Metal Prices Research Articles

RETRACTED: Joshi et al. An Evolutionary Technique for Building Neural Network Models for Predicting Metal Prices. Mathematics 2023, 11, 1675

The journal and authors retract the article entitled “An Evolutionary Technique for Building Neural Network Models for Predicting Metal Prices” [...]

Volatility persistence in metal prices

This article deals with the analysis of volatility persistence in a group of metal prices, namely gold, silver, copper, platinum, aluminium, palladium, lead, zinc and tin, using monthly data from January 1994 to February 2023. Applying fractional integration techniques, the findings show that all series are highly persistent, although the prices for Gold and Silver display a limited mean reversion. The volatility was approximated by the absolute and squared returns and the results show that in the case of the annual difference returns, the series are persistent and the evidence of mean reversion is only observed for Gold and Silver. In the case of monthly differences, the hypothesis of short memory (d = 0) behavior cannot be rejected in all cases. For the absolute returns, the values are all positive, denoting a long memory ranging from 0.14 for Gold to 0.18 for Silver. For the squared returns, the values are slightly smaller but positive, ranging from 0.11 (Gold) to 0.16 (Aluminum and Palladium). The supply-side economic policy should be intensified in the case of the most volatile metals.

The energy transition: The behavior of renewable energy stock during the times of energy security uncertainty

The global energy sector is experiencing a transition towards renewable energy primarily driven by issues related to climate change and energy security. In this paper, we investigate the impact of uncertainties and potential drivers connected to energy security on the volatilities and returns of renewable energy stocks. Further, we examine how uncertainty and potential drivers connected to energy security affect the volatilities and returns of renewable energy stocks. By applying the MS-GARCH (1,1) and MS-GJR-GARCH (1,1) approach we calculate the Value at Risk (VaR) and Conditional Value-at Risk (CVaR). In addition, we estimate a fixed effects model to determine the impact of the uncertainty variables on the estimated conditional volatility and returns. Our findings indicate that economic policy uncertainty (EPU) positively impacts the returns of renewable stocks, attributing to an increased engagement towards a renewable transition. However, the prices of crucial green metals were found to have a negative impact on renewable stocks suggesting that the transition to renewable energy might impose implications regarding energy security if not managed correctly. The findings of this study have important implications for policymakers, market participants, and governmental agencies in devising a roadmap to promote the transition process to renewable energy sources.

Climate policy uncertainty and its relationship with precious metals price volatility: Comparative analysis pre and during COVID-19

Climate change and its associated challenges have become pressing global concerns. This has necessitated policymakers and governments to take action by implementing climate policies to mitigate its effects. However, the uncertainty surrounding climate policies has caused adverse effects in several areas, particularly in the financial markets. Likewise, the precious metals market has traditionally been considered a haven for investors in times of economic crisis. However, with the ongoing changes in climate policies, the previously stable prices of precious metals have encountered unexpected fluctuations. This state leads us to question how climate policy uncertainty impacts the volatility of precious metals. Thus, this study is designed to delve into the association between climate policy uncertainty and the volatile nature of precious metals prices, focusing on a comparative analysis before and during the COVID-19 pandemic. An advanced technique, i.e., GARCH-MIDAS, has been employed for data analysis. The results illustrate that climate policy uncertainty significantly affects the price volatility of precious metals. The results also indicate that climate policy uncertainty significantly impacted precious metals price volatility during the COVID-19 pandemic, suggesting that price volatility of metals escalates during a crisis. It has been revealed further that platinum and palladium are the foremost precious metals with high fluctuation series. The findings of this research contribute to a deeper understanding of the proposed relationship, providing significant information for long-term investment strategies and developing policy in light of changing climate policies.

Colloidal Plasmonic TiN Nanoparticles for Efficient Solar Seawater Desalination.

Transferring traditional plasmonic noble metal nanomaterials from the laboratory to industrial production has remained challenging due to the high price of noble metals. The development of cost-effective non-noble-metal alternatives with outstanding plasmonic properties has therefore become essential. Herein, we report on the gram-scale production of differently shaped TiN nanoparticles with strong plasmon-enabled broadband light absorption, including differently sized TiN nanospheres, nanobipyramids, and nanorod arrays. The TiN nanospheres and nanobipyramids are further coembedded in highly porous poly(vinyl alcohol) films to function as a photothermal material for solar seawater desalination. A seawater evaporation rate of 3.8 kg m-2 h-1 is achieved, which marks the record performance among all plasmonic solar seawater desalination systems reported so far. The removal percentage of phenol reaches 98.3%, which is attributed to the joint action of the excellent photocatalytic ability and the superhydrophilicity of the porous TiN-based composite film.

