US Commodity Research Articles

Comparison of Indian corn with The US and South American commodity corn for wet milling starch yields

Higher starch extractability is critical for improving the profitability of a corn wet milling process. The starch extractability of Indian corn was compared to the corn grown in the USA, Brazil, and Argentina. Two corn crops (rainy and winter season) are produced every year in India. The endosperm hardness of Indian samples was similar to South American corn and higher than the US corn. Extractable starch yields of Indian corn samples were 2–3 % lower than the average starch yield of US commodity corn, however, was comparable with Brazilian, and Argentinian. No significant difference in starch yields between two crop years was observed. However, starch yield from the rainy (Kharif) season was lower compared to the winter (Rabi) season. Like South American corn, Indian corn contains a hard endosperm and resulted in a lower starch yield than the soft endosperm US corn. This information is useful for corn wet milling plants that buy commodity corn from various geographic locations around the world.

Exploring the unpredictable nature of climate policy uncertainty: An empirical analysis of its impact on commodity futures returns in the United States

AbstractThis study offers a nuanced examination of the interplay between climate policy uncertainty (CPU) and three categories of the US commodity futures returns (metals, energy, and soft commodities). By integrating a regression framework with a Markov regime‐switching approach, our results uncover both linear and nonlinear effects of CPU in varying volatility contexts. This comprehensive methodological approach sheds light on CPU's diverse impacts across various types of commodity futures. The findings illustrate CPU's notable influence on metal and energy futures in low‐volatility regime, contrasted with its more pronounced effect on soft commodities during high‐volatility regime. These insights are pivotal for investors strategizing in light of CPU, and underscore the significance of renewable energy in alleviating climate policy uncertainties within commodity markets.

Diversification, hedging, and safe-haven characteristics of cryptocurrencies: A structural change approach

This study investigates the influence of structural change on the diversification, hedging, and safe-haven characteristics of Bitcoin and Ethereum against various financial assets such as gold, the US Dollar Index, stock indices, oil, and commodity indices from August 7, 2015, to August 15, 2022, using the DCC–ARMA–GARCH models with the CUSUM test. Our results indicate that cryptocurrencies have the same characteristics vis-à-vis financial markets during the entire sample period and periods tied to the date of major international events (COVID-19 and the early-2022 Russia–Ukraine War). However, we find that cryptocurrencies play different roles against specific asset markets in different periods separated by structural change models. Our findings suggest that incorporating structural changes into a model accounts for higher volatility and may better describe the real-world capabilities of cryptocurrencies against financial assets.

Macroeconomic uncertainty and earnings management: evidence from commodity firms

This study examines the relationship between macroeconomic uncertainty and earnings management, using quarterly data of US commodity firms from the period 1990–2019. The findings show that oil and iron firms use both accruals and real activities to decrease earnings in quarters with high basis risk. Earnings management is economically significant. Further investigation provides fine-grained evidence that specific types of uncertainty (economic policy, climate policy, geopolitical) have varying effects on earnings management. The study also provides evidence that earnings management is aimed at giving investors useful information about the firms’ performance during uncertain times. The study contributes to previous research on uncertainty and earnings management. It also informs market participants about the financial reporting quality of commodity firms, and has practical implications for financial reporting regulation in extracting industries.

EMERGING MARKETS ECONOMIES SENSITIVITY TO US DOLLAR'S STRENGTH DURING RUSSIA-UKRAINE WAR

This paper aims to study the sensitivity of emerging markets economies (EMEs) to the US Dollar's strength during the Russian-Ukrainian war in the long and the short run, where Autoregressive distributed lag model (ARDL) was used to analyze time series daily data in the period 2020-2023. We studied the impact of the US Dollar's strength on EMEs asset prices: EMEs currencies, EMEs bonds (US Dollar-denominated) and EMEs stocks, including the factor of the Russian-Ukrainian war as a dummy variable, as well as the effects of various economic factors: Federal Funds Rate, the US 10-year bond yield, commodity prices index and gold prices. The results showed that Russian-Ukrainian war has a significant positive impact on US Dollar's strength. In the other hand, both of them have significant negative impacts on EMEs asset prices, with varying degrees, where the aforementioned economic factors have asymmetric impacts. Moreover, EMEs stocks were the most Sensitive to Russian-Ukrainian war, US Dollar's strength and commodity prices, meanwhile EMEs bonds market were the least Sensitive.

