Copper Prices Research Articles

Relationship of iron ore price with other major commodity prices

Understanding the interdependency of commodity market pricing system is very important for running a successful mining business. Much of the iron ore price is derived from the prices of other commodities. This study investigates the relationship between monthly iron ore prices against 12 other monthly commodity prices or indices including LNG, aluminium, nickel, silver, Australian coal, zinc, gold, oil, tin, copper, lead, and Commodity Price Index (Metals) in both bivariate and multivariate perspectives. An augmented Dickey-Fuller (ADF) test is carried out to ensure that all the time series commodity prices and index are non-stationary. In multivariate modelling co-integration tests, observation is made on how many co-integrations exist out of 12 co-integrations for each respective lag between 0 and 45 months’ period. It is observed that 6 out of 12 commodity prices follow co-integrations in 1-month lag and continues in a cyclic pattern until 27 months after which it disappears. There are 3 commodities which continuously co-integrate with iron ore price change at all lags. For bivariate modelling, vector error correction model (VECM) estimation is carried out to prove the evidence of short-run responses to long-term relationship between iron ore prices and it is observed that oil, copper, and Australian coal prices have influence on and from iron ore prices. Then, Granger causality test is carried out to verify the VECM result by testing bi-directional causality between iron ore prices and copper, oil, and coal prices. It has been concluded that the iron ore price has bi-directional influence on oil, copper, and Australian coal prices and vice versa.

Conditional sovereign CDS in market basket risk scenario: A dynamic vine-copula analysis

This study examines the sensitivity of sovereign CDS markets in G7 and BRICS, which is conditional on a joint market basket risk scenario consisting of crude oil, gold, stock indices, exchange rates, freight indices, and copper prices. By compare the conditional and unconditional sovereign CDS returns using dynamic Vine-Copula model, we find that: 1) The conditional sovereign CDS returns will be less than (greater than) the unconditional ones, when scenario settings is at upper (lower) quantile level. Extreme scenario risk level settings (e.g., 1% or 99%) do not always make a significant difference between conditional and unconditional sovereign CDS. 2) Major black swan evens have significant impact on the difference between the conditional and unconditional sovereign CDS, but such an impact is short-lived especially in G7 countries. 3) Taking into account of the covariate effects, the conditional risk scenarios of sovereign CDS are heterogeneous across countries, down- and up-ward tail as well risk factors associated with the market basket.

Structural shocks of international copper price fluctuation on China’s industrial economy: Based on MSVAR and TVP-SVAR-SV models

Structural shocks of international copper price fluctuation on China’s industrial economy: Based on MSVAR and TVP-SVAR-SV models

Possibilities of constructing a copper smelter in the republic of Armenia

Nowadays, the export volume of copper concentrate from the Republic of Armenia equals more than 500 thousand tons per year. This extent fully satisfies the minimum requirements for constructing a copper smelter in our country. Calculations show that the copper production capacity of the Republic of Armenia is estimated more than 120 thousand tons per year. Although it is considered to be a modest result in the context of copper mining scale worldwide, it is of significant importance for our country. The production and export of pure copper will contribute to the flow of currencies into our country. Meanwhile, it will enable to construct and exploit a cable factory, which, in turn, will create favorable grounds for new working places and for the rise of overall economic activation. The necessity for constructing a copper smelter has also been stated by the government of the Republic of Armenia. One of the concerns remains the realization of sulfuric acid extracted in the process of copper production. A solution could be transporting it to the port by railway through the area of Georgia. Still, according to some viewpoints, it is possible to recycle sulfuric acid on the spot and convert to fertilizers, varnish, paint, clay etc. The paper takes into focus the possibilities of constructing a copper smelter in the Republic of Armenia demonstrating the efficiency of investments and their buyback deadlines on current copper price conditions.