The connectedness of Energy Transition Metals

We assess the degree of connectedness among 16 metals that are critical for the production of clean energy technologies. These commodities are the constituents of the Energy Transition Metals (ETMs) price index maintained by the International Monetary Fund and comprise base, precious, and minor metals. We rely on Vector Autoregressive models and generalised forecast error variance decomposition to quantify spillovers among ETMs returns and volatilities. By calculating both static and dynamic measures of connectedness, we gain insight into the patterns of shock transmission between ETMs. Our static analysis reveals that base and precious metals are net shock transmitters, while minor and most battery metals are net receivers. By splitting the analysis into three groups, we find that almost half of the connectedness originates within each group, whereas the other half is due to cross-group spillovers. Moreover, we find that the system-wide connectedness of returns is positively correlated with proxies of economic activity, whereas volatility connectedness seems to be more related to global economic policy uncertainty.

Precious Metals and Oil Price Dynamics

The aim of this study is to examine dynamic relationship among the prices of four precious metals (gold, silver, platinum, and palladium), oil price, and the US dollar/euro exchange rate. Recent literature for precious metals (gold, silver, platinum, palladium) and oil depict a lot contradiction results of their nexus in the long and short runs. So that, there is still much to investigate and learn about their relationship between them and with the exchange rate. The monthly data span from January 1990 to October 2021 is utilized, the study applies the co-integration analysis, multivariate Granger causality test and Variance Decomposition (VDC) analysis. The result shows the evidence of a long-run equilibrium relationship but weak feedbacks in the short run. The precious metal markets respond significantly (but temporarily) to a shock in any of the prices of the other metal prices and the exchange rate. Furthermore, we discover some evidence of market overreactions in the palladium and platinum cases as well as in the oil market. The study implies whether there are overreactions and re-adjustments or not, investors may diversify at least a portion of the risk away by investing in precious metals, oil, and the euro.

Why did agriculture's share of Australian gross domestic product not decline for a century?

AbstractThe agricultural sector's share of gross domestic product (GDP) in growing economies typically declines but, for a century from the early 1850s, Australia's did not. Drawing on recent structural transformation literature, this paper seeks explanations for this unusual phenomenon, which is all the more striking because agriculture's share of employment continued to decline throughout and growth in manufacturing was being stimulated by tariff protection from imports. Several factors contributed, including a huge land frontier that took more than a century for settlers to explore, rapid declines in initially crippling domestic and ocean trade costs for farm products, the absence of a need to do any processing of the two main exports during that period (gold and wool) and innovations by farmers and via a strong public agricultural R&D system that contributed to farm labour productivity nearly doubling over those 10 decades. The ban on iron ore exports from 1938 and low export prices for fuels, minerals and metals during the two world wars and in the intervening decades also contributed.

Trajectories for Lithium‐Ion Battery Cost Production: Can Metal Prices Hamper the Deployment of Lithium‐Ion Batteries?

AbstractCost‐savings in lithium‐ion battery production are crucial for promoting widespread adoption of Battery Electric Vehicles and achieving cost‐parity with internal combustion engines. This study presents a comprehensive analysis of projected production costs for lithium‐ion batteries by 2030, focusing on essential metals. It explores the complex interplay of factors, including economies of scale, R&D innovations, market dynamics, and metal price trends. The findings highlight the significant role of R&D innovations and economies of scale in substantial cost reductions by 2030, with projected ranges of 21–28 % and 25–37 %, respectively. However, potential cost escalations due to elevated metal prices, particularly for nickel‐cobalt‐containing chemistries, are also cautioned. To address these challenges, the study proposes a strategic shift towards robust Lithium‐Iron‐Phosphate (LFP) chemistry to mitigate cost pressures and meet predefined cost targets. Moreover, by analyzing medium or low metal price trends, the study reveals the potential for significant cost savings, with exceptional scenarios demonstrating up to a remarkable 65 % reduction in costs. Given the broad range of cost likelihoods, the study underscores the importance of vertical integration and international cooperation in managing the essential metals supply chain securely. The research has profound implications for policymakers and industry decision‐makers, providing valuable insights into the lithium‐ion battery industry.