The impact of financial speculation on futures contracts price movements: A study of the US markets for dairy commodities

Research background: The study analyzes whether financial speculation destabilizes commodity prices in light of recent price volatility and spikes in agricultural commodities. The study delves deeper into the US dairy futures markets, which are less studied by other authors in their research and relatively new in comparison to other agricultural commodity markets. These dairy commodity futures contracts provide dairy businesses and farmers the chance to hedge against price risks, which are particularly crucial in uncertain economic times such as the post-2020 COVID-19 pandemic timeframe. The analysis makes use of the weekly returns on futures contracts for nonfat milk powder, butter, milk class III, and cheese that are obtained from the Chicago Mercantile Exchange (CME). Purpose of the article: Conduct an empirical study to evaluate the effect of financial speculation on dairy product prices on US commodity markets, including the post-2020 timeframe. Methods: Time series analysis is used in the investigation: the generalized auto-regressive conditional heteroskedasticity (GARCH) method, the Granger causality test, and the Augmented Dickey-Fuller (ADF) test. Findings & value added: Our analysis's findings show that, even though most commodities experienced an increase in return volatility during the post-2020 period, there is no evidence for financial speculation being the cause of increased returns from dairy futures contracts. The research also suggests that financial speculation, in some cases, even lowers the volatility of dairy futures prices. Therefore, non-commercial market participants may help to distribute price risks, making these markets more liquid.

From craft to industry and back: transnational efforts in reconstructing tin’s social meaning to the retail consumer (1950s–1960s)

PurposeCommodity histories generally posit colonies’ roles as mere producers but have overlooked their roles in shaping global consumption. This study aims to investigate how Malayan tin producers and British colonial institutions used public relations and advertising strategies as entrepreneurial tools to fend off competition from substitutes amid global economic and geopolitical transitions during the height of the Cold War crisis and post-war boom in the 1950s and 1960s.Design/methodology/approachThis study draws on archival research of newspaper reports written in Singapore, the USA and Britain from the inter-war years until the 1960s. It also consults advertisements placed by the Malayan Tin Bureau on Time and Scientific American, data and views on tin scarcity by US congressional reports and commodity trends data published by the US Department of Commerce and the US Department of the Interior.FindingsThis paper demonstrates how the value of tin is recreated by manipulating its symbolic meanings and embedding them within the national and political contexts of the targeted consumer markets. This creative resistance against tin substitution was enacted through a transnational collaboration among colonial institutions, entrepreneurs in colonies and marketing strategists across geographies and territories.Research implicationsThis paper provokes further reflections on the importance of socially constructed meanings in shaping the market value of a product and the understanding of embedded political value systems in marketing generic commodities. Future research may adopt this perspective to reassess the framing of meanings of commodities in the contemporary setting, especially against rising concerns on the sustainability of mining natural resources, including minerals.OriginalityThis study integrates the perspectives of Malayan tin producers in reframing the meaning of a commodity and so, widens the scope of historical analyses of commodities beyond the industrialized global North. It reassesses how a commodity’s marketing value evolves and interacts with colonial politics. It also highlights the collaborative nature of colonial governments and local producers in developing new uses and representations of a generic commodity to create new markets for its consumption.

Links between US and Turkish agricultural commodity Markets: Nonlinear dependence and tail risk

In these unprecedented times, marred by the effects of the Covid-19 pandemic, global warming, and the war in Ukraine that began in February 2022, new approaches such as tail dependence have attracted more interest than conventional market dependence methodologies in analyzing time series in order to evaluate market linkage. In this study, we use the tail-restricted integrated regression function (IRF), introduced as a new methodology for nonlinear tail-mean dependence analysis. The IRF approach has several advantages over the existing tail-dependence measures focused solely on the occurrence of single tail events. To examine market dependence and tail risk, we analyze the nexus between the US and Turkish agricultural commodity markets over the period January 5, 2016–May 31, 2022. In addition to the daily prices of barley, corn, and wheat on agricultural commodity markets, we use Brent oil as a representative for the energy market, interest, and foreign exchange rates for financial markets as variables in our study. The results of upside and downside asymmetric risk spillovers show the direction of impact from the US agricultural market to the Turkish agricultural market. The findings suggest that, following the launch of a spot market in Türkiye, the launch of an agricultural commodity futures market will enhance the completeness and the link between the spot and derivatives markets there.

