Commodity Price Volatility Research Articles

Digitalization and Sustainability of Supply Chains: Assessing the Potential of the DYLLI Application in the Hotel, Restaurant, and Catering Industry

This article investigates the integration of Information and Communication Technologies (ICT) to enhance service quality and sustainability within the Hotel, Restaurant, and Catering (HoReCa) sector. The study aims to explore how improved communication through ICT can optimize supply chain processes and elevate service standards. A usability analysis was conducted via surveys among HoReCa companies to assess perceptions of a demo application designed to facilitate sustainable practices. Additionally, a competitiveness analysis, grounded in Porter’s Five Forces model, examines the dynamics of mobile applications in the HoReCa market. The usability analysis validates business model assumptions while providing insights into market challenges and opportunities. The findings indicate that volatile commodity prices and recruitment difficulties are significant challenges for restaurateurs, highlighting the need for tools that support sustainable operations. The proposed ICT solutions serve as innovative tools that improve communication between HoReCa businesses and their suppliers, thereby directly influencing service quality. By promoting transparency in pricing and collaboration terms, these solutions align with sustainable supply chain principles, addressing environmental concerns while meeting evolving consumer expectations. This research contributes to the literature on competitiveness in the HoReCa sector by emphasizing the pivotal role of technology in fostering both service quality and sustainable development.

The tail risk premium in the oil market

This paper studies tail risk and its option-implied risk compensation in the crude oil market. We identify economically large premia for upside and downside tail risks that significantly forecast crude oil futures returns. These premia are also reflected in the convenience yield for physical oil, which amplifies the predictive power for spot returns. Oil tail risk premia are not spanned by aggregate uncertainty measures, suggesting that shifts in market-specific risk attitudes contribute to commodity price volatility and return predictability.

Sustainability Implications of Commodity Price Shocks and Commodity Dependence in Selected Sub-Saharan Countries

Sub-Saharan economies often rely heavily on a narrow range of commodities, making them particularly vulnerable to price fluctuations in global markets. This volatility predisposes these countries to economic instability, threatening short-term growth and long-term development goals. As a result, this study examines the sustainability implications of commodity price volatility and commodity dependence for 31 Sub-Saharan African countries from 2000 to 2023. Eleven agricultural commodity-dependent countries, six energy commodity-dependent countries, and fourteen mineral and metal ore-dependent countries were chosen. This study uses balanced annual panel data from World Development Indicators, World Bank Commodity Price Data, and Federal Reserve Bank Data. The data were analyzed using the VECM, and this study’s findings were threefold and unanimous for all three categories of commodities (agricultural, energy and mineral, and metal ore). First, commodity dependence is positively related to economic growth, suggesting that higher commodity prices benefit the economy in the long run. Second, commodity price volatility is negatively related to economic growth, indicating adverse impacts on economic stability in the long run. Third, commodity dependence is positively related to commodity price volatility in the long run. By analyzing the interconnectedness of these factors, this study underscores the need for diversified economic policies and sustainable practices to reduce vulnerability and promote sustainable development in the region. The findings highlight the critical role of strategic resource management and policy interventions in achieving economic stability and ensuring the well-being of future generations.

War in foreign lands, hunger at home

The war between Russia and Ukraine pitted two major agricultural countries and threatened the African continent with food insecurity and hunger. The war came at a time when the world was emerging from another global challenge of the COVID-19 pandemic and its attendant effects of distorted food prices and disruptions of the food supply chain. Informed by the analysis of the literature available on the Web of Science, Scopus, Google Scholar databases and grey literature from United Nations (UN) agencies, this study explores the challenges that emanated from the global problem of conflict and its implications on food security and the fight against hunger in Africa. The study found that the Russo-Ukrainian war exposed the lack of progress in Africa towards the fight to eliminate hunger. The consequences of the war have been aggravated by this lack of progress towards achieving zero hunger in many African countries. The war disrupted the global supply chain and threatened Africa with hunger as the continent experienced food shortages, food inflation and general commodity price volatility.