Yoksullaştıran Büyüme: Seçilmiş Ülkeler İçin Bir Panel Veri Yaklaşımı

In the study, using panel data analysis, the relationship between GDP and global iron, global cotton, global oil, and global copper prices were analyzed under the impoverishing growth hypothesis. The countries analyzed in this study are developing countries such as China, Chile, Czechia, Greece, Hungary, India, South Korea, Malaysia, Pakistan, Peru, Philippines, Poland, Thailand, and Turkey. The data covers the period 2003-2018. The analysis results show that the increase in global cotton and oil prices decreases the GDP of these countries, and the increase in global iron and copper prices increases the GDPs of these countries. Therefore, policymakers should support investment in renewable energy, domestic agriculture, and mining sectors.

Copper cross-market volatility transition based on a coupled hidden Markov model and the complex network method

Copper, as a strategic resource, has attracted widespread attention due to its price changes. Copper's futures prices and spot prices tend to be highly correlated in different countries. The copper market price change should consider the relationship between market trends of different countries, especially in the current situation where economic globalization and financial integration are inevitable. The volatility transition between datasets of different countries presents the characteristics of nonlinearity, interaction, dynamics and heterogeneity. To explore the cross-market transition of copper price fluctuations, this paper proposes a hybrid method of the coupled hidden Markov model (CHMM) and complex network. The coupled hidden Markov model (CHMM) is used to identify the transition characteristics of the market price trends, and the complex network method is used to analyze the evolutionary characteristics of the market state transition. We take the copper futures and spot market prices between the U.S., China, and the UK between January 2003 and December 2019 as the object. The results show that the copper futures market's state transition has a clustering effect, the price rise is easy to maintain, and the decline is rapid and easy to transfer. The relationship between daily markets is more volatile, and weekly and monthly are more stable. The British copper futures market has shown advantages in terms of transition ability, assimilation ability, media ability and transfer structure, while the influence of China's copper futures market is relatively weak. In addition, during the financial crisis, the UK spot market played an important role in the frequent declining transition. The nonlinear measurement model and conclusion established in this paper provide a new framework and analysis tool for explaining the volatility transition of the copper market.

Copper price forecasted by hybrid neural network with Bayesian Optimization and wavelet transform

The metal prices play an important role in many aspects of economics. Copper, a widely used metal in the industry, has received an extensive attention recently. Due to the high fluctuations in copper price that makes it difficult to predict especially when using the traditional statistical models, in this work, a hybrid Neural Network with Bayesian Optimization and Wavelet Transform is applied to forecast the copper price in both short- and long-terms, in which Bayesian Optimization Algorithm is used on the hyperparameter searching task, the Wavelet Transform is applied to denoise the data and eliminate the irrelevant information, and Long Short Time Memory (LSTM) and Gated Recurrent Units (GRU) are employed to train the data and predict future copper price, respectively. The results indicate that our methods, either LSTM or GRU, can appropriately predict the copper price for both short- and long-terms with mean squared error both below 3% and this hybrid Neural Network is robust to remove the irrelevant information and search the optimized set of hyperparameters. Meanwhile, it is easily and readily applicable to predict the prices of other commodities (i.e., stock market).

Copper price: A brief analysis of China’s impact over its short-term forecasting

This work addresses the feasibility of modeling the copper price through SARIMA approach. The period under study was 30 years (1991–2020), leaving the last year (2020) as the testing set, and the previous 29 years as the training set.The results with the nominal copper price by itself are of good quality in comparison with present literature (best MAPE for training set was 3.98%). As the exploratory data analysis (EDA) suggest a non-seasonal series, the best model obtained under this assumption was, following the notation p,d,qP,D,QS, the model (0,1,2)(0,0,0)0. This model has 4.16% of MAPE in the training set, and it showed 4.31% MAPE in the testing period of one year (2020), using the rolling window technique.To improve forecast modeling, exogenous variables were included as a complement, using a SARIMAX approach. China’s Leading Economic Index and Copper Consumption were the chosen alternatives. In the training data, adding any of them does not show an improvement. However, in the testing group, the economic activity becomes an interesting choice for performance, reducing the MAPE to 4.26%.

