Copper Prices Research Articles

How Commodity Prices Influence the Members of the Eurasian Economic Union

This paper tests the hypothesis on the presence of common factors in the dynamics of the Eurasian Economic Union (EAEU) countries’ macroeconomic indicators. As the most important factor, the common dynamics of commodity prices in world markets was chosen. We identified key channels of the transmission of commodity price shocks for EAEU economies. These transmission channels include both direct transmission channels, connected with the terms of trade, and indirect channels. The latter ones include labour migration between EAEU countries, money transactions by migrants, high trade linkages between EAEU countries, and preferential pricing for energy resources. The empirical part of the paper studies the influence of commodity prices on EAEU countries’ macroeconomic indicators. We have proposed a factor autoregressive model. Based on this model, we have identified the first principal component price movements in oil, natural gas, aluminum, copper, steel, gold and wheat. The first principal component explains 75 % of oil and gas price growth rate variation, 45 % of the copper price growth rate variation, and more than 25 % of the variation of other series. Further, we used VAR model to estimate the influence of the first principal component on the EAEU countries’ GDP, consumption and investment dynamics. The results showed the significant positive effect of commodity prices growth in all EAEU members. We also revealed a number of shock transmission features. For example, price shock affected Russian economy through the income effect, while the same shock affected Armenian economy through the direct output stimulation. Our results may be useful for the coordination of the economic policy and business cycles stabilization amongst EAEU countries.

Determining the embedding parameters governing long-term dynamics of copper prices

Mineral extraction activities and the commercialisation of mineral-based products in later stages are fundamental for economic, technological and social development, so understanding their long-term market behaviour in order to forecast prices is crucial for governments, companies and society. Mineral commodity markets are multidimensional as they are driven by the dynamic evolution of financial, technological, psychological and geopolitical factors. Thus, identifying the key features governing mineral commodity prices and modelling their long-term behaviour is an intricate problem that becomes even more complex when the relationship with time is also considered. Although several techniques are available to represent the behaviour and reduce the dimensionality of data sets, these techniques often neglect the temporal relationship and evolution of variables. In many cases, the selection of key variables is solely based on the correlation with each other, relevance and degree of variance, and the cause and effect behaviour of the time-relation of variables, is neglected. This is a critical issue in assessing dynamic systems, especially those involving human behaviour such as social sciences and biology where learning and cognition capacities evolve through time. In this sense, before any attempt to forecast mineral commodity prices, a proper understanding of the embedding dimension and time delay of the variables governing the system is fundamental to determining the number of key features driving the system and the extension of the delayed effects of changes in the initial conditions, respectively. Determining these parameters may become even more complicated because of data scarcity, since the high cost, technical or temporal limitations often hamper obtaining large data sets related to complex dynamic systems that occur in nature and the social sciences. Daily and monthly quotations have been commonly used to represent the dynamics of mineral commodity markets and predict prices, yet they are not sufficient to capture and understand the dynamic evolution of prices in the long-term. These quotations are too short as the delayed effects that reflect on the entire economy and arises from changes in macroeconomic variables can normally be perceived about a year ahead, and may last for up to six years. However, chaos theory has provided valuable tools to assess the dynamics of complex systems allowing the proper determination of the embedding parameters (dimension and time delay), even when using small data sets. This paper examines the long-term dynamics of mineral commodity prices via chaos theory based on a short data set of annual copper prices observed between 1900 and 2015. Copper was used as a study case because it is one of the most competitive markets and representative of mineral commodities traded worldwide. It was found that the dynamic-deterministic behaviour of annual copper prices is embedded in a high-dimensional space that fluctuates between five and seven, and a relatively short time delay between two and three periods. Our finding argues for the use of chaos theory as an important technique to assess the long-term behaviour of mineral commodity prices using short data sets because it improves the understanding of those complex dynamic systems and provides important guidelines that remarkably simplifies the forecasting task.

Forecasting copper price using gene expression programming

Forecasting the prices of metals is important in many aspects of economics. Metal prices are also vital variables in financial models for revenue evaluation, which forms the basis of an effective payment regime using resource policymakers. According to the severe changes of the metal prices in the recent years, the classic estimation methods cannot correctly estimate the volatility. In order to solve this problem, it is necessary to use the artificial algorithms, which have a good ability to predict the volatility of various phenomena. In the present work, the gene expression programming (GEP) method was used to predict the copper price volatility. In order to understand the ability of this method, the results obtained were compared with the other classical prediction methods. The results indicated that the GEP method was much better than the time series and multivariate regression methods in terms of the prediction accuracy.