ЭКОНОМИЧЕСКИЙ ПОРТРЕТ КАЗАХСТАНА: НОВЫЕ ВЕХИ

Recent years have been marked by steady growth of the Kazakh economy, which is supported by the dynamic development of key sectors and the active introduction of new technologies. In 2024, the country continues to successfully adapt to the changing global economic environment, including commodity price volatility and global economic turmoil. The Kazakh economy has demonstrated steady positive dynamics, which is due to the stable growth of key industries and innovative development. Despite global economic challenges and fluctuations in commodity prices, the country continues to demonstrate adaptability and sustainability of economic growth in 2024. International financial institutions and analytical centers agree that the Kazakh economy will maintain moderate growth rates in 2024. This indicates a gradual recovery from global economic turmoil. An important factor contributing to such development is the diversification of the economy, aimed at reducing dependence on commodity exports and stimulating domestic consumption, infrastructure projects and innovation. The Kazakh government is actively working to improve the country's investment attractiveness, support small and medium-sized businesses and develop human capital. These measures are aimed at ensuring sustainable economic growth and improving the well-being of the population. This article provides an in-depth analysis of the current economic state of Kazakhstan, including the dynamics of GDP and GRP, as well as development prospects.

THE EVOLUTION AND FORECASTING OF SINO-KOREAN COMMODITY TRADE: A THEORETICAL EXPLORATION BASED ON GARCH AND MACHINE LEARNING MODELS

This paper examines the evolution of Sino-Korean commodity trade and explores the application of advanced econometric and machine learning models for forecasting commodity price volatility. With increasing interdependence between China and South Korea in trading key commodities such as crude oil, LNG, iron ore, and rare earth elements, accurate price forecasting has become crucial for managing economic risks and optimizing trade strategies. The study highlights the limitations of traditional econometric models like GARCH, which, while effective at capturing short-term volatility, struggle to account for the complex, nonlinear dynamics present in modern commodity markets. Machine learning models, including LSTM, random forests, and support vector machines, offer a more flexible and accurate approach by incorporating real-time data and adapting to market shifts. The combination of GARCH and machine learning in hybrid models further enhances forecasting accuracy. As both countries transition toward sustainable energy, the role of advanced forecasting tools will be pivotal in maintaining economic stability and fostering deeper trade cooperation. KEYWORDS：Sino-Korean commodity trade,GARCH model, Machine learning, Commodity price volatility, Crude oil forecasting, LNG and rare earth elements

The Impact of the Federal Reserve’s Interest Rate Hikes on U.S. Inflation Rate

The Federal Reserve’s interest rate hikes have a significant impact on domestic economic activities and monetary policies in the United States. Since March 2022, the Federal Reserve has raised interest rates multiple times to counter domestic inflationary pressures. In this article, data on the U.S. inflation rate and the federal benchmark interest rate from January 2010 to March 2024 were extracted, and an ARIMA model was used for analysis to predict how the U.S. inflation rate will develop under the condition of interest rate hikes. The study found that after the Federal Reserve raised interest rates, the inflation rate in the United States also rose. The reasons for this situation may be due to increased inflation expectations and external factors such as COVID-19. To mitigate risks brought by the rise in inflation, such as asset devaluation, commodity price volatility, and reduced investment returns, investors can adopt strategies like diversifying their investments, closely monitoring the Federal Reserve’s interest rate hikes, and avoiding high-leverage investments.

Comparative Analysis of Key Success Factors in S&OP: Focusing on Manufacturing and Retail Industry in South Korea

The global supply chain is facing unprecedented challenges due to pandemics, geopolitical tensions, volatile commodity prices, and rapid changes in consumer demand. The Sales and Operations Planning (S&OP) process can be an important solution by integrating supply and demand with business strategy and operational planning. However, previous studies have developed single frameworks or maturity models without considering industry-specific factors, overlooking the structural and operational characteristics of different industries that can have a significant impact on the approach and success factors of S&OP. Therefore, this study identifies and compares the key success factors of S&OP in the manufacturing and retail industries using the Analytic Hierarchy Process (AHP) technique. The results show clear differences in the prioritization of success factors between two industries. In the manufacturing industry, strategic support and leadership were evaluated as the most critical factor, while in the retail industry, operational processes were emphasized. In addition, the relative importance was concentrated on the top-ranked factor in the manufacturing industry, but the relative importance was distributed among several factors in the retail industry. This study provides valuable insights into the critical success factors for S&OP implementation in different industries and offers an understanding of how industry-specific characteristics influence the effectiveness of S&OP strategies.

Risk Assessment of Banks under Fed Rate Hikes

The global economy was hit after the start of the COVID-19 pandemic. Economic stagnation and high volatility in commodity prices also had a negative impact on global supply chains. The Federal Reserve, in an effort to prevent a repeat of the 2008 financial crisis, slow down inflation, increase the liquidity of money, prevent asset value bubbles, and further revive and stabilize the economy, adopted a policy of raising interest rates, gradually increasing the federal funds rate to between 5.25 and 5.5 per cent over a two-year period. This change, however, also led to many changes in the banking sector. Commercial banks are trying to find ways to avoid risks while maintaining their returns in the face of financial market changes. This paper analyzes the systemic risk of commercial banks through liquidity risk, credit risk and market risk with the cases of Silicon Valley Bank, Signature Bank and Credit Suisse Bank. Through the analysis, this paper finds that commercial banks suffer from systemic risk, often reflecting their own operational problems, which ultimately led to bankruptcy or acquisition of the end.