An integrated approach to open-pit mines production scheduling

In an Open-Pit Production Scheduling (OPPS) problem, the aim is to determine the mining sequence of an orebody as a block model. In common research methods for scheduling, commodity price and consequently block economic value are considered as fixed values. Due to commodity price time series fluctuations throughout the time, it is necessary to consider the variation of commodity price in each period of the OPPS problem. This paper introduces a new approach for the integration of forecasted copper prices based on stochastic differential equations (SDE) with the conventional integer programming OPPS problem. If the block values are different in each period, the block value is referred to as dynamic, else it is considered static. Since OPPS is known to be an NP-Hard problem and block values vary according to the scheduling period, an efficient Genetic Algorithm (GA) is presented to find feasible solutions. Our proposed algorithm is implemented on Marvin copper orebody. Comparison of the design by the conventional static and the SDE simulation-based dynamic block value methods illustrates that compared to the static design; the total value of the dynamic design is 43% higher than that of static design.

Effects of crude oil prices on copper and maize prices

This study explains the effects of crude oil prices on copper and maize prices. Vector autoregressive and vector error correction models are used to study the relationship between oil prices and prices of copper and maize. The commodity price data used consist of average monthly prices of each of the commodities: crude oil, copper and maize for the months January 1982 to June 2021. For robustness, the analysis was also run on a sample of the same data for the period January 2000 to June 2021. A long-run relationship was found between crude oil and copper prices on the one hand and maize prices on the other for the 1982 to 2021 period at the 5% significance level. The same was not true for the shorter sample (2000 to 2021). Granger causality flowing from crude oil prices alone to copper and maize prices was not found. Recommendations that are useful for energy, mining, agriculture and general development policy and practice are made. The findings are also useful for bilateral and multilateral aid discussions. The limitations of the study and recommendations for future scholarship are also made.

The Chilean economy, an analysis of the dynamics of profits, investments and production: A Marxist approach

This article aims to examine the dynamics of the Chilean economy as a consequence of actions taken by companies whose aim is to make profits. As such, the economic analysis used is Marxist and makes use of those classical indicators described in Capital (Rate of Surplus-Value, Organic Composition of Capital and Rate of Profit). It is maintained that with the Marxist method, we can discover that behind the accumulation of incomes lies the fact that out of each 8 hours worked, only 3 finance wages and 5 benefit the owners of capital. That fraction of the unpaid labour received by capital but invested back as new capital, plus that ‘excess’ surplus value that is consequence of high copper prices, raises the physical, but not necessarily the value, capital-per-worker ratio. As a consequence, that relation of exploitation to capital accumulation, which Marx called the Rate of Profit, is found to fall, rise and then fall again. We understand that various approaches have been made to calculate the classical indicators and include some of them as alternative methods in our results.

COMPARATIVE STUDIES OF THE SUSCEPTIBILITY OF COPPER AND ALUMINUM TO PROCESSING IN WIRE DRAWING PROCESS

Modern technological solutions in electrical applications require, above all, high energy efficiency. This means that in such applications, materials with the highest possible electrical conductivity should be used for conductive elements. Of all metals, copper is the natural choice. Copper cables or wires used in electrical networks and installations provide better electrical and thermal conductivity and guarantee reliability - which translates into the quality of electricity. And bad quality of energy means tangible financial losses. On the other hand, apart from physical properties, an important factor is the price of cables and wires, which is partially dependent on the price of copper on the stock exchange. The last 20 years have seen a high increase in copper prices on the markets and at the same time the price is unstable. Therefore, cheaper substitutes for copper are being sought. The natural choice is aluminum, which is currently widely used in overhead power lines. At the same time, in the case of long cables, copper products still dominate. The paper presents a synthetic comparative analysis of technological processing problems in industrial drawing processes of aluminum and copper wires used in electrical applications.. In particular, comparative analysis applied the susceptibility into formability in the wire drawing process and susceptibility to recrystallization annealing of copper and aluminum wires. The drawing process and the annealing process are simple processes for the production of wires used for electrical purposes.

