Copper Prices Research Articles

La medición de la renta de la tierra minera en Chile y sus cursos de apropiación (1940–2017)

Abstract In this paper, we present the foundations and results for a new rent database on mining land in Chile (1940–2017), which takes into account not only the surplus profits of the sector, but also the different mechanisms in which this land is appropriated by other social actors. The results are weighted in relation to the whole national economy, which is why an original time series of the general rate of profits and its components, surplus-value and total advance capital, is also provided. In this paper, we posit a methodological foundation based on Marx's developments and a critical review of the existing statistics and previous measures. The results are original as they are the first long-run time series of mining land rent which considers the main appropriation mechanisms by different social actors. In turn, it shows that previous studies underestimate the weight of mining land rent in the Chilean economy, particularly when the prices of copper are rising. In addition, the results make it possible to pose new questions regarding the development of the national specificity considered. As a result of this new evidence, we indicate specific determinations of the different political cycles in Chilean national life, showing the historical persistency of mining land rent beyond changes in its appropriation forms and, therefore, stepping outside of the import substitution industrialization and neoliberalism dichotomy, which dominates the long-run economic historiography in this country.

Forecasting Copper Price with Multi-view Graph Transformer and Fractional Brownian Motion-Based Data Augmentation

Forecasting Copper Price with Multi-view Graph Transformer and Fractional Brownian Motion-Based Data Augmentation

Detecting bubbles in world aluminum prices: Evidence from GSADF test

The aim of this research is to assess the existence of multiple bubbles in the global aluminum market by employing the Generalized Supremum Augmented Dickey-Fuller (GSADF) methodology. This method offers practical time series analysis tools for identifying periods of rapid price escalation, followed by subsequent collapses. Findings indicate the identification of six explosive bubbles occurring between January 1980 and March 2023, during which the aluminum price strayed from its underlying fundamental value. Additionally, this finding is consistent with the asset pricing model, which generally considers both fundamental and bubble components. Based on the empirical results, the aluminum price bubbles are positively influenced by the copper price, GDP, the U. S dollar index, industrialization of China, China’s urbanization rate, whereas the global aluminum production, oil price, and base metal price index have a negative explanatory effect on the aluminum price bubbles. To effectively stabilize the international aluminum price, policymakers are suggested to be vigilant in identifying bubble episodes and monitoring their progression. Additionally, regulatory authorities should implement measures to curb excessive speculative activity during periods of extreme market volatility, thereby mitigating excessive price fluctuations and the formation of aluminum bubbles.

Transmission and Volatility Mechanisms of Mineral and Oil Prices in the Context of Clean Energy Transition: A Global Perspective

The prices that govern the output of producers in specific industries hold great significance, particularly in the context of the renewable energy industry, which is profoundly influenced by the dynamics of critical mineral prices. Oil prices also affect mineral transportation and cost-competitive consumer choices, altering the renewable energy sector. This study examines the global transmission of shocks and volatility spillover effects of essential mineral and oil prices in renewable energy production covering the monthly data from January 1990 to June 2023. We use partial cross-quantilogram (PCQ) and rolling window-based recursive cross-quantilogram (RCQ) methods to address the dataset's non-normal and fat-tailed nature. The PCQ analysis shows that aluminum and copper price shock transmission and volatility spillover effects boost cumulative and decomposed renewable energy generation, particularly solar and wind power, in bullish market conditions with long-term memory. Nickel prices decline in bullish markets with long memory. Interestingly, oil price shock transmission and spillover effects benefit solar and wind energy generation over long time horizons. Thus, this study emphasizes the importance of leveraging mineral prices by determining favorable values and implementing market operations to promote environmental sustainability and sustainable development in the face of oil-based industrial expansion.

Earthquakes in Chile and Peru: How are they reflected in the copper financial market?

A large earthquake is a devastating natural disaster affecting life and infrastructure. Copper, vital for combating climate change, sees over 40% production from Chile and Peru. An earthquake near these mines induces uncertainty, boosting copper prices. After analyzing copper returns after large-magnitude earthquakes (2005–2020) in Chile and Peru, event studies revealed positive abnormal returns. These are intensified by media coverage of the earthquake and available stock, which has a short-lived impact on other metals. This outlook will help investors to hedge and cope with such earthquakes.

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.

Uticaj berzanskih indeksa i cena sirovina na kretanje prinosa hartija od vrednosti kompanije Caterpillar

The goal of this paper is to examine the effect of selected stock market indices and the market prices of chosen raw materials on the movement of future daily returns of Caterpillar's shares. Historical data on stock returns and other relevant factors can be utilized to build statistical models for forecasting future market movements. Daily time-series data were collected from Yahoo finance for a five-year period. In this study, we use multiple linear regression to predict the returns of CAT shares. The model results can be analyzed to identify patterns, trends, and insights that may be useful for forecasting. The results show that the prediction accuracy of the proposed model, with four additional predictors, is 13.3% higher than the benchmark model, which only used the SPY market index (SPDR S&P 500 ETF) to explain the variability in CAT stock returns. The research confirmed that, in addition to the SPY market index, the performance of the PKB (Invesco Dynamic Building and Construction ETF) fund, as well as the prices of copper, steel, and aluminium have a statistically significant impact on the movement of CAT stock returns. The practical implication of this study is that investors, when deciding whether to invest in CAT shares, must consider not only systematic factors but also the performance of shares within its supply chain. The implication highlights the importance of analyzing Caterpillar's entire business system when making investment decisions. Such predictions contribute to a better understanding of the characteristics that enable optimal aggregation of information and efficient price determination. They also assist investors in selecting an investment strategy aligned with their objectives

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.