Commodity price volatility, institutions and economic growth: An empirical investigation

AbstractThis article investigates the role of institutional quality in transmitting effects of commodity price volatility to economic growth. To do so, we collect data on 107 primary commodity exporting countries in both developing and developed ones over the period 1976–2015. Our empirical approach is based on Solow growth model framework (Solow, R. M. (1956). The Quarterly Journal of Economics, 70(1), 65) and consists of estimating a dynamic panel model using the two‐step system GMM estimator. Our results show evidence that commodity price booms are associated with good economic performances that are unfortunately wiped out by the negative effects of price volatility in developing commodity‐dependent countries (CDCs). The main channel through which this volatility affects economic growth turns out to be through factor productivity. Finally, we have formally established that the negative effect of price volatility in CDCs is mainly due to the poor quality of institutions in these countries. These results suggest that it is important for commodity‐exporting economies, especially developing CDCs, to work on building strong economic and political institutions to guard against the risk of commodity price volatility.

Russia-Ukraine War and Price Volatility of Global Commodities: The Role of Public Sentiments

We analysed how public sentiments have affected global commodity market volatility during the Russia-Ukraine war. Using principal component analysis, we created a sentiments index from 30 carefully selected Google trends search keywords related to the war. We tested the predictability of the sentiments index against market volatility. Our results show that while public sentiments increase commodity market volatility, incorporating the sentiment index into our predictive model significantly improves its precision.

Estimation and Prediction of Commodity Returns Using Long Memory Volatility Models

Modelling the volatility of commodity prices and creating more reliable models for estimating and forecasting commodity price returns are crucial. The body of research on statistical models that can fully reflect the empirical characteristics of commodity price returns is lacking. The main aim of this research was to develop a modelling framework that could be used to accurately estimate and forecast commodity price returns by combining long memory models with heavy-tailed distributions. This study employed dual hybrid long-memory generalised autoregressive conditionally heteroscedasticity (GARCH) models with heavy-tailed innovations, namely, the Student-t distribution (StD), skewed-Student-t distribution (SStD), and the generalised error distribution (GED). Based on the smallest forecasting metrics values for mean absolute error (MAE) and mean squared error (MSE) values, the best performing LM-GARCH-type model for lithium is the ARFIMA (1, o, 1)-FIAPARCH (1, ξ, 1) with normal innovations. For tobacco, the best model is ARFIMA (1, o, 1)-FIGARCH (1, ξ, 1) with SStD innovations. The robust performing model for gold is the ARFIMA (1, o, 1)-FIGARCH (1, ξ, 1)-GED model. The best performing forecasting model for crude oil and cotton returns are the FIAPARCH 1,ξ, 1−SStD model and HYGARCH 1,ξ, 1−StD model, respectively. The results obtained from this study would be beneficial to those concerned with financial market modelling techniques, such as derivative pricing, risk management, asset allocation, and valuation.

Monetary policy and distributional effects of agricultural commodity price volatility in Sub-Saharan Africa

Monetary policy and distributional effects of agricultural commodity price volatility in Sub-Saharan Africa

Supply Chain Finance: How Do Supply Chain Strategies Perform in Mitigating Supply Chain Disruption and Commodity Price Volatility Risks?

Supply Chain Finance: How Do Supply Chain Strategies Perform in Mitigating Supply Chain Disruption and Commodity Price Volatility Risks?

Sentiment and energy price volatility: A nonlinear high frequency analysis

This study investigates the volatility dynamics of oil and gas prices in an environment characterized by post-coronavirus disease 2019 recovery, uncertainty, high inflation, and geopolitical tensions. Unlike previous studies, we examine a long-run series of high-frequency data on gas and oil prices from July 2007 to May 2022, which provides more than one million observations with which to analyze volatility. We compute realized volatility (RV) and decompose it into continuous volatility and jumps. We then investigate the relationship between uncertainty, investor sentiment, and RV, as well as its main components. Econometrically, we extend the heterogeneous autoregressive model of Corsi (2009) while considering not only disaggregate proxies for volatility (jumps and continuous volatility) and introducing uncertainty and heterogeneous investor sentiment, but also by allowing the model to include asymmetry, nonlinearity, and time variation according to the regime under consideration. Our results present three main findings. First, we find significant evidence of volatility decomposition, suggesting that both markets are characterized by significant jumps. Second, we show that trading volume, extra-financial news (uncertainty, investor sentiment), and jumps appear to drive commodity price volatility. Third, we find evidence of nonlinearity and threshold effects on energy price volatility. These findings are relevant for policymakers, regulators, investors, and portfolio managers, as they enable them to better characterize and forecast changes in commodity prices.