MODEL EFFECT OF COPPER PRICE ON FREEPORT MCMORAN STOCK PRICE

The copper price in copper mining companies is an essential aspect in terms of profit, revenue, production targets, and hedging. This research aims to determine an alternative of copper price modeling and its causality relationship to Freeport McMoRan (FCX) stock price. The methods utilized in this research were Autoregressive Integrated Moving Average (ARIMA), Artificial Neural Network (ANN), Genetic Algorithms (GA) and Granger Causality Test. Based on this research result, all modeling methods equally show excellent performance for modeling copper price. Another finding from this research is that the copper price positively affects the FCX stock price. Therefore, it can be concluded that the copper commodity price influences the value of a copper mining company. The results of this research can be utilized as a reference for company analysts as a part to estimate profit probability, estimate revenue, estimate production targets, and hedging strategies. Keywords: ARIMA, causality, genetic algorithm, neural network, price model

Market risk factors analysis for an international mining company. Multi-dimensional, heavy-tailed-based modelling

Mining companies to properly manage their operations and be ready to make business decisions, are required to analyse potential scenarios for main market risk factors. The most important risk factors for KGHM, one of the biggest companies active in the metals and mining industry, are the price of copper (Cu), traded in US dollars, and the Polish zloty (PLN) exchange rate (USDPLN). The main scope of the paper is to understand the mid- and long-term dynamics of these two risk factors. For a mining company it might help to properly evaluate potential downside market risk and optimize hedging instruments. From the market risk management perspective, it is also important to analyse the dynamics of these two factors combined with the price of copper in Polish zloty (Cu in PLN), which jointly drive the revenues, cash flows, and financial results of the company. Based on the relation between analysed risk factors and distribution analysis, we propose to use two-dimensional vector autoregressive (VAR) model with the α-stable distribution. The non-homogeneity of the data is reflected in two identified regimes: first – corresponding to the 2008 crisis and second – to the stable market situation. As a natural implication of the model fitted to market assets, we derive the dynamics of the copper price in PLN, which is not a traded asset but is crucial for the KGHM company risk exposure. A comparative study is performed to demonstrate the effect of including dependencies of the assets and the implications of the regime change. Since for various international companies, risk factors are given rather in the national than the market currency, the approach is universal and can be used in different market contexts, like mining or oil companies, but also other commodities involved in the global trading system.

Development of copper price from July 1959 and predicted development till the end of year 2022

The increasingly meagre copper ore resources constitute one of the decisive factors influencing the price of this commodity. The demand for copper has been showing an accelerating trend since the Covid pandemic broke out. It is thereby imperative to estimate the future price movement of this material. The article focuses on a daily prediction of the forthcoming change in prices of copper on the commodity market. The research data were gathered from day-to-day closing historical prices of copper from commodity stock COMEX converted to a time series. The price is expressed in US Dollars per pound. The data were processed using artificial intelligence, recurrent neural networks, including the Long Short Term Memory layer. Neural networks have a great potential to predict this type of time series. The results show that the volatility in copper price during the monitored period was low or close to zero. We may thereby argue that neural networks foresee the first three months more accurately than the rest of the examined period. Neural structures anticipate copper prices from 4.5 to 4.6 USD to the end of the period in question. Low volatility that would last longer than one year would cut down speculators’ profits to a minimum (lower risk). On the other hand, this situation would bring about balance which the purchasing companies avidly seek for. However, the presented article is solely confined to a limited number of variables to work with, disregarding other decisive criteria. Although the very high performance of the experimental prediction model, there is always space for improvement – e.g. effectively combining traditional methods with advanced techniques of artificial intelligence.

