Copper Prices Research Articles

Forecasting Copper Prices Using Deep Learning: Implications for Energy Sector Economies

Energy is a foundational element of the modern industrial economy. Prices of metals play a crucial role in energy sectors’ revenue evaluations, making them the cornerstone of effective payment management employed by resource policymakers. Copper is one of the most important industrial metals, and plays a vital role in various aspects of today’s economies. Copper is strongly associated with many industries, such as electrical wiring, construction, and equipment manufacturing; therefore, the price of copper has become a significant impact factor on the performance of related energy companies and economies. The accurate prediction of copper prices holds particular significance for market participants and policymakers. This study carried out research to address the gap in copper price forecasting using a one-dimensional convolutional neural network (1D-CNN). The proposed method was implemented and tested using extensive data spanning from November 1991 to May 2023. To assess the performance of the CNN model, standard evaluation metrics, such as the R-value, mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE), were employed. For the prediction of global copper prices, the proposed artificial intelligence algorithm demonstrated high accuracy. Lastly, future global copper prices were predicted up to 2027 by the CNN and compared with forecasts published by the International Monetary Fund and the International Society of Automation. The results show the exceptional performance of the CNN, establishing it as a reliable tool for monitoring copper prices and predicting global copper price volatilities near reality, and as carrying significant implications for policymakers and governments in shaping energy policies and ensuring equitable implementation of energy strategies.

Brown seaweeds fishery and copper mining production: Two distant economic industries connected by socioecological impacts in Northern Chile

Northern Chile is the scenario of two main activities from a social and economic point of view the: brown seaweeds fishery and cooper mining industry. Chilean brown seaweeds of economic importance, commonly known as kelps, constitute a multi-specific fishery with a workforce of 15,000 people. Four species of kelps are harvested from natural populations by artisanal fishers and exported to world markets as raw material for alginate extraction. On the other hand, copper mining production, contributes 36 % of GDP and it is the major Chilean export commodity. Due to the non-existence of entry barriers to seaweed extraction, this activity has become the sink for unemployment in northern Chile mainly from mining activity. In this context, our results show that when the price of copper is in a downward cycle, it negatively affects the abundance of brown seaweeds, due to the mobility of the workforce to the coastline, increasing the illegal harvest of these marine resources. In contrast, the upward cycle of copper decreases the harvesting pressure, removing illegal harvesters from the coastline and returning them to a mining industry and its suppliers with better wages. These drivers would generate a temporary window for recovery and restoration of brown algae populations, as well as the numerous ecosystem services provided by these engineering organisms.

Copper Price Forecasting Based on Improved Least Squares Support Vector Machine with Butterfly Optimization Algorithm

Copper Price Forecasting Based on Improved Least Squares Support Vector Machine with Butterfly Optimization Algorithm

Quantifying the Impact of the COVID-19 Pandemic on Copper Price Trends Using Time Series Analysis

Quantifying the Impact of the COVID-19 Pandemic on Copper Price Trends Using Time Series Analysis

Is the prediction of precious metal market volatility influenced by internet searches regarding uncertainty?

There are countless articles on the relationship between economic uncertainty and the precious metals market, but studies examining whether economic uncertainty related queries (EURQ) can provide valuable information for the prediction of precious metal price volatility are scarce. Therefore, this paper examines the predictive power of EURQ for price volatility in the precious metals market with the GARCH-MIDAS model structure. The parametric results indicate a significant positive effect of the EURQ indicator on precious metal market volatility and mixed results for its asymmetric variation. Additionally, we investigate the performance of forecasting models that incorporate long- and short-term asymmetric effects from an out-of-sample perspective. We discover that using information from negative changes in returns and positive and negative shocks to the EURQ is more beneficial for forecasting precious metal futures price volatility than forecasting models that consider only a single indicator. Our findings emphasize the importance of queries on uncertainty-related topics in forecasting gold, silver, and copper price volatility and provide valuable guidance for rationally planning investment ratios.