Forecasting the realized volatility of agricultural commodity prices: Does sentiment matter?

AbstractWe analyze the out‐of‐sample predictive power of sentiment for the realized volatility of agricultural commodity price returns. We use high‐frequency intra‐day data covering the period from 2009 to 2020 to estimate realized volatility. Our baseline forecasting model is a heterogeneous autoregressive (HAR) model, which we extend to include sentiment. We further enhance this model by incorporating various key realized moments such as leverage, realized skewness, realized kurtosis, realized upside (“good”) volatility, realized downside (“bad”) volatility, realized jumps, realized upside tail risk, and realized downside tail risk. In order to setup a forecasting model, we use (i) forward and backward stepwise predictor selection and (ii) a model‐based averaging algorithm. The forecasting models constructed through these algorithms outperform both the baseline HAR‐RV model and the HAR‐RV‐sentiment model. We conclude that, for the agricultural commodities studied in our research, realized moments play a more significant role in forecasting realized volatility compared to sentiment.

Environmental Risks and Agricultural Commodities

As natural disasters are expected to increase both in severity and frequency, their impact on agricultural commodities and the food supply chain is likely to rise in tandem. We use a unique data set of natural disasters that occurred globally between 1970 and 2015 and study their impact on the price and price volatility of eight globally traded agricultural commodities that account for more than 75% of the food supply in the world. Our results show that price and the price volatility of commodities are impacted around the occurrence of a natural disaster in producing countries. In addition, we find that the affected country’s production shares, the total damage caused by a natural disaster relative to the affected country’s GDP, and the demand pressure for the commodity have an impact on the magnitude of price and price volatility changes of a given commodity around the occurrence of a natural disaster.

Commodity price volatility and the psychological well‐being of farmers

AbstractWe examine the effects of income uncertainty on mental health in Vietnam. We assess this issue using volatility in the price of coffee, a key export commodity, that exposes small coffee farmers to income uncertainty. Using household panel data collected over 2016–2020, we find an increase in volatility of the international coffee price to be positively associated with psychological distress among coffee farmers. The magnitude is greater for men, and the findings are robust to several checks. These results are further substantiated by corresponding estimates for related health measures and self‐reported happiness. Channels include an increase in mental stress due to pessimistic expectations of future economic well‐being, increased cognitive load and alcohol consumption, and reduced social capital. The results highlight the psychological toll of living with income uncertainty and provide support for the provision of social safety nets that protect farmers from frequent commodity price fluctuations.

Efficient commodity price forecasting using long short-term memory model

<p>Predicting commodity prices, particularly food prices, is a significant concern for various stakeholders, especially in regions that are highly sensitive to commodity price volatility. Historically, many machine learning models like autoregressive integrated moving average (ARIMA) and support vector machine (SVM) have been suggested to overcome the forecasting task. These models struggle to capture the multifaceted and dynamic factors influencing these prices. Recently, deep learning approaches have demonstrated considerable promise in handling complex forecasting tasks. This paper presents a novel long short-term memory (LSTM) network-based model for commodity price forecasting. The model uses five essential commodities namely bread, meat, milk, oil, and petrol. The proposed model focuses on advanced feature engineering which involves moving averages, price volatility, and past prices. The results reveal that our model outperforms traditional methods as it achieves 0.14, 3.04%, and 98.2% for root mean square error (RMSE), mean absolute percentage error (MAPE), and R-squared (R<sup>2</sup>), respectively. In addition to the simplicity of the model, which consists of an LSTM single-cell architecture that reduced the training time to a few minutes instead of hours. This paper contributes to the economic literature on price prediction using advanced deep learning techniques as well as provides practical implications for managing commodity price instability globally.</p>

An Analysis of the Price Volatility of Commodities: Gold, Silver, Aluminum, Copper, and Zinc with Particular Reference to MCX

An Analysis of the Price Volatility of Commodities: Gold, Silver, Aluminum, Copper, and Zinc with Particular Reference to MCX