Copper Prices Research Articles

The Response of Housing Construction to a Copper Price Shock in Chile (2009–2020)

The copper price is a leading indicator of real estate activity. Price increases are statistically related to increasing numbers of applications for residential building permits. However, this reciprocity is not instantaneous as permit numbers lag price rises by 9 to 10 months. This dynamic is implicit in various transmission channels: from the first effects on investment plans and demand for durable goods due to better expectations from investors and consumers to the real impact of higher copper revenues on the economy’s aggregate production and demand (multiplier or second-round effect). In this paper, we proposed the impulse-response functions of a vector autoregressive model to capture the dynamic between the copper price and house building permits. Therefore, it would be expected that the recent copper price increase will boost construction and real estate activity. The effects could materialize this year and extend into early 2022.

Recovery of Copper and Magnetite from Copper Slag Using Concentrated Solar Power (CSP)

On the one hand, copper slag is nowadays a waste in copper pyrometallurgy despite the significant quantities of iron (>40 wt. %) and copper (1 to 2 wt. %). On the other hand, solar energy, when properly concentrated, offers great potential in high-temperature processes. Therefore, concentrated solar power (CSP) could be used in the treatment of copper slag to transform fayalite into magnetite and copper sulfides and oxides into copper nodules. This is the objective of this paper. The results show that fayalite was partially decomposed into magnetite and silica. Moreover, copper nodules (65–85 wt. % Cu) were identified in the treated samples, while the initial slag, analyzed by X-ray diffraction, X-ray fluorescence, and SEM-EDX, did not show the presence of metallic copper. Finally, the treated copper slag was crushed and grinded down to 40 μm, and two fractions were obtained by magnetic separation. The magnetic fraction (85%) was mainly comprised of magnetite, while the non-magnetic fraction (15%) had 5–10 wt. % Cu. Considering the experimental results, 7.5–18 kg Cu/t slag might be recovered from the slag. A preliminary economic analysis, considering the current copper price, indicates that only the recovery of copper could represent a significant economic benefit (>30 €/t slag). Therefore, CSP might be a potential candidate for the treatment of copper slag to recover copper and iron.

Proposing two novel hybrid intelligence models for forecasting copper price based on extreme learning machine and meta-heuristic algorithms

The focus of this study aims at developing two novel hybrid intelligence models for forecasting copper prices in the future with high accuracy based on the extreme learning machine (ELM) and two meta-heuristic algorithms (i.e., particle swarm optimization (PSO) and genetic algorithm (GA)), named as PSO-ELM and GA-ELM models. Accordingly, the time series datasets of the copper price for thirty years were collected based on the influencing parameters, such as crude oil, iron ore, gold, silver, and natural gas prices. Furthermore, the exchange rate of the four largest countries in copper-producing, including Chile (USD/CLP), China (USD/CNY), Peru (USD/PEN), and Australia (USD/AUD), were also considered to evaluate the copper prices. The GA and PSO algorithms then optimized the weights and biases of the ELM model to reduce the error of the ELM model for forecasting copper price. The traditional ELM model (without optimization), and artificial neural networks (ANN) were also developed as the comparative models for resulting in convincing experimental results in the proposed PSO-ELM and GA-ELM models. The results indicated that the proposed hybrid PSO-ELM and GA-ELM models could forecast copper price with higher accuracy and reliability over the traditional ELM and ANN models. Of those, the PSO-ELM yielded the most dominant accuracy with a root-mean-squared error (RMSE) of 304.943, mean absolute error (MAE) of 241.946, mean absolute percentage error (MAPE) of 0.037, and mean absolute scaled error (MASE) of 0.933. The t-test and Wilcoxon test also demonstrated the statistical significance of the proposed models and the best 95% confident interval of the PSO-ELM model with the range of $177.046 to $67.054 with p-value = 2.589e-05. Whereas, the GA-ELM model provided the forecasted copper price higher $137.233 than the actual copper price, and the 95% confidence interval is from $189.672 to $84.793 with p-value = 1.027e-06.