A novel deep-learning technique for forecasting oil price volatility using historical prices of five precious metals in context of green financing – A comparison of deep learning, machine learning, and statistical models

This study proposes a novel deep-learning convolution neural network (CNN) to forecast crude oil prices based on historical prices of five precious metals (Gold, Silver, Platinum, Palladium, and Rhodium) in context of green financing. The proposed deep learning CNN has three components: a convolution block called a group block, a novel convolutional neural network architecture called GroupNet, and a regression layer. The proposed model is tested against seven machine learning models and three traditional statistical models for predicting oil price volatility using the same independent variables (5 precious metals). A comparison of the deep learning model (our proposed model) with machine learning/deep learning models and statistical methods indicates that the proposed deep learning model has the highest prediction accuracy. A feature selection technique is also applied using the WEKA ML tool to improve the accuracy of the proposed model and existing machine learning and traditional statistical models. The findings indicate a non-linear correlation between oil price volatility and prices of precious metals. Moreover, statistical analysis indicates that deep learning can be used to predict oil price volatility with greater accuracy than machine learning and statistical methods while using precious metals as predictors. The results also indicate that machine learning models (Decision Tables and M5rules) can be used to predict oil price volatility with considerable accuracy. Moreover, the study proves that traditional statistical models can perform better than a few machine learning models (Lazy LWL and GPR).

A novel non-ferrous metal price hybrid forecasting model based on data preprocessing and error correction

Accurately forecasting the price of non-ferrous metals is of great significance for traders to avoid risks, enterprises to arrange production plans, and countries to formulate economic policies. In order to improve the forecasting accuracy of non-ferrous metal prices, this paper proposes a novel non-ferrous metal price hybrid forecasting model named IVWAIEE (IVMD-WPD-ARIMA-IELM-ECD). Firstly, the original price series is decomposed into several smoother IMFs using variational mode decomposition (VMD). Simultaneously, the improved sparrow search algorithm (IFASSA) is used to optimize the parameters of VMD to improve the adaptability of VMD. Secondly, wavelet packet decomposition (WPD) is used to decompose the residual sequence generated by VMD to further extract the information in the residual sequence. Then, the components generated by VMD and WPD are defined as high frequency components and low frequency components according to the zero-crossing rate. ARIMA is used to forecast the low frequency components with gentle fluctuations, and extreme learning machine optimized by IFASSA (IELM) is used to forecast the high frequency components with strong fluctuations. The forecasting results of each component are accumulated to obtain the initial forecasting results and error sequence of the non-ferrous metal price. Next, WPD is used to further decompose the error sequence, and the error subsequence is predicted by ARIMA and IELM to obtain the error prediction results. Finally, the error prediction results are used to correct the initial forecasting results, and the final forecasting results of non-ferrous metal prices are obtained. In order to verify the superiority of the proposed model, the copper, aluminum, and zinc futures prices of the London Metal Exchange (LME) are selected as empirical data to verify the model. The results show that the proposed IVWAIEE model has better prediction accuracy and robustness than other benchmark models. Its RMSE values in predicting copper, aluminum, and zinc futures prices are 0.2238, 0.1863, and 0.2137, respectively, and MAE values are 0.1696, 0.1171, and 0.1644, respectively, which are lower than those of other benchmark models; The proposed model not only enriches the application of secondary decomposition and error correction in the field of non-ferrous metal price forecasting, but also solves the problems of insufficient adaptability and underutilization of residual sequence in the traditional variational mode decomposition method; The research results of this paper can provide scientific and effective guidance for the investment, production, and decision-making of non-ferrous metal stakeholders.

Current status of battery recycling and technology

In recent years, battery recycling has become one of the hot environmental issues in China. This paper analysis the current situation of battery recycling and the methods of recycling, and analyzes the effective means of recycling batteries under the existing conditions. The results show that the current situation of battery recycling in China is worrying, with no complete recycling industry chain and low recycling efficiency. And as the demand for batteries increases and the price of metals rises, the urgency of battery recycling is further increased. From the analysis of existing methods, pyrometallurgy is defective in many aspects and the environmental life cycle is not complete. The cost of electrolytic recycling method, although high recovery efficiency and wide range, requires adequate technology and high cost. Bioleaching method is highly efficient, low cost and produces almost no pollution. Although the technology is not mature enough and is still in the laboratory stage, the bioleaching method has a good future. This paper looks forward to exploring more appropriate recycling methods to address the current situation of battery recycling difficules in China and discusses the future prospects of these methods.

