in European ports, measured via detailed vessel-level AIS data, affects euro area energy inflation. As energy inflation significantly affects headline inflation, this study provides an additional factor that can contribute to inflationary pressures. Overall, the results show that higher port congestion increases natural gas prices with the latter having an impact on energy inflation. The reaction stands at 0.1% per 1% shock in port congestion. These findings underline the relationship between the shipping industry and the real economy and support the view that shipping developments can potentially be used as leading indicators. Full article

The cryptocurrency market is characterized by extreme volatility, with events such as the Terra-LUNA crash of 2022 raising significant questions about the resilience of algorithmic stablecoins. This paper investigates the collapse of LUNA Classic during the USTC depeg, focusing on the role of trading volumes and collateral assets like Bitcoin in amplifying the price crash. Using a Vector Logistic Smooth Transition AutoRegressive (VLSTAR) model, we analyze daily data from October 2020 to November 2022 to uncover how exogenous volumes influenced LUNA’s price trajectory during the crisis. Our findings reveal that high trading volumes, particularly during regime two (the post-depeg period), significantly exacerbated the price decline, validating the impact of large-scale liquidations on LUNA’s price path. Additionally, Bitcoin volumes played a critical role in destabilizing the system, confirming that the liquidity of underlying collateral assets is pivotal in maintaining price stability. These insights contribute to understanding the systemic vulnerabilities in algorithmic stablecoins and offer implications for future stablecoin design and risk management strategies. They are relevant for investors, policymakers, and researchers seeking to be aware of market volatility and prevent future crises in stablecoin ecosystems. Full article

Property Assessed Clean Energy (PACE) programs finance energy efficiency and renewable energy improvements to residential and commercial properties with a special tax assessment added to property taxes. This paper surveys the literature and documents the quantitative estimates of the environmental and economic benefits. This paper extends to discuss the securitization of PACE loans. The issuance of PACE green bonds frees up capacity for more PACE improvements. In addition, we summarize the concerns raised after the programs have been implemented. Those concerns include consumer protection, audit after program implementation, and lien-related risks for lenders. We discuss those concerns and suggest measures to continue to grow PACE financing. The success of PACE programs will contribute to reducing carbon emissions, mitigating climate change and to achieving six of the seventeen United Nations Sustainable Development Goals (SDGs). Full article

This research paper examines the risks posed by the MIH EV Open platform to German automotive suppliers, in particular, the risk of commoditization and falling into a commodity trap. The term commodity trap describes a situation in which companies dealing with standardized products or services face intense price and margin pressure and struggle to differentiate themselves from competitors. The MIH EV Open platform, established by Foxconn, also known as Hon Hai Precision Industry Co. Ltd., headquartered in Tucheng, Taipei, Taiwan, aims to create a collaborative platform for the comprehensive development of key software, hardware components, and services in the electric vehicle (EV) industry. It unites over 2700 companies from more than 70 countries and fosters collaboration to accelerate the development and market entry of new EV products. This paper analyzes the MIH EV Open business ecosystem model and assesses the strengths and weaknesses of German suppliers in addressing these challenges. This study highlights strategic approaches, including innovation, portfolio adaptation, customer relationships, and sustainability practices, that can enable German suppliers to mitigate commodity trap risks. The findings underscore the importance of proactive, segment-specific strategies amidst the transformation of the automotive industry. Key insights are provided on the potential impact of open platform ecosystems and recommendations for German automotive suppliers to maintain competitiveness. This research fills a gap in the literature by examining the commoditization risks posed by the MIH EV Open platform for German automotive suppliers. Unlike previous studies that focus on traditional market structures, this study explores the novel dynamics introduced by platform ecosystems and provides strategic insights to mitigate these risks. Full article

This paper analyzes the return performance of various commodity futures-based dynamic portfolios over the period from 31 January 1986 to 31 July 2023. By constructing 30 distinct portfolios categorized by style and performance, we assess their potential for enhancing the performance of traditional portfolios consisting of equity and bonds. We find that most commodity portfolios do not offer statistically significant returns, either in terms of average or risk-adjusted returns. Only the portfolios in the basis category and portfolios in the term structure category exhibit significantly positive risk-adjusted returns, indicating their potential value for portfolio enhancement. The performance of these portfolios is not different in pre-financialization and financialization periods. Full article