Forecasting monthly copper price: A comparative study of various machine learning-based methods

Copper is one of the valuable natural resources, and it was widely used in many different industries. The complicated fluctuations of copper prices can significantly affect other industries. Therefore, this study aims to develop and propose several forecast models for forecasting monthly copper prices in the future based on various algorithms in machine learning, including multi-layer perceptron (MLP) neural network, k-nearest neighbors (KNN), support vector machine (SVM), gradient boosting tree (GBT), and random forest (RF). The monthly copper price dataset from January 1990 to December 2019 was collected for this aim based on other metals and natural gas prices. In addition, the influence of currency exchange rates of the countries that have the largest copper production around the world was also taken into account and used as input variables for forecasting copper price. The different set of predictors (t, t-1, t-2, t-3, t-4. t-5) were considered to forecast monthly copper prices based on the mentioned machine learning techniques. The results revealed that the currency exchange rates of the countries that have the most abundant copper production around the world have a significant effect on the volatility of monthly copper prices in the world, and they should be used to forecast monthly copper prices in the future. A comprehensive comparison of various machine learning techniques shows that MLP neural network (with deep learning techniques) is the best method for forecasting monthly copper price with an MAE of 228.617 and RMSE of 287.539. Whereas, the other models, such as SVM, RF, KNN, and GBT, provided higher errors with an MAE in the range of 308.691–453.147, RMSE in the range of 393.599–552.208. In this sense, MLP neural network can be used as a reliable tool to forecast copper prices in the future.

Manhole intrusion detection system with notification stages

Appropriate design of intrusion detection systems is extremely important to safeguard critical and valuable infrastructure. Telecommunications companies (TELCOs) and electric power utilities are experiencing an increased rate of rampant underground copper cable theft and vandalism. This paper addresses the important subject of combating the physical intrusion of critical infrastructure by proposing a low-cost, effective and unique manhole intrusion detection system. Manholes are openings to confined underground spaces that are used to access critical underground infrastructure and utilities such as sewer systems, electricity and telecommunication networks for maintenance and inspection. Critical infrastructure protection is very essential to avoid losses and incapacitations that would have a debilitating effect on economic activities, security, public health and safety, and so on. The increasing rate of rampant copper theft and vandalism could be attributed to the increased demand and high prices for copper on the black market. Critical infrastructure sectors such as TELCOs, electricity supply, water and rail transport are utilizing intrusion detection systems that only trigger an alarm when the critical infrastructure has already been vandalized or stolen. In this paper, we propose a low-cost and unique manhole intrusion detection system with notification stages devoted to safeguard critical infrastructure. Simulations of the system were performed before hardware implementation. The proposed system utilizes an Arduino Uno microcontroller and multiple sensors to trigger the intrusion stages early before the copper cables are vandalized. Due to the inclusion of three different additional sensors, the proposed system has an advantage of timely or early-stage intrusion detection. The response time is improved since the first alert is sent early before the actual cutting of the cable. The GSM messaging system is utilized as the alert mechanism during the intrusion stages. Notifications are also displayed on a local LCD.

Efficient separation of zinc from zinc containing copper sulfide concentrate by chemical leaching