RAW MATERIALS PRICE VOLATILITY AND ECONOMIC IMPACTS ON THE JAMNAGAR BRASS PARTS INDUSTRY: A STUDY ON MICRO AND SMALL-SCALE UNITS

Most of the production work is carried out by micro and small-scale units in the Jamnagar Brass Parts industry. Brass scrap is most often used in this industry. Brass scrap prices are based on brass metal prices, and Brass is composed of a mixture of copper, zinc, and lead metals, in addition, copper holds the highest position among other related metals. Therefore, the highest percentage of copper can be found in comparison to other metals. When it comes to brass scrap, prices mostly depend on copper prices. Copper is considered a globle metal today, with developed countries dominating both its production and consumption. In this regard, over the past few years, copper prices have experienced significant instability. Consequently, brass prices also exhibit instability. In this research paper focuses on the Jamnagar Brass Parts industry and examines the economic impacts and challenges arising from the volatility in prices of raw materials, such as brass scrap, on both foundry units and manufacturing units. Furthermore, these small-scale units find themselves in an uncertain position as a result of the volatile fluctuations in raw material prices. Consequently, these units react or respond to the price fluctuations of raw materials like brass scrap. This paper also tries to address this issue.

The time-varying impact of geopolitical risk on natural resource prices: The post-COVID era evidence

The geopolitical risk plays a pivotal role in affecting commodity price variations, especially natural resource commodity prices. Geopolitical risk is sensitive to political environmental changes and geopolitical events, including COVID-19 and the Russia-Ukraine conflict. On this basis, our study attempts to scrutinize the time-varying characteristics of geopolitical risk impact on natural resource commodity prices against recent geopolitical events. By applying the data from natural resource commodity markets, we unravel the fact that U.S. geopolitical risk has a stronger impact on energy market prices, and this impact becomes even stronger after the Russia-Ukraine conflict. Finally, we unfold that the downside risk comovement between metal prices and China geopolitical risk is stronger. Further, the downside risk comovement between energy prices and United States geopolitical risk is more profound. Our paper could deliver implications for policymakers and investors. We notify that the establishment of an early warning system for geopolitical events can be useful to policymakers. Our result further motivates investors to rebalance their portfolios’ risk exposure in a time-varying way, especially after geopolitical events.

The Impact of U.S. Monetary Policy on Metal Futures Prices: An Arch Model Analysis

The article empirically examines the impact of U.S. monetary policy on futures prices of non-ferrous metals (including copper, alum, zinc, lead, tin, nickel, and overall non-ferrous metal price index), using the data since the 1980s. The regression analysis with the arch model indicates that the rise of federal funds rates has some extent explaining power on the decrease of metal futures prices, as shown by the significant negative relationship between them. Intuitively, an increase in interest rates reduces currency liquidity and suppresses the demand for commodities such as non-ferrous metals, thus the decrease of their prices. Besides, this research also identifies a significant correlation between futures prices and M2, with a positive correlation between the means of their variables and a negative correlation between the variances of the variables. The metal spot price reasonably predicts its corresponding future prices, as those two prices are found significantly positively correlated with coefficients around 0.9-1.1.

Agrarian change through speculation: Rural elites as land brokers for mining in Colombia

AbstractThis paper studies the connections between the expansion of mining capital, speculative forms of land grabbing and agrarian transformation. It is argued that in periods of commodity boom, the landowning rural elite benefits from mining through speculative land deals with mining companies. They act as ‘land brokers’ for the mining firms, helping them to overcome a significant barrier to land accumulation through the de facto abolition of landed property. The analysis is based on a qualitative case study on the expansion of coal mining in central Cesar in northern Colombia. To develop my arguments, I refer to the concept of accumulation by dispossession as defined by Michael Levien, and historical materialist approaches on rent, and speculative land dispossession. In addition, I use concepts developed for studying coercive land grabbing and agrarian elite participation in armed conflicts to analyse the mechanisms applied to (coercively) acquire rights to land. It is concluded that with high global prices for minerals, metals and fossil fuels, the expansion of mining in the countryside fosters a process of agrarian change through land speculation that is articulated in a reconcentration of landed property, a re‐strengthening of the rural landowning elite and the dissolution of peasant agriculture.