Extreme risk spillover effect and dynamic linkages between uncertainty and commodity markets: A comparison between China and America

This study aims to assess the extreme risk spillover effect and dynamic linkages between uncertainty and commodity markets. We consider geopolitical risk, economic policy uncertainty, and climate policy uncertainty to measure uncertainty shocks and compare two common commodity markets spanning China and America. Then, the quantile-based TVP-VAR method is used to conduct an empirical study using monthly data from May 2008 to August 2021. Our findings demonstrate that, first, the interaction between uncertainty and commodity markets is particularly vulnerable to extreme shocks, with greater lower quantile spillover effects than those at the upper quantile. Second, there is a time-varying variation in the spillover effects between uncertainty and commodity markets, and the spillovers are vulnerable to shocks from a significant crisis. Third, the impulse response of commodity price indexes to the uncertainty indexes is time-varying, with shocks fluctuating sharply in the short run and stabilizing over the long term. Finally, compared to American commodity markets, Chinese commodity markets exhibit greater complexity and higher volatility in their initial responses to uncertainty shocks. In most cases, the US commodity market is a transmitter of risks, while the Chinese commodity market often receives risks, with uncertainty shocks acting as both the transmitter and receiver. These intriguing findings can serve as a guide to prevent market risks and optimize investment decisions.

Characterization and Comparison of Wet Milling Fractions of Export Commodity Corn Originating from Different International Geographical Locations

AbstractSoft endosperm corn has evident characteristics suitable for wet milling but is susceptible to breakage and fracture during transportation. In this study the millability of commodity corn samples with different endosperm hardness originating from different international locations is compared, and its economic impact on corn importers for wet milling is discussed. The broken corn and foreign material (BCFM) for the soft endosperm US commodity corn ranges from 1.13% to 5.57% which is higher than other commodity corn from other international locations exported to the same country. US corn exported to different international markets shows higher starch yield in a range of 5–8% compared to the corn from different geographical locations exported to the same market. The excess starch from US corn directly translates to an additional revenue of 8.13–13 million USD per year for a 2540 MT day−1 wet mill plant. Therefore, it can be concluded that the soft endosperm US commodity corn, despite higher breakage, has superior millability and gives higher starch yields compared to the hard endosperm corn from other international locations which has comparatively lower BCFM. There are technologies that can be used to process broken corn separately after the initial cleaning process.

Impacts of weather conditions on the US commodity markets systemic interdependence across multi-timescales

This study analyses the role of weather conditions in driving the interdependence of the US commodities system comprising energy, agricultural, and metal markets from January 2000 to October 2021. By measuring the commodity markets systemic interdependence through dynamic equicorrelation (DECO), total returns spillover (TRS), upside and downside tail risk spillover (UTRS/DTRS), we investigate the co-vary dynamics between weather conditions and interdependence of commodity system in time-frequency domains. We discover that low temperature level plays a significant role in leading the commodities DECO at 32–64 months time scale. It is shown that high temperature level, global horizontal irradiance, and wind speed acts as an important part in driving TRS at around 64-month time scale. We also find that high temperature level and global warming conditions have a negative leading influence on UTRS at 32–64 months time scale whilst cloud coverage, precipitation and runoff have a positive leading impact on the UTRS at the same time scale. Moreover, temperature is found to lead the change of DTRS at around 64-month time scale persistently throughout the sample period. Through incorporating heterogeneous investment horizons, our findings provide practical implications for commodity investors in adapting to climate change.

The impact of maritime shipping disruptions on US tree nut exports and inventories

AbstractThis paper assesses the impact of the 2021/22 maritime shipping disruptions on US tree nut exports and domestic inventories. Using detailed trade data for almonds, pistachios, and walnuts and counterfactual empirical methods that account for treatment dynamics, we estimate the trade response to maritime shipping disruptions at the US port and commodity level. We find that tree nut shipments were 457 million pounds or $943 million below the counterfactual between May 2021 and July 2022. Almonds experienced the brunt of those trade losses, while pistachio and walnut exports recorded smaller losses and recovered faster. Our estimates imply that maritime shipping disruptions are a primary driver of surging tree nut inventories. Without the trade destruction caused by maritime shipping disruptions, the almond inventory could have been 38.7% lower and close to the long‐run average in the 2021/22 marketing year. [EconLit Citations: F14, Q17].