Time-varying impact of political risk on copper prices

Using the International Country Risk Guide (ICRG) index to represent countries' political risk, the time-varying effect of political risk on copper prices was examined based on the time-varying parameter structural vector autoregression with stochastic volatility (TVP-SVAR-SV) model. The empirical results show that the impact of political risk on copper prices is time-varying and has tended to increase gradually in recent years. There are significant country-level differences in the impact of political risk on copper prices. Political risk has a stronger and longer-lasting impact on copper prices in exporting countries. In terms of risk sources, external and internal conflicts contribute most to international copper price fluctuations in the sample period. The impact of political risk on copper prices reaches an extreme level during the international financial crisis, the European debt crisis, and the election of Donald Trump.

Forecasting copper price by application of robust artificial intelligence techniques

Metal price is one of the most important and effective parameters in assessing different projects such as industry and mining. In this regard, price variations can play a vital role in the correct decision-making of managers to develop or limit mining activities. Considering the increasing use of artificial intelligence (AI)-based networks in different fields such as price estimation, four methods were used in the present work for the first time to predict the price of important and extensively used copper-grade A cathode. These methods include Gene expression programming (GEP), Artificial neural network (ANN), Adaptive neuro-fuzzy inference system (ANFIS), and ANFIS-ACO (ant colony optimization algorithm). In this process, coal, aluminum, crude oil, gold, iron ore, natural gas, nickel, and lead were selected as the copper price parameters from 1990 to 2020. In this study, the ANN model with one hidden layer comprising 13 neurons, RMSE of 356.51, MAE of 239.105 ($/ton), MAPEof 5.70% ($/ton), and coefficient of determination (R2) of 98.1% for network test data was selected as the best model in predicting copper prices. In terms of their performance, ANFIS, ANFIS - ACO and GEP models were ranked next in the order of their appearance. Overall, an acceptable performance was found through all four AI methods in this study for predicting copper prices.

Point and interval prediction for non-ferrous metals based on a hybrid prediction framework

As a bulk product with huge international circulation, non-ferrous metals have frequent and severe price fluctuations, which have attracted great attention from academia and industry. However, the non-ferrous metal price series has strong volatility and nonlinear characteristics, which makes the realization of high-precision forecasts still a difficult and challenging problem. In this paper, a hybrid point prediction system is constructed to achieve high precision point prediction results. Moreover, uncertain forecasts contain more information and can provide market participants with more detailed guidance, but uncertainty forecasting is often ignored in practice. Based on the high precision point prediction system, the uncertainty prediction framework is proposed in this paper. Different distribution functions were used to analyze the distribution characteristics of the data, and the uncertainty prediction at different levels was successfully realized according to point prediction results. To verify prediction performance of the proposed prediction framework, multiple contrast experiments have been carried out using the London Metal Exchange daily future prices of Zinc, Copper and Lead. The empirical results show that the developed prediction framework has better predictive power for non-ferrous metals price prediction.

Economic drivers of commodity volatility: The case of copper

This paper examines whether economic variables provide useful information with which to forecast monthly copper price volatility. Prior literature regarding equity markets has discussed this question extensively, but less is known about these variables’ predictive power in the context of commodity markets. We focus on copper, which is a non-renewable commodity that plays a significant role in several economies and markets. To shed new light on this topic, we employ a Bayesian Model Averaging (BMA) approach to account for both parameter and model uncertainty. Our empirical results show that several economic variables have significant forecasting power when compared against an autoregressive benchmark model, and that predictability varies across the business cycle.

Prediction and Analysis of Gold Prices using Ensemble Machine Learning Algorithms

In this article, we used historical time series data up to the current day gold price. In this study of predicting gold price, we consider few correlating factors like silver price, copper price, standard, and poor’s 500 value, dollar-rupee exchange rate, Dow Jones Industrial Average Value. Considering the prices of every correlating factor and gold price data where dates ranging from 2008 January to 2021 February. Few algorithms of machine learning are used to analyze the time-series data are Random Forest Regression, Support Vector Regressor, Linear Regressor, ExtraTrees Regressor and Gradient boosting Regression. While seeing the results the Extra Tree Regressor algorithm gives the predicted value of gold prices more accurately.