Forecasting base metal prices with exchange rate expectations

AbstractIn this paper, we show that survey‐based expectations of the Chilean exchange rate have the ability to predict the returns of the six primary non‐ferrous metals: aluminum, copper, lead, nickel, tin, and zinc. Predictability is also found for returns of the London Metal Exchange Index. Previous studies have shown that the Chilean exchange rate has the ability to predict copper returns, a world commodity index and base metal prices. Nevertheless, our results indicate that expectations about the Chilean peso have stronger predictive ability relative to the first lag of the Chilean currency. This is shown both in‐sample and out‐of‐sample. By focusing on expectations of a commodity currency, and not on the currency itself, our paper provides indirect but new and strong evidence of the ability that commodity currencies have to forecast commodity prices.

Critical metals in uncertainty: How Russia-Ukraine conflict drives their prices?

Critical minerals bring new challenges to energy security in the transition to clean energy, so their supply and prices are a spectacle of global uncertainties. This research investigates the impact of the Russia-Ukraine war on critical metal prices and the extent to which it affects them. The study will also highlight the price trends in the absence of this geopolitical event. The results outline that the Russia-Ukraine conflict drives critical metals prices. The outcomes show rapid divergence from counterfactual predictions, and the critical metals prices are consistently higher than expected without conflict. The curves suggest a reconvening pattern in metals prices around April 2022. The point-wise causal effect displays an estimate of the critical metal prices increased following the conflict. In relative terms, the prices of critical metals, namely cobalt, nickel, lithium, copper, and aluminum, experienced increases of 2%, 36%, 14.97%, 3%, and less than 1%, respectively. In comparison, the price of lead exhibited a decrease of 8%. The highest increase was observed in nickel, followed by lithium, indicating that these two metals are more responsive to such variations. However, it is noteworthy that prices tend to stabilize and return to pre-event levels from May to September, depending on the respective metal's sensitivity. Moreover, outcomes emphasize that global dependencies, lack of resources and investments, and intentional conflict of crucial minerals exporting nations lead to the conflagration.

Economic policy uncertainty, geopolitical risks, and the heterogeneity of commodity price fluctuations in China ——an empirical study based on TVP-SV-VAR model

The combination of geopolitical conflicts and economic shocks could cause severe volatility in the global commodity market. Based on the monthly data from June 2006 to March 2023, this paper employs the TVP-SV-VAR model to investigate the time-varying impacts of economic policy uncertainty (EPU) and China's geopolitical risk (GPRC) on China's commodities prices (CCPI). The findings reveal that: (1) CCPI can be collectively influenced by EPU and GPRC shocks, with time-varying impulse responses showing notable fluctuations with alternating positive and negative values. The response of CCPI to EPU is more intense than that to GPRC shock after 2019; (2) Under the financial crisis, GPRC has a significantly positive impact on CCPI, while positive responses to global EPU and China's EPU are more pronounced under the Russia-Ukraine conflict. Under the Sino-US trade friction, the responses of CCPI to various shocks are mostly positive, with a continuing positive impact of EPU; (3) The responses of energy prices, agricultural product prices, and non-ferrous metal prices align with that of overall commodity prices. However, under the Russia-Ukraine conflict, the positive impulse responses of non-ferrous metal prices to EPU and GRPC shocks are persistent.

Causality inference among base metal, rare metal and precious metal markets

The relationship among metal markets has become closer and more complicated. It is necessary to study the causal relationship among metal markets, and then identify the dominators and receivers in the transmission process of metal price fluctuations, which can facilitate product pricing and avoid risk. This paper aims to reveal the complex interaction structure and its time-varying characteristics existing among base metal, rare metal and precious metal markets. The results show that the causality among metal futures is mainly positive causality, and the strongest influencer and receiver is copper. The dark causality is first unveiled in this paper with the main influencer is nickel and the main receiver is cobalt. From the dynamic perspective, macroeconomic factors, political events and disputes and other factors lead to significant fluctuations of causality among metal markets, even cause clear mutations appear in several metal causal relationship. These findings can provide valuable reference for metal commodity pricing and decision making.