Multivariate models of commodity futures markets: a dynamic copula approach.

We apply flexible multivariate dynamic models to capture the dependence structure of various US commodity futures across different sectors between 2004 and 2022; particular attention is paid to the 2008 financial crisis and the COVID-19 pandemic. Our copula-based models allow for time-varying nonlinear and asymmetric dependence by integrating elliptical and skewed copulas with dynamic conditional correlation (DCC) and block dynamic equicorrelation (Block DECO). Flexible copula models that allow for multivariate asymmetry and tail dependence are found to provide the best performance in characterizing co-movements of commodity returns. We also find that the connectedness between commodities has dramatically increased during the financial distress and the COVID-19 pandemic. The impacts of the financial crisis appear to be more persistent than those of the pandemic. We apply our models to some risk management tasks in the commodity markets. Our results suggest that optimal portfolio weights based on dynamic copulas have persistently outperformed the equal-weighted portfolio, demonstrating the practicality and usefulness of our proposed models.

Application of fault tree analysis: Failure mode and effect analysis to evaluate critical factors influencing non‐GM segregation in the US grain and feed supply chain

AbstractBackground and ObjectiveAdventitious presence (AP) or unintentional commingling of genetically modified (GM) grain in non‐GM grain lots is a concern for stakeholders who wish to produce non‐GM grain and grain‐based products. AP that exceeds tolerance levels results in the loss of product marketability as “non‐GM” and subsequent loss of a premium market price. The objective of this study was to evaluate the US commodity corn supply chain using Fault Tree Analysis (FTA) and Failure Mode and Effect Analysis (FMEA) to assess factors contributing to AP in supply chains that handle both GM and non‐GM products.FindingsSystematic analysis of the farm‐to‐feed supply chain identified key processes and actors involved. FTA identified 27 factors potentially contributing to AP along with their root causes. Ranking of factors using FMEA prioritized 14 factors as “critical” with a comparatively higher likelihood of occurrence and impact on AP levels.ConclusionsAP contributing factors were found across the supply chain, indicating that successful segregation relies on all supply chain participants. Identification and prioritization of critical factors make it possible to target resources to a small number of significant factors to attain low AP levels rather than distributing resources across the entire set of factors.Significance and NoveltyThis study is an initial attempt to provide insights into the assessment and management of AP in the grain and feed supply chain with a novel application of FTA and FMEA techniques.

Small fish in big ponds: Connections of green finance assets to commodity and sectoral stock markets

We analyze return and volatility connectedness of the rising green asset and the well-established US industry stock and commodity markets from September 2010 to July 2021. We find that the time-varying return and volatility connectedness have exhibited serious crisis jumps. Some individual assets of both the green and commodity markets are in connection to the US sectoral stock market returns, and the volatility connections are even more common than the return connections. Furthermore, some financial and economic uncertainty indicators manifest positive impacts from the volatility of some ‘big pond’ markets for e.g. commodities, whereas some others affect the connectedness negatively. Additional analysis of financial and economic uncertainty indicators manifests positive impacts from the volatility of some ‘big pond’ markets, e.g., commodities, while others negatively affect the connectedness.

Hedging pressure and liquidity provision in commodity options markets

AbstractThis study examines hedging pressure and liquidity provision in commodity options markets using data from the weekly US Commodity Futures Trading Commission Commitments of Traders and Disaggregated Commitments of Traders reports. The “options‐only” reports are obtained from futures‐only and futures‐and‐options‐combined reports. We build the futures price series based on commodity options expiration dates. There are significant hedging pressure effects in commodity options markets. The empirical results indicate that commercials (swap dealers) provide liquidity to non‐commercials (money managers) in commodity options markets in the short run.

Ride the trend: Is there spread momentum profit in the US commodity markets?