Time-varying co-movement of the prices of three metals and oil: Evidence from recursive cointegration

The aim of this paper is to study the continuous and time-varing long-run relationships among three metals’ prices, oil price, and the US dollar exchange rate. The recursive cointegration is applied to trace the dynamic linkages. The empirical evidence is follows. First, the results of the recursive trace statistics display one significant and strong conitegration among the gold price and the other variables over much of the period after 1995, and that the European sovereign debt crisis caused a closer linkage from 2010 to 2012. Second, rising gold prices increase silver and copper prices in the long run and are also a long-run leading indicator of silver and copper prices, but their function as a leading signal becomes unstable and weaker after the 2008–2009 global financial crisis. Finally, the long-run relationship between oil and gold prices is an inverse interaction before 2003, but then turns uncertain after 2003, and there is no long-run causality between the two prices.

Determining the chaotic behaviour of copper prices in the long-term using annual price data

Mineral commodity prices are influenced by economic, technological, psychological, and geopolitical factors. Stochastic approaches, and time series and econometric techniques have been used to represent the dynamics of mineral commodity markets and predict prices. However, these techniques cannot provide a comprehensive representation of market dynamics because they do not recognise the relationship between these factors over time, and they are unable to capture both the evolution and the cumulative effects of these factors on prices. Stability of motion and chaos theories can detect sensitivity to initial conditions, and therefore the evolutionary patterns allowing a proper understanding and representation of mineral commodity market dynamics. Most of the techniques used to assess chaos require a colossal amount of data, so the use of small data sets to assess chaos has been largely criticised. Nevertheless, by definition, the dynamics of a chaotic system remain at different scales owing to its self-organisation features that exhibit ordered patterns in the absence of codes or rules. Therefore, any deterministic chaotic behaviour of mineral commodity prices can be captured by using small data sets if a detailed qualitative and quantitative analysis are carried out. This paper examines the chaotic behaviour of annual copper prices between 1900 and 2015. To do so, we combine chaos theory, stability of motion and statistical techniques to reconstruct the long-term dynamics of copper prices. First, we examine the time dependency and the presence of a strange attractor by a visual analysis of the time series and phase space reconstruction based on Takens’ theorem and determine embedding parameters. Then we examine the dynamic characteristics of the system which assesses its complexity and regularity patterns to measure the system’s entropy. Finally, we calculate the largest Lyapunov exponent λ to assess the sensitivity to initial conditions and determine chaotic behaviour supported by a surrogate test. We find that annual copper prices have a chaotic behaviour embedded in a high-dimensional space and short time delay. The study suggests that copper prices exhibit only a single state of low prices, which fluctuate through transitional periods of high prices. It challenges the assertion that metal markets have fluctuated over four major super cycles and debate the adequacy of stochastic and econometric models for representing mineral commodity market behaviour.This study recommends that the use of chaotic behaviour improves our understanding of mineral commodity markets and narrows the data searching, processing and monitoring requirements for forecasting. Therefore, it improves the performance of traditional techniques for selecting key factors that influence the market dynamics, and may also be used to select the most suitable algorithm for forecasting prices.

Co-movement of international copper prices, China's economic activity, and stock returns: Structural breaks and volatility dynamics

This study empirically investigates both causal nexus and time-varying correlations among international copper prices, China's real economic activity, and stock returns from January 1991 to December 2015. Using a cross-correlation function approach with structural breaks and dynamic conditional correlation models, we find, first, significant volatility cross-effects between international copper prices and real economic activity in China. Second, China's past stock returns play a pivotal role in forecasting future volatility in international copper prices, but not vice versa. Third, negative dynamic correlations between copper prices and China's stock returns around the 2008 global financial crisis suggest that a copper asset can hedge the risk of stock investment in China. Our results have important implications for investors, portfolio managers, and Chinese policymakers, who should regulate extreme financial speculation in copper to minimize the impact of excess volatility on the real economy.