As the most abundant copper containing resource and zinc containing resource, chalcopyrite and sphalerite/marmatite commonly coexist as Cu-Zn mixed ores in deposits. However, it is difficult to completely separate sphalerite and chalcopyrite by flotation, thus resulting in the existence of zinc impurity in copper concentrate. Sphalerite/marmatite existed in copper sulfide concentrate as impurity may lead to severe damage of the smelting equipment, and cause the waste of copper and Zn resources, it will also decrease of the sale price of copper concentrates. Therefore, the deep separation of zinc from zinc bearing copper sulfide concentrate is of great significance. In this work, selective chemical leaching was developed to efficiently remove zinc from zinc containing copper sulfide concentrate. Under the optimal condition (i.e., sulfuric acid concentration exceed 100 g/L, temperature of 80 °C, pulp density of 10%, leaching time of 48 h), over 85% Zn was extracted into the leaching solution together with only about 10% Cu and Fe, according to the leaching experiment. Leaching slurry had good solid-liquid separation characteristics, and zinc can be further effectively recovered from the leaching solution. According to X-ray diffraction (XRD) and scanning electron microscope/energy dispersive spectrometer (SEM/EDS) analysis, chalcopyrite was the main mineralogical phase in the residues, which can be regarded as high quality copper concentrate for metallurgy. Accordingly, a new process for deep and efficient separation of Cu-Zn mixed ores has been proposed.

The brassware industry and the salvage campaigns of wartime colonial Korea (1937-1945)

The collective memory inscribed in Korean history books recalls when the Japanese colonial state requisitioned brassware from Korean households during World War II. This study explores the complex mechanism behind these campaigns. Copper was a scarce commodity in the Japanese empire. The colonial brassware industry expanded but struggled due to fluctuating copper prices before the war. To overcome the reluctance of Koreans to part with their brassware, the colonial state had to coordinate various actors across multiple organisations, provide ceramic replacements, and establish a system of financial payments. The Japanese also ultimately created a ‘brass bureaucracy’ capable of carrying out the sensitive task of removing copper from Korean households. The Korean case reminds us that institutional frameworks are necessary for wartime salvage. Finally, brassware collections also resulted in fundamental changes in Korean society and everyday material culture that require careful analysis. (141 words)

The economic aspect of batik stamp made of can waste

The traditional batik stamp (canting cap) is relatively expensive tool, made of copper. The lack and high price of copper made it difficult for batik stamp crafter to market their products, and made alternative materials from wood and paper/cardboard. The weakness of wood and paper stamp is that the resulting rough motifs and not long lasting. The use of used can is environmentally friendly, assure sustainability by recycling waste of cans which causes environmental problems. This study departs from research on the innovation of Cantik batik stamp made of biscuit can waste, focusing on the economic aspect of the innovations. This analysis was important to see the advantages and or weaknesses of the can stamp, as a basis for applying the research product in the batik stamp rafter community. Descriptive research was conducted for cost calculation, analysis of selling power, and service life. The cost of can batik stamp found cheaper than copper stamp, since the materials is low cost, though 21% copper content was still needed for isen-isen, ganjelan, and bajelan part. However the labor processing cost was equal. The processing time efficiency was lower, while level of difficulty in processing was higher, because cans are harder and more difficult to shape and cut compare with copper. The durability of the can batik stamp was lower than copper, because can is easily oxidized and corrosive, so it will be more difficult to maintain. The risk of rust can be reduced by processing the final stamp in hot gondorukem (resina colophonium). For long lasting product, the can stamp has to storage in a dry and low humidity place, in hung position.

Copper tailings reprocessing

Abstract in EnglishCopper is widely used in the modern world. An excellent conductor of electricity, it is used in the electrical industry, in the construction industry because of its good corrosion resistance, and in the manufacture of heat exchangers in heating and cooling systems owing to its excellent thermal conductivity. Copper production has increased throughout the twentieth century, and this trend has continued over the last twenty years. The demand for copper is expected to increase significantly by the year 2030. Owing to the high prices of this metal and the lack of deposits, part of the demand can be met by extraction from copper-bearing tailings. In the past, owing to the lower level of technological development and lower copper prices, materials comparable in copper content to the copper ores mined today have ended up in tailings. Since these are already processed materials, the costs of mining, crushing and milling are largely eliminated, making them promising raw materials. The article presents the technological possibilities of reprocessing and also estimates the amount of copper that could be obtained worldwide in this way.