Time-varying and multi-scale analysis of copper price influencing factors based on LASSO and EMD methods

In this paper, we select 75 indicators to conduct a comprehensive analysis of the factors influencing the copper price along six dimensions: inventory, supply, demand, the macroeconomy, finance, and geopolitics. Facing the high-dimensionality problem, we use the least absolute shrinkage and selection operator (LASSO) regression model to select variables to measure the contribution of each category of factors. Furthermore, we identify the time-varying nature of the relationship among factors with rolling windows. Then, we decompose copper prices into different scales of fluctuations by means of empirical mode decomposition (EMD) and investigate the driving factors at each scale. The results show that financial and geopolitical factors have played an important role in copper pricing in recent years. The long-run fluctuation trend of copper prices is mainly determined by fundamental factors, while financial and geopolitical factors have a more direct impact on short-term fluctuations.

Research on Spatiotemporal Heterogeneity of the Impact of Earthquakes on Global Copper Ore Supply Based on Geographically Weighted Regression

The large and super large copper deposits worldwide are mainly distributed in the Pacific Rim and Gondwana metallogenic domains, and they are highly coupled with the spatial location of seismically active zones. Major copper-producing countries such as Chile are located in areas with high seismic activity. Earthquakes often cause copper mines to shut down, copper prices to soar, and even mining disasters, affecting the stable supply of global copper. In order to study the impact of earthquakes on the global copper ore supply, information on resource endowments, production, and earthquake data from the past 30 years are collected. This article mainly analyzes (1) the spatial correlation between earthquakes and copper mine projects, and the spatial characteristics of earthquakes’ impact on copper mine production, using correlation analysis and geographically weighted regression (GWR); (2) the impact of sudden earthquake events on the export volume and price of copper mines from the perspective of time, using the earthquake index constructed based on the magnitude of the earthquake, the depth of the epicenter, and the distance from the copper mine. The results indicate that the regions with high spatial correlation between copper mine projects and earthquakes are mainly located along the Pacific coast of South and North Americas. Earthquakes can lead to an increase in copper prices, but they will fall significantly in the short term. The impact of earthquakes on export volume generally has a delay period of 1–2 months, resulting in a short-term decrease in export volume. This article quantitatively evaluates the impact of earthquake risk on the supply chain from both spatial and temporal dimensions, providing reference for supply chain risk monitoring, risk impact assessment, and prediction. On the basis of the research results of this article, mineral resource management departments can quantitatively evaluate the spatiotemporal impact of natural risks such as earthquakes on the supply chain, adjust management policies in a timely manner, and improve the level of refined management of supply chain risks.

Performance Evaluation of HWMA Control Chart based on AR(p) with Trend Model to Detect Shift Process Mean

The main goal of this study is to establish explicit solutions for the average run length (ARL) of the Homogenously Weighted Moving Average control chart when subjected to autoregressive with trend process. The accuracy of the explicit formula for the ARL is evaluated in comparison to the numerical integral equation method. To evaluate the two approaches, the accuracy percentage was employed. A determination is carried out of the HWMA control chart’s effectiveness using the median run length (MRL), the standard deviation of run length (SDRL), and the average run length (ARL). A comprehensive comparison is performed between the HWMA control chart, the Extended Exponentially Weighted Moving Average (EEWMA), and the cumulative sum (CUSUM) control charts with mean process shifts to illustrate the design and implementation of the HWMA control chart. As criteria for various values of design parameters, the performance of these control charts can also be evaluated using the relative mean index (RMI), the average extra quadratic loss (AEQL), and the performance comparison index (PCI). To evaluate the effectiveness of our explicit formula approach, we employ this formula on copper price data.