Forecasting Base Metal Prices with Commodity Currencies

In this paper we show that the Chilean exchange rate has the ability to predict the returns of the London Metal Exchange Index and of the six primary non-ferrous metals that are part of the index: aluminum, copper, lead, nickel, tin and zinc. The economic relationship hinges on the present-value theory for exchange rates, a floating exchange rate regime and the fact that copper represents about a half of Chilean exports and nearly 45% of Foreign Direct Investment. Consequently, the Chilean peso is heavily affected by fluctuations in the copper price. As all six base metal prices show an important comovement, we test whether the relationship between copper prices and Chilean exchange rates also holds true when it comes to the six primary non-ferrous metals. We find interesting evidence of predictability both in-sample and out-of-sample. Our paper is part of a growing literature that in the recent years has evaluated and called into question the ability of commodity currencies to forecast commodity prices.

Mineral commodity prices and economic growth: a case of Zimbabwe

The study investigated the relationship between mineral commodity prices and economic growth in Zimbabwe using autoregressive distributive lag (ARDL) framework. Theoretical literature says that the impact of commodity prices on economic growth depends on whether a country is a net commodity exporter or importer. Subsequent empirical studies showed quite conflicting and divergent views on the relationship between mineral commodity prices and economic growth. Moreover, prior research works on mineral prices-economic growth nexus have eluded Africa and Zimbabwe in particular. The F-bounds test found out that only the relationship between copper prices and economic growth were co-integrated. The findings of the causality test are twofold: changes in copper prices only affected Zimbabwe's economic growth in the long run whereas economic growth in Zimbabwe only influenced copper prices in the short run. The implication is that Zimbabwean authorities should invest more towards copper extraction in order to economically benefit from increase in copper prices.

Mineral commodity prices and economic growth: a case of Zimbabwe

The study investigated the relationship between mineral commodity prices and economic growth in Zimbabwe using autoregressive distributive lag (ARDL) framework. Theoretical literature says that the impact of commodity prices on economic growth depends on whether a country is a net commodity exporter or importer. Subsequent empirical studies showed quite conflicting and divergent views on the relationship between mineral commodity prices and economic growth. Moreover, prior research works on mineral prices-economic growth nexus have eluded Africa and Zimbabwe in particular. The F-bounds test found out that only the relationship between copper prices and economic growth were co-integrated. The findings of the causality test are twofold: changes in copper prices only affected Zimbabwe's economic growth in the long run whereas economic growth in Zimbabwe only influenced copper prices in the short run. The implication is that Zimbabwean authorities should invest more towards copper extraction in order to economically benefit from increase in copper prices.

Chile: 2018 Article IV Consultation-Press Release; Staff Report; and Statement by the Executive Director for Chile

The Chilean economy is recovering from a prolonged slowdown that started with the decline in copper prices in 2011 and intensified over the past two years. The new administration, which took office in March, aims at reinvigorating investment and economic growth through structural reforms, but a divided Congress may constrain the reform space.

An Analysis on the Impact of Commodity Prices and Exchange Rates on the Price of Bitcoin

Bitcoin is the first decentralized cryptocurrency to be traded. There has been drastic increase in the price of bitcoin since 2013. Granger Causality analysis has been carried out to examine whether the price of commodities and the exchange rates helps in predicting the future price of bitcoin. For this study, the price of bitcoin, commodity prices and exchange rates have been considered from Jan 2103-Sep 2017. After the analysis it can be concluded that the price of commodities and the exchange rates does not help in predicting the future price of bitcoin. The past data of the price of bitcoin helps in predicting the future price of copper and British pound exchange rate with that of U.S dollars. Using Regression analysis, it can be determined that when the price increases by 0.0084 dollars there is one unit increase in the volume of transaction. Using variance analysis it can be observed that the price of bitcoin is more volatile compared to the price of commodities and the exchange rates.

Оценка мультипликатора государственных расходов в Республике Армения

The purpose of this article is to estimate government expenditure multiplier in the Republic of Armenia via a SVAR model. The model is evaluated using the data over the period from 2004 to 2017 and accounts for a structural break, which occurred in the year 2009. In addition, it controls for the dynamics of copper prices and output of the Russian Federation. The results indicate that the response of output to the government expenditure shock is quite strong right after the discretionary change of fiscal policy. The multiplier reaches its peak on impact with the estimated value about 3. However, there is a substantial uncertainty regarding the exact value of the multiplier and the 95 % lower bound estimate is only 0.8. Moreover, the value of the government expenditure multiplier decreases over time rather quickly. In the medium term the impact of the government expenditure shock on output is negligible. Overall, the authors conclude that government expenditure policy can be used successfully to stabilize the dynamics of output in the event of substantial recession or boom. Estimates obtained in this study do not contradict the results of previous researches dedicated to the evaluation of fiscal multipliers in the Republic of Armenia.