AbstractSome previous researchers have argued that trading strategies based on calendar spread time series momentum (STSM) can deliver significant returns (Szymanowska et al. 2014; Boons and Prado 2019), which, if true, is at odds with the efficient market hypothesis. These arguments however, do not exclude the unrealisable futures contract roll yield and are also affected by other empirical and statistical issues that may lead to misleading results. With more than 30 years of data, we investigate STSM in 22 US commodity futures markets. First, we assess whether past spread returns can predict future returns, a necessary condition for the existence of momentum. We find predictability to be very weak after correcting for the issues affecting prior research. Second, we implement STSM‐based investment strategies. We compare STSM profits for individual markets and portfolios to profits generated by a simple long‐only benchmark strategy that does not require any predictability. STSM does not generate returns statistically different from the benchmark trading strategy, with both strategies generating very low or negative returns. For the momentum to outperform the benchmark strategy, predictability should be three times larger than observed from real data, but would entail substantial downside risk. In sum, the empirical evidence indicates that returns from STSM‐type strategies are illusive for the commodities and period studied. Our results strongly suggest that inclusion of unrealisable roll yield generates the illusion of profitable STSM trading strategies in previous research.

Does economic policy uncertainty drive nonlinear risk spillover in the commodity futures market?

We propose the rolling tail-event driven network technique (RTENET) to measure the dynamic nonlinear tail risk spillover of 20 US commodity futures. In addition, we investigate the effect of economic policy uncertainty (EPU) on risk spillover based on quantile-on-quantile regression (QQR). We find that the risk spillover effect increases sharply and that the market is tightly connected when EPU is at a high level. Crude oil, silver and corn, the three greatest risk transmitters in the system, need more attention. More importantly, the effect of EPU on the risk spillover of the commodity futures market is asymmetric and heterogeneous. When the risk spillover falls within extremely high quantiles, a significant positive effect of EPU is observed. In addition, grain and soft crops are more sensitive to EPU. Our findings provide a reference for policy-makers and investors to manage commodity futures markets in different uncertainty periods.

TAIL-RISK DEPENDENCE NETWORKS IN THE US COMMODITY SECTORS. HAS COVID-19 MADE A THING?

The objective of this paper is to examine the tail-risk dependence networks in the US commodity sectors: agriculture, livestock, energy, industrial and precious metals before and during COVID-19. Applying penalised quantile regression models extended with the dummy variable for the COVID-19 period in daily commodity returns and in the time horizon 3/1/2012 to 31/5/2022, CoVaR estimations are provided. The main empirical results are that (i) COVID has affected the tail-risk connectedness between commodities in the case of their extreme good events (ii) energy sector has remained a risk receiver in the risk-network of commodities independently of their conditions (welfare, burst) and (iii) the risk transmission linkages between commodity sectors are mostly positive. As a result, all commodity markets counterparts (farmers, investors, policymakers, governments) should not ignore pandemic uncertainties, as well that shocks in the other commodities sectors can control the booms and bursts of the energy sector. Finally, commodity markets seem to attract more speculators than hedgers. To the best of author(s) knowledge this is the first research paper that examines formally potential difference in the pattern of the tail risk dependence of the 5 US commodity sectors with respect to COVID’s existence and defines new connectedness measures for the detection of the tailrisk net transmitters and receivers of the US commodity sectors’ network.

A Deep Reinforcement Learning-Based Decision Support System for Automated Stock Market Trading

Presently, the volatile and dynamic aspects of stock prices are significant research challenges for stock markets or any other financial sector to design accurate and profitable trading strategies in all market situations. To meet such challenges, the usage of computer-aided stock trading techniques has grown in prominence in recent decades owing to their ability to rapidly and accurately analyze stock market situations. In the recent past, deep reinforcement learning (DRL) methods and trading bots are commonly utilized for algorithmic trading. However, in the existing literature, the trading agents employ the historical and present trends of stock prices as an observing state to make trading decisions without taking into account the long-term market future pattern of stock prices. Therefore, in this study, we proposed a novel decision support system for automated stock trading based on deep reinforcement learning that observes both past and future trends of stock prices whether single and multi-step ahead as an observing state to make the optimal trading decisions of buying, selling, and holding the stocks. More specifically, at every time step, future trends are monitored concurrently using a forecasting network whose output is concatenated with past trends of stock prices. The concatenated vectors are subsequently supplied to the DRL agent as an observation state. In addition, the suggested forecasting network is built on a Gated Recurrent Unit (GRU). The GRU-based agent captures more informative and inherent aspects of time-series financial data. Furthermore, the suggested decision support system has been tested on several stock markets such as Tesla, IBM, Amazon, CSCO, and Chinese Stocks as well as equity markets i-e SSE Composite Index, NIFTY 50 Index, US Commodity Index Fund, and has achieved encouraging profit values while trading.