A Cross-Country Examination of Mineral Import Demand and Wind Energy Generation: Empirical Insights from Leading Mineral Importers

The relevance of this research lies in the utilization of mineral goods as critical industrial inputs for the manufacture of renewable energy machinery, which has sparked an increase in demand and prices for essential minerals. The purpose of the study is to examine the import-demand function for metallic mineral goods by applying the quantiles via moments (MM-QR) approach, considering the potential heterogeneity across the sample of the top 5 mineral-consuming (importing) nations. The dataset, covering the years 1996-2021, is analysed to test the hypothesis regarding the influence of wind production capacity on mineral-import requirements, considering the price of mineral goods, exchange rates, and income growth. We observe a monotonic favourable response of mineral import demands to wind power generation across all quantiles. However, when considering the quadratic form of wind energy generation, the mineral import demand shows a monotonic reverse trend as the size of wind energy generation expands. Our results reveal the unexpected finding of a monotonic positive effect of copper prices on mineral import demand, which contradicts the Marshallian price theorem. Conversely, the reaction of mineral imports to exchange rates remains consistently positive without modulation. Additionally, we observe a non-monotonic association between the income factor and mineral imports, indicating that the mineral import response to economic growth remains positive until a specific threshold is reached, beyond which it tends to stabilize. The theoretical and practical significance of these findings lies in boosting mineral goods trade to advance the clean energy transition goal for a decarbonized global environment.

Copper economic dynamics: Navigating resource scarcity, price volatility, and green growth

This research delves into the intricate discourse surrounding nations' pursuit of sustainable development between 2000 and 2020, focusing on ten major global copper-producing countries and leveraging international databases. It reveals a positive correlation between historical and current green growth efforts, an inverse relationship between copper prices and sustainable development, and explores the impact of Foreign Direct Investment (FDI) in the energy sector on energy inefficiency. Emphasizing the financial dynamics within the copper industry, the study highlights the potential negative impact of increased tax revenues on green growth. Furthermore, it underscores the pivotal role of financial conditions and economic context in shaping sustainable development, advocating for a balanced policy approach. To advance green growth in the copper industry, strategic interventions are proposed, encompassing effective copper price management, the promotion of green technological advancements, attraction of green FDI, implementation of emission taxation systems, advocacy for sustainable corporate management, and leveraging AI and big data analytics to optimize green solutions and minimize ecological footprints.

Comprehensive Evaluation of the Performances of Heat Exchangers with Aluminum and Copper Finned Tubes

The finned-tube heat exchanger is the core part of an air conditioning system. Its heat exchange performance directly affects the energy consumption and efficiency of the air conditioner. The shortage and rising price of copper have led to increasing replacement of copper tubes with aluminum tubes in finned-tube heat exchangers. This paper studies two kinds of such heat exchangers, one consisting of copper tubes and aluminum fins and the other consisting of aluminum tubes and aluminum fins. The influences of the different base tube materials on heat transfer are compared and analyzed in terms of heat transfer strength and cost per unit heat transfer. The results show that the heat transfer and heat transfer coefficient increase with increasing inlet wind speed. Under different inlet wind speeds, the heat transfer and heat transfer coefficient of the finned-tube heat exchanger with aluminum tubes are 4%–12% and 7%%–9% lower than those of an identically structured heat exchanger with copper tubes, respectively. The aluminum-aluminum exchanger achieves 67% higher heat transfer than that of the copper-aluminum exchanger at only 8% of the cost. These results are significant for guiding the development and application of finned-tube heat exchangers.