Prediction of Gold Prices Using Artificial Neural Networks

Gold, which has fulfilled the functions that money has in the past years, is now mostly used as a means of saving. Gold has been an investment instrument for Turkish society for many years. In addition, it is perceived as the most reliable means of investment by people in times of crisis and war. It is very important to estimate gold prices for investors who want to earn high profits from their investments. In this study, as input (independent variables), the data set consisting of Brent oil price, USD price, BIST100 index, Central Bank of the Republic of Turkey weekly interest rate, silver and copper prices was applied to the price of gold in ounce by applying Multi-Layer Perceptron Neural Network (MLPNN) is intended to be estimated. As the data set, weekly price and index values were used between January 2010 and December 2016 period. As a result of the study, suggestions were made about the effectiveness of gold on the input variables created by using MLPNN.

Copper price determination: fundamentals versus non-fundamentals

In mineral economics, there is quite broad consensus that market fundamentals (physical supply and demand) explain commodity price fluctuations, particularly in the medium and long term. However, following the recent price boom, some dissent has arisen about the role played by non-fundamentals such as liquidity or money supply in key countries and regions, the financialization of commodities and, particularly, financial speculation. This paper develops an empirical model for the copper market, consisting in a vector autoregression (VAR) with 16 variables, including both fundamentals and non-fundamentals. Since the variables’ impact probably changes over time, the 20 years studied are divided into three periods (1995–2003, 2003–2008, and 2008–2015) separated by two structural breaks related to the Chinese boom and the financial crisis/Great Moderation. The results show that, although the fundamentals are relevant in all the periods analyzed, liquidity and other macroeconomic variables are also necessary in order to understand the level of copper prices and their fluctuations. In the case of financial speculation, the results indicate that its impact was significant only in 2003–2008 and, even then, was smaller than that of the fundamentals and macroeconomic variables, explaining around 9% of the price increase in this period. The results support the conclusion that, for the purposes of modeling and forecasting, current models based only on the fundamentals cannot fully explain price dynamics which are shown to be, in general, more complex than has been assumed by mainstream mineral economics.

Monetary Policy and the Copper Price Bust: A Reassessment of the Causes of the Panic of 1907

We find evidence that the runs on banks and trust companies in the Panic of 1907 were linked to the Bank of England’s contractionary monetary policy actions taken in 1906 and 1907 through the medium of copper prices. Results from our VAR models and copper stockpile data support our argument that a copper commodity price channel may have been active in transmitting the Bank’s policy to the New York markets. Archival evidence suggests that the plunge in copper prices may have partially triggered both the initiation and the failure of an attempt to corner the shares of United Copper, and in turn, the bank and trust company runs related to that transaction’s failure. We suggest that the substantial short-term uncertainties accompanying the development of the copper-intensive electrical and telecommunications industries may have also played a role in the sharp drop in copper prices. Additionally, we find evidence that the copper price transmission mechanism was also likely active in five other financial systems that year. While we do not argue that copper caused the 1907 crisis, we suggest that it was an active policy transmission channel amplifying the classic effect that was already spreading through the money market channel. If, as we argue, the bust in copper prices partially provided a trigger to that bank panic, then it provides additional evidence that contractionary monetary policy may have had an unintended, adverse consequence of contributing to a bank panic and, therefore, supports other recent findings that monetary policy deliberations might benefit from considering the policy impact on asset prices.

Copper price estimation using bat algorithm

The most effective parameter on the value of mining projects is metal price volatility. Therefore, knowing the metal price volatility can help the managers and shareholders of the mining projects to make the right decisions for extending or restricting the mining activities. Nowadays, classical estimation methods cannot correctly estimate the metal prices volatility due to their frequent variations in the past years. For solving this problem, it is necessary to use the artificial algorithms that have a good ability to predict the volatility of the various phenomena. In this paper, the Bat algorithm was used to predict the copper price volatility. Accordingly, Brownian motion with mean reversion (BMMR) was chosen as the most suitable time series function with the root mean square error (RMSE) of 0.449. Then, the estimation parameters of the equation were modified using Bat algorithm. Finally, it is concluded that the determined equation with 0.132 of RMSE can predict the copper price better than the classic estimation methods.