What Has Driven the U.S. Monthly Oil Production Since 2009? Empirical Results from Two Modeling Approaches

From the early 1970s to the Global Financial Crisis of 2007–09, U.S. crude oil production followed a declining trend. After the Global Financial Crisis, U.S. crude oil production increased rapidly. This paper addresses the important question “what economic factors have driven U.S. crude oil production since the Global Financial Crisis?”. We propose that factors such as: the price of oil, the one period lagged price of oil, the price of copper, the crude oil price volatility, the Trade Weighted U.S. Dollar Index, and the high yield index spread, are important explanatory variables. Using two modeling approaches, namely, multiple regression, and the random tree methodology, we conclude that the one month lagged price of oil is the most significant explanatory variable, among all considered, for the upward trend of U.S. oil production from 2009 to early 2020.

Economic feasibility of the co-production of indium from zinc sulphide using bioleaching extraction in Germany

Bioleaching has been a well-established technique for the extraction of various metals since the 1990s. In particular, leaching in continuously stirred tank reactors (CSTR) facilitates the rapid and environmentally friendly extraction of different metals. This work investigates the economic feasibility of this technology when carried out using a suitable process and sulphide concentrate from a mining operation in the Erzgebirge region of Germany. The process is based on assumptions and tests are pending. The focus is on recovering zinc, copper and, in particular, indium from zinc sulphide. Based on the defined process characteristics of the bioleaching step as well as down- and upstream processes, investment costs and operating costs of the necessary plant structure were determined. After determining the potential profits based on given metal concentrations and current market prices of zinc, copper and indium, a net present value (NPV) and the corresponding internal rate of return (IRR) were calculated to ensure comparability with other investments. Both values are positive, showing that an investment in the proposed bioleaching plant pays off even with low process volumes.

Pass-through of commodity price to Mongolian stock price: Symmetric or asymmetric?

Mongolia is a natural resource-rich country, so that its stock market is highly dependent on price fluctuations of commodities, particularly copper and coal. One crucial problem is that the responses of the stock market performance to a shock of the commodity price can be nonlinear or asymmetric. This study addresses such asymmetric linkages by applying nonlinear autoregressive distributed lags (NARDL) models during the period from January 2007 to December 2018. The results show clear evidence of asymmetric long-run relationships between stock price and the commodity price, supporting that different behaviors of stock price in response to positive and negative shocks on the commodity price. Specifically, our analysis presents a positive relationship between copper price and stock price in the case of a positive shock on copper price, but no clear relationship in the case of a negative shock on copper price. The results also reveal a positive relationship between coal price and stock price in the case of a negative shock on coal price, but no clear relationship in the case of a positive shock on coal price. The Mongolian stock market is favorably sensitive to an increase in copper price but vulnerable to a decline in coal price. Possible explanations behind the results are also discussed.

Sub-national economic effects of the resources sector in Chile

ABSTRACT The significant contributions of resource sectors to national economies are well known; however, information on sub-national impacts is much scarcer. Using Chile as a case study, this paper investigates how disturbances in the price of copper impact the economies where this mineral is produced. The proposed method combines estimates of long-term copper prices with a general equilibrium model to simulate the differential effects of copper price variations. The results suggest that price variations affect the design and implementation of public and private policies, at economic and business levels.

Using conditional asymmetry to predict commodity futures prices

Despite decades of studies, there is still no consensus on what type of serial dependence, if any, might be present in risky asset returns. This manuscript provides an empirical study of the prices of energy commodities, gold and copper in the futures markets and demonstrates that, for these assets, the level of asymmetry of asset returns varies through time and can be forecast using past returns. A regime switching model is used to construct a managed futures trading strategy that provides returns that are statistically significant. It is also demonstrated how such model can be used to make probabilistic predictions of commodity prices in futures markets, which can be used to drive value-at-risk and potential future exposure metrics or guide dynamic hedging strategies of commodity price risk.