Economic Variables as an Early Warning Sign for Monitoring Construction Cost Indices – “An Empirical Study on Egypt”

The construction industry plays a crucial role in any nation's economic growth and prosperity through the implementation of the SDGs. Researchers reveal that the 17 SDGs are directly dependent by 17% and indirectly reliant by 27% on the construction industry's activities. In addition, the construction projects, whatever their type or components, continue for several years, during which many price changes and sharp economic fluctuations occur, whether at the local or international level, reflected in overall economic performance and the construction industry's health, leading to the project's cost changes and a shortage in the allocated budgets. Due to the difficulties this industry faces in reaching these goals, including the Egypt Vision 2030, we chose to look into these effects by using the ENR methodology to develop an explanatory construction cost indices model in Egypt similar to models used in other countries while making appropriate changes for the Egyptian markets. To illustrate the relationship between these selected economic variables and construction costs in Egypt and to act as an early warning sign to quantify, track, and predict the construction cost movements and trends in Egypt's construction sectors through a reliable model. A mixed research methodology consisted of semi-structured interviews with 15 industry experts and an empirical study covering 1990 through 2020 for selected local and international economic variables. We concluded that national and international economic variables significantly impacted as explanatory and predictive variables in Egypt's construction industry's cost indices; these variables are as follows: gross domestic product (GDP), consumer price index (CPI), USD to EGP exchange rate ($), inflation rate, lending rate, money supply, and unemployment rate, as well as international crude oil prices, gold prices, and copper prices. Nevertheless, further investigation is required with more economic variables across various construction industry sectors over various time intervals and different time series statistical and econometric modeling techniques.

The macroeconomic effects of exchange rate movements in a commodity-exporting developing economy

This paper empirically examines the macroeconomic effects of exchange rate movements and their transmission mechanisms (trade and financial channels) in Mongolia, a net debtor in foreign currency and a commodity-exporting developing economy, using Structural Bayesian Vector Autoregression (SBVAR) models. Our findings suggest that both the financial and trade channels of exchange rates are operative and have a notable impact at the macroeconomic level. We identify a significant financial channel, causing GDP contractions and affecting investments, particularly in sensitive sectors such as manufacturing and construction in response to DWER depreciation shocks. The traditional trade channel, driven by NEER depreciation, leads to increased net exports with pass-through to CPI. Despite these effects on key macro variables, exchange rate shocks do not substantially destabilize the economy. Foreign shocks, including federal funds rates, China's GDP, and copper prices, have a more pronounced impact. The trade channel plays a crucial role in the transmission of external demand and commodity price shocks, while the financial channel is essential in the transmission of commodity price shocks.

Testing Housing Price Drivers in Santiago de Chile: A Hedonic Price Approach

Hedonic modelling is an empirical analysis technique that is widely used to review the housing market and unpack the main determinants of price. A correct determination of housing price drivers allows a weighted prediction of the value of each dwelling for sale or rent. This paper reviews the fundamental determinants of housing prices that have been published in the literature to see which ones have a better predictive fit for the case of the housing market in the city of Santiago, Chile. From a record of 456,000 property transactions, a dataset composed of 11 explanatory variables is elaborated. Through a semi-logarithmic regression, 4 variables that contribute to explaining the formation of housing prices in Santiago, Chile, are identified. The results indicate that the socioeconomic characterisation of urban areas where housing is traded, the price of copper on the London Metal Exchange, the mortgage interest rate, the age of residential buildings are the main drivers for the main drivers of prices in Santiago. Given the crisis of access to housing in Chile, the article provides relevant information for decision-makers in housing policy.

Fossil Fuel-Based versus Electric Vehicles: A Volatility Spillover Perspective Regarding the Environment