This study evaluates the efficiency of China’s seven emission trading schemes (ETS) piloted in 2013. We evaluate seven pilots’ overall technical and scale efficiencies and temporal dynamics during 2014–2023. We use a bootstrap correction data envelopment analysis (bootstrap-DEA), which guarantees a more accurate efficiency estimation than the traditional DEA model. The results show that the average overall (pure technical) efficiency of the seven pilot markets increased from 0.612 (0.844) in 2014 to 0.898 (0.990) in 2023. Furthermore, we document that seven ETS pilots differ remarkably in efficiency and transaction price, whilst all have shortages. Specifically, the small-scale market transaction is the main constraint effect on the average scale efficiency of the ETS. This study provides concrete recommendations for policy makers to consummate institutional designs to improve ETS efficiency. Full article

This study introduces a robust model that captures wheat futures’ volatility dynamics, influenced by seasonality, time to maturity, and storage dynamics, with minimal calibratable parameters. Our approach reduces error-proneness and enhances plausibility checks, offering a reliable alternative to models that are difficult to calibrate. Transferring estimated parameters from liquid to illiquid markets is feasible, which is challenging for models with numerous parameters. This is of practical importance as it improves the modeling of volatility in illiquid markets, where price discovery is less efficient. In liquid markets, on the other hand, where speculative activity is high, we find that implied volatility is usually the best measure. Additionally, the introduced volatility model is suitable for pricing options on wheat futures as a risk-neutral measure. Full article

The transition to battery electric vehicles (BEVs) poses significant challenges for automotive suppliers, particularly in the Northern Black Forest Region, Germany’s largest industrial area. This study examines the risk of falling into the commodity trap and the impact of platform economics on these suppliers. A VUCA (volatility, uncertainty, complexity, ambiguity) analysis is used to assess the consequences of the open platform approach promoted by the Mobility in Harmony (MIH) Consortium. The methodology is based on a comprehensive literature review on commodity traps and platform economies, as well as an analysis of the MIH Consortium’s strategies for standardizing BEV components. The results show that while the MIH Consortium’s modularization and standardization efforts can reduce costs and facilitate mass production, they also intensify competition and limit differentiation, threatening the profit margins of smaller suppliers. The study highlights the importance of strategic positioning and innovation to counter these risks. It concludes with recommendations on how automotive suppliers can adapt to the evolving environment and be agile in responding to new technological standards and market dynamics. Full article

The goal of this paper is to derive closed-form approximation formulas for the spread option value and Greeks by using double integration and investigating the exercise boundary. We have found that the straight-line approximation suggested in previous research does not perform well for curved exercise boundaries. We propose a novel approach: to integrate in a sector and find a closed-form formula expressed in terms of the bivariate normal CDF. We call it the sector formula. Numerical tests show the good accuracy of our sector formula. We demonstrate applications of the formula to the market data of calendar spread options for three major commodities, WTI, Natural Gas, and Corn, listed on the CME site as of May, April, and June 2024. Full article

The dynamic structure of electricity markets, where uncertainties abound due to, e.g., demand variations and renewable energy intermittency, poses challenges for market participants. We propose generative adversarial networks (GANs) to generate synthetic electricity price data. This approach aims to provide comprehensive data that accurately reflect the complexities of the actual electricity market by capturing its distribution. Consequently, we would like to equip market participants with a versatile tool for successfully dealing with strategy testing, risk model validation, and decision-making enhancement. Access to high-quality synthetic electricity price data is instrumental in cultivating a resilient and adaptive marketplace, ultimately contributing to a more knowledgeable and prepared electricity market community. In order to assess the performance of various types of GANs, we performed a numerical study on Turkey’s intraday electricity market weighted average price (IDM-WAP). As a key finding, we show that GANs can effectively generate realistic synthetic electricity prices. Furthermore, we reveal that the use of complex variants of GAN algorithms does not lead to a significant improvement in synthetic data quality. However, it requires a notable increase in computational costs. Full article

In this study, we investigate the ability of three higher-order risk-neutral return cumulants to predict short maturity (weekly) returns of oil futures. Our data includes weekly West Texas Crude Oil futures options that expire in 7 days (7DTE). Using a model-free approach, we estimate these risk-neutral return cumulants at the beginning of each options expiration cycle. Our results suggest that the third risk-neutral return cumulant consistently predicts the returns of various oil futures (including WTI, Brent, Dubai, Heating Oil, and RBOB Gasoline). We compare our findings with 14 other predictors and offer a theoretical explanation for the negative coefficient observed for the 7DTE third risk-neutral return cumulant. Our theory connects higher-order risk-neutral return cumulants with the risk premiums of oil futures. Furthermore, our quantitative investment strategy favors the predictability of oil futures returns. Full article