Primary crusher site selection in open pit mines: Case study in Sungun copper mine

Facility site selection, (such as Primary or in pit Crusher location) is one of the most important steps in mine design process that has a significant impact on economy of the mining operation. This study covers an attempt to find the optimal location for primary crusher in order to support the expansion of Sungun copper mine, which is the second largest open pit mine in Iran. Recent exploration drilling has led to a considerable increase in the ore reserve. Therefore, Sungun started the studies followed with an expansion plan with the goal to increase the capacity from 14 to 28 Mt/Year within the next 8 years. In this paper, 14 effective parameters for finding the best location for a new crusher in five possible options were considered. The options were evaluated based on weighted linear combination method. The results show that the location in South East part of the open pit is the best option for locating of the new crusher. Finally, based on the economic parameters and copper price forecasts, pit optimization and sensitive analysis are applied. Results show that in the worst case, just 0.17 % of ore reserve is affected by the selected area and the minable reserve can cover 78 years of operation.

Network approach to the dynamic transformation characteristics of the joint impacts of gold and oil on copper

Previous studies often established econometric models to investigate the long-term relationship between the copper price and other commodity prices, but these models ignored the dynamic transformation characteristics of these relationships. This paper examines the dynamic transformation characteristics of the joint impacts of gold and oil on the copper log-return. We employed the joint impact transformation network based on the multiple regression model and the sliding time window approach. The results indicate that ‘NN’ (gold and oil both have no impact on the copper log-return), ‘N+Y’ (gold has no impact on the copper log-return and oil has a positive impact on the copper log-return) and ‘+YN’ (gold has a positive impact on the copper log-return and oil has no impact on the copper log-return) are the three most common joint impact modes over the entire time. ‘-Y+Y’ (gold has a negative impact on the copper log-return and oil has a positive impact on the copper log-return) and ‘-Y-Y’ (gold and oil both have a negative impact on the copper log-return) are the two joint impact modes that may emerge in the deepest period of economic turmoil. The joint impact modes have a tendency to transform to the modes transformed into them in a period when the economy is relatively stable or fluctuating due to financial factors. However, this trend becomes less evident when the economic fluctuations are subject to national policies. Our research not only provides a process orientation to explore the characteristics of the dynamic transformation of the relationship between commodity prices but also offers important implications for stakeholders to make decisions.

Transfer pricing: The Glencore appeal. To hedge or not to hedge? That is the question

The Full Federal Court decision in the Glencore Case raises very important questions in relation to core concepts underlying Australia’s previous and current transfer pricing rules. The case involves the pricing structure that an Australian mining company used for the copper concentrate it sold to its Swiss parent, the largest global trader in copper concentrate. Broadly speaking, the price for copper concentrate is worked out by deducting selecting a copper reference price from a listed metals exchange, like the London Metals Exchange, and deducting an allowance for treatment and copper refining costs (TCRCs). This was the main issue in the case. There were two options realistically available to the taxpayer: the use of market benchmarks that took account of both the prices set by the major mills and those set by the spot market; and, another method known as price sharing where the TCRCs are set at a fixed percentage of the copper reference price, which had the effect of protecting the seller from the risk that copper prices and TCRCs may trend in different directions. In February 2007 when the taxpayer switched to a price sharing formula that set the TCRC rate at 23% of the copper reference price, the benchmark rate set by the Japanese mills was much lower, and the TCRC rate established for the purposes of the mining company’s budget, which was approved by its parent around August to September 2006, was 6.5% of the copper reference price. The Appeal Court accepted the taxpayer’s argument that the 23% was an arm’s length rate because there were contracts between independent parties that used price sharing, and 23% was the midpoint of the rates in those cases, and because the taxpayer’s expert witness testified that for such a high cost mine price sharing was “a conservative and reasonable approach to minimising risk”. The Court did not consider how independent parties dealing at arm’s length (or wholly independently) with each other would have gone about deciding whether or not to hedge the risk, or the comparative benefits of market benchmark pricing relative to price sharing. A question therefore arises as to whether the Appeal Court correctly applied the statutory tests, a matter that would benefit from a High Court review.