Due to environmental concerns, electric vehicles (EVs) are gaining traction over fossil fuel-based vehicles. For electronic devices, including vehicles, copper is the key material used for building. This situation draws attention to the impact of copper prices, crude oil prices, and exchange rates on the economic viability of using EVs over fossil fuels. We use the volatility spillover effect (VSE) to determine the financial viability of these two types of vehicles in the context of environmental issues. Daily data on copper prices, crude oil, exchange rate, and the BSE100 ESG (“Bombay Stock Exchange 100 Environmental, Social and Governance”) index are taken from 1 November 2017 to 20 September 2022. Two popular multivariate GARCH (“Multivariate Generalized Autoregressive Conditional Heteroscedasticity”) family models, i.e., the BEKK (“Baba–Engle–Kraft–Kroner”)-GARCH (BG) and DCC (“Dynamic Conditional Correlation”)-GARCH (DG) models, are utilized to find volatility connections between these variables. These are appropriate GARCH models to observe the volatility dependence of one market on another market. It is found that there exist volatility effects of copper and exchange rate on the S&P BSE100 ESG Equity Index Price, which we will refer to here as ESG. However, crude oil is found to be insignificant for ESG. The novelty of this study is in the use of volatility spillover to determine economic viability. The volatility effects of copper prices are positive for ESG in the short run and negative for long-term volatility. The exchange rate has a positive volatility effect on ESG in the long run. Surprisingly, we find that EVs are technologically better than fossil fuel-based vehicles as a possible sustainable energy source. We observe studies that have raised similar concerns about EVs’ lack of business sense compared to fossil fuels. However, using VSE to explore financial viability offers a fresh perspective. Based on the findings of the current study, it is recommended that policymakers and researchers revisit their support for EVs as an alternate and sustainable source of energy.

Analysis of the Impact of the Net Zero Movement on International Trade

The net-zero carbon emission mission raised by UNFCCC accelerated the already looming shortage of various key metals in the construction of renewable energy facilities, such as copper for the construction of solar panels. Using basic econometric tools through Stata, this paper studied the effect on copper prices related to the net-zero campaign, aiming at both establishing the validity of such an effect and quantifying its scale. Relevant data from FRED demonstrates that this effect drives up copper price at a high significance level and quantifies it to be 2440.636 USD per metric ton. This effect also seems to be lagging into future periods, and a staggered events analysis suggests that the growth in copper price due to the net-zero campaign will decelerate in the near future. Potential flaws are discussed, and several valid and valuable future research directions are also raised.

Identifying price bubbles in copper market: Evidence from a GSADF test approach.

This paper uses the test proposed by Generalized Supremum Augmented Dickey-Fuller to identify whether there are multiple bubbles in copper price. The empirical results show that base on market fundamentals, there are seven bubbles existed from January 1980 to March 2023. Through analyses, the first two bubbles can be explained by the demand from Japan by the industry concentration and persistent supply constraint. The third to sixth bubbles are mainly negatively impacted by the global financial crisis and growing demand of China. The last bubble is caused by the economic recovery from Covid-19. The logit regression has stated that aluminum price, copper production, all metals index and GDP have a positive impact on copper bubbles, while China's copper imports and precious metals price negatively explains copper bubbles. The main contributions are the investigation of the copper price bubbles, its determinants and the different technique of GSADF to detect copper price bubbles. Furthermore, it provides helpful information for those investors to make reasonable investment decisions and thus, avoid potential price risk.

Copper price prediction using LSTM recurrent neural network integrated simulated annealing algorithm.

Copper is an important mineral and fluctuations in copper prices can affect the stable functioning of some countries' economies. Policy makers, futures traders and individual investors are very concerned about copper prices. In a recent paper, we use an artificial intelligence model long short-term memory (LSTM) to predict copper prices. To improve the efficiency of long short-term memory (LSTM) model, we introduced a simulated annealing (SA) algorithm to find the best combination of hyperparameters. The feature engineering problem of the AI model is then solved by correlation analysis. Three economic indicators, West Texas Intermediate Oil Price, Gold Price and Silver Price, which are highly correlated with copper prices, were selected as inputs to be used in the training and forecasting model. Three different copper price time periods, namely 485, 363 and 242 days, were chosen for the model forecasts. The forecast errors are 0.00195, 0.0019 and 0.00097, respectively. Compared with the existing literature, the prediction results of this paper are more accurate and less error. The research in this paper provides a reliable reference for analyzing future copper price changes.

Dynamic relationship between refined and scrap copper prices

Dynamic relationship between refined and scrap copper prices

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.