The effect of mine aging on the evolution of environmental footprint indicators in the Chilean copper mining industry 2001–2015

Abundant evidence suggests that the massive global exploitation of copper mines over the last two decades led to mine aging expressed as ore grade reduction, deepening of open pits and underground operations, hardening of the rock, and increasing stripping ratios. These processes have affected the evolution and change of key environmental footprint indicators. Copper ore grade decline seems the most important of these factors and depends on variables including the rate of extraction, ore deposit geological features, introduction of new technologies, the copper price and its co-products, and the discovery and exploitation of new ore deposits. From 2001 to 2015 Chile increased its copper production by 22% and produced overall 80 million tons of copper, more than it produced in the entire 20th century. This paper explores the effects of mine aging on three key environmental footprint indicators: energy and water consumption, and greenhouse gas emissions. Electric energy consumption per ton of copper should grow in the coming decade at a significantly slower rate than in the last fifteen years because of the slowdown of the ore grade decline in mill concentrators. Additionally, a steeper decrease is expected for the emission of greenhouse gases per ton of copper.

Copper mining productivity: Lessons from Chile

Chile represents almost one third of the world’s copper production. Mining is one of the main industries that contributes to our country’s development with resources and is globally recognized. Due to the end of the commodity cycle, improving productivity will be a key variable in mining performance in incoming years. This paper studies mining productivity in Chile by relying on two indicators: measure of the total factor productivity (TFP) using the traditional Solow methodology, and labor productivity. Since 2000, we found a decrease in TFP, explained mainly by the participation of capital as well as diverse factor adjustments to labor and capital inputs. Average labor productivity also decreases 42% from 1999 to 2010, a decrease explained by four determinants: real mining wages, electricity prices, copper prices and mineral grade. Since 2010, average labor productivity has increased 30%, and there is also an opportunity for additional improvement by reducing energy costs as well as by aligning productivity and labor performances.

Ethnicity, politics and Zambian youth

ABSTRACTZambia is a very young nation in the midst of economic and political turbulence. The current trials are exacerbated by a youth bulge. Zambian youth have been exposed to a globalising world, and their expectations often exceed the current realities of the country. The country's economy has been decimated by daily power outages and falling copper prices. The fragile democracy has seen ethnic politics expand in the face of two presidential elections in less than two years. There have been concerns about the potential for increased political and ethnic violence as a result of these financial and political challenges. In this article, we draw upon a survey of 419 public university students in order to uncover how ethnic identities contribute to the democratic landscape for the nation's youth. We find that Zambian youth consider ethnicity to be an important part of personal identities, but they do not believe ethnicity should be a political factor. Contrary to this desire, the youth overwhelmingly perceive politicians to be engaging in political tribalism. The data suggest that the youth's widespread dissatisfaction with the perceived ethnic politics has contributed to lower voter turnouts, especially among Zambia's largest tribe.

AN ANALYSIS OF THE IMPACT OF EXTERNAL FINANCIAL RISKS ON THE SOVEREIGN RISK PREMIUM OF LATIN AMERICAN ECONOMIES

This article presents a quantification of the response of the sovereign risk premium (EMBI) of a group of Latin American countries, to unexpected changes (shocks) in external financial variables. The main contribution of the paper is to use the estimated parameters of a vector autoregression (VAR) model using a special Cholesky variance-covariance decomposition as a tool for risk scenario’s assessment. The proposed interpretation of the estimated matrix allows for the quantification of the impact of more than one shock and also to quantify spillovers. A VAR is estimated for each country (Colombia, Chile, Mexico, and Peru) in monthly frequency that includes China’s and Brazil’s EMBI, the global volatility index (VIX), plus the value of the dollar against a basket of currencies (Broad Index) and a proxy of the slope of the US Treasury yield curve (Spread US). The VIX and Broad Index shocks turn out to have a relatively homogenous effect on each country’s EMBI, while shocks to the China and Brazil EMBI are more heterogeneous. For the case of Chile, we further study three alternative risk scenarios, incorporating the copper price as an additional variable. The most disruptive scenario at the time when the shock hits is the “Volatility driven” one. Nevertheless, it is the “Emerging markets” scenario (namely one with simultaneous shocks to China’ and Brazil’s EMBI) the one with the most harmful dynamics, as it dyes out slower. Finally, a “Copper price bust” scenario, in which the price of copper drops significantly in addition to a shock to the EMBI China, is the one with the least effect as the price of copper is relatively less affected by shocks to other variables, displaying lower spillovers.