The lead–lag relationship between spot and futures prices: Empirical evidence from the Indian commodity market

This paper examines the relationship between spot and futures prices in the Indian commodity market for the period 2009 to 2020. The ARDL bounds-testing technique is used to explore the long-run relationship between these two prices. A vector error correction model is then used to reveal the nature of Granger causality between the two. Six commodities, namely aluminum, copper, crude oil, gold, nickel, and silver, and three indices, namely agricultural, livestock, and precious metals, are used for this empirical process. The results indicate a long-run equilibrium relationship between the spot and futures prices of these commodities. The causality analysis reveals unidirectional causality in the long run from the spot to the futures price for aluminum and copper, and both bidirectional and unidirectional causality in the short run between the two prices for aluminum, copper, gold, and silver.

Multi-scale comovement of the dynamic correlations between copper futures and spot prices

Correlations between copper futures and spot prices are dynamic and show various fluctuation characteristics over different time horizons. This creates a challenge for industrial policy, product pricing strategy, and portfolio management. Investigating the multiscale comovement of the dynamic correlations between copper futures and spot prices can provide a useful periodicity-based reference for a copper price adjustment strategy and portfolio management. This study uses grey correlation analysis and wavelet analysis to study the comovement characteristics of copper futures-spot price correlations on a scale by scale basis. The Shanghai Metal Exchange copper spot prices and copper futures prices in three main copper markets (London, New York, and Shanghai) are used as sample data. Our main findings are as follows. First, the dynamic correlation of copper futures and spot prices vary at different scales. Second, the fluctuation of copper future-spot price correlations is obviously strong in the daily, quarterly, and annual periodicity. For policymakers and manufacturers, their daily, quarterly, and annual copper price adjustment strategies should reduce the reference weights of New York, London, and Shanghai, respectively, to avoid the risk of a single reference. For investors, their daily, quarterly, and annual portfolio and arbitrage strategies should focus on the leading futures price of the corresponding period to enact precise market timing. Moreover, Shanghai futures can be a haven in the short and medium terms, and New York and London futures can be a haven in the long run. Third, copper futures-spot price correlations show time asynchronism from semi-monthly to annual periodicity, providing a useful periodicity-based reference for pricing adjustment strategy and cross-market arbitrage.

Empirical analysis of Chile's copper boom and the Dutch Disease through causality and cointegration tests

Over the period 1990–2018, Chile has experienced the great copper boom manifested in an initial phase of the strong expansion of the productive base, followed by a period of high copper prices derived from the supercycle. Over this period, Chile has achieved great social progress in poverty and wealth indicators that have allowed it to decouple its progress from that of its peers in the region. However, it is valid to ask whether, as a consequence of the mining boom, Chile has suffered from an industrial weakening and other negative effects as a result of the Dutch Disease (DD), jeopardizing the country's long-term economic progress. This paper proposes a methodology to evaluate whether the Chilean copper boom induced the DD symptoms in its economy through Granger-causality, cointegration, and impulse-response function over a vast set of socioeconomic variables. According to the authors' knowledge, the approach implemented allowed for the broadest empirical DD evaluation in the Chilean economy taking into account four crucial aspects: i) the political and economic regime established and consolidated along the 1990–2018 period; ii) the full Chilean copper industry expansion cycle; iii) the complete supercycle of the commodity prices; iv) data availability. The results suggest that the copper mining industry has not only allowed the country to enjoy an income surplus but actually levered the country's development. The proven success over the last three decades of Chile's mining development and windfall management should be a strong argument in favor of the model continuity and as a valid benchmark for macroeconomic management purposes by other countries in similar macroeconomic and idiosyncratic contexts.