Metal Prices Research Articles

EWMA modeli kullanarak Covid-19 dönemi oynaklık yayılım etkisi analizi: BİST sağlık sektörü hisse senetleri örneği

The importance of the health sector is once again understood by the emergence of the Covid-19 pandemic. The purpose of this study is to examine the volatility spillover effect between health sector stocks traded in Istanbul Stock Exchange and exchange rate and precious metal prices during the pre-Covid-19 period and the Covid-19 period. For this purpose, the volatility of returns of four health sector stocks traded on the Istanbul Stock Exchange, foreign exchange rate, and the price of gold were obtained using the Exponential Weighted Moving Average model and used in the Diebold-Yılmaz Spillover Index approach. The data set is divided into two periods according to the date of the first cases seen in Turkey. While the first period consisted of 267 observations between January 2, 2019, and February 28, 2020, the second period consisting of 267 observations was created between March 2, 2020, and April 1, 2021. According to the results, the total spillover index in the period before Covid-19 is 9.60%, which indicates a low connectedness between markets. The spillover index for the Covid-19 period is calculated at 21.90% which means the error variances in markets are on average 21.90% originated from other markets. Moreover, it is found that RTA Laboratories has the highest net spillover in the Covid-19 period.

Nanocasted perovskites as potential catalysts for acetalization of glycerol

Herein, we report on the catalytic activity of ABO3 perovskites (A = La, Sr or Sn, B = Co, Mn, Sn, Fe, or Al) synthesized by nanocasting using silica-based KIT-6 and SBA-15 as hard templates. The search for less expensive and easy to synthesize active catalysts is gaining momentum due to global financial markets that escalate the prices of precious metals. Interestingly, alternatives that utilize affordable commodities than precious metals have attracted interest. The biodiesel industry is playing a more central part in fuel economy in many developing economies. This work focuses on circumventing the drawbacks of the bio-fuel industry by valorising glycerol, a by-product of biodiesel production into value-added chemicals through the acetalization reaction. The as-synthesized perovskites, with surface areas ranging from 19 to 99 m2/g, showed up to 100% glycerol conversion and selectivity towards 2-methylol-1,4-dioxaspirol [4,5] decane. Perovskites containing tin (Sn) showed better catalytic activity than the rest of the catalysts. The NH3-TPD results suggest that the greater catalytic activity observed in tin-based perovskites is mainly due to their high acidity content. Furthermore, we showed that the catalyst reusability can be studied in-depth using kinetics and our catalyst showed good stability in the first two cycles.

Energy Transition Metals

The energy transition requires substantial amounts of metals such as copper, nickel, cobalt and lithium. Are these metals a key bottleneck? We identify metal-specific demand shocks, estimate supply elasticities and pin down the price impact of the energy transition in a structural scenario analysis. Metal prices would reach historical peaks for an unprecedented, sustained period in a net-zero emissions scenario. The total value of metals production would rise more than four-fold for the period 2021 to 2040, rivaling the total value of crude oil production. Metals are a potentially important input into integrated assessments models of climate change.

Multi-step metal prices forecasting based on a data preprocessing method and an optimized extreme learning machine by marine predators algorithm

The prediction of metal prices can provide references of future investment and decision-making for mining projects and affiliates. However, the improvement of metal prices prediction accuracy is still a highly challenging task owing to the complex influencing factors and nonlinear features. Most previous models usually overlook the significances of model optimization and data preconditioning, and often fail to obtain satisfactory results. Therefore, this study designed a hybrid model for multi-step copper and gold prices prediction. First, a robust data processing algorithm was introduced to decompose time series into several modes, which can effectively identify and mine the main characteristics of metal prices time series. Second, to enhance the prediction performance and conquer the limitations of individual models, nature-inspired metaheuristic algorithms are adopted to tune the parameters of these forecasting models. Third, two metal price data sets, hypothesis testing, seven model performance evaluation indices and several experiments are used to comprehensively compare and analyze the prediction ability of the proposed model. Finally, the results revealed that the developed hybrid model can not only significantly outperform the comparison models considered in this study, also offer effective and valuable suggestions for the related policymakers, investors and metal production and trading enterprises in metal market. • Multi-step metal prices prediction is performed using a hybrid forecasting model. • A novel nature-inspired metaheuristic algorithm is used to tune forecasting models. • The proposed hybrid model has a higher prediction ability than comparison models. • Three experiments confirmed the effectiveness of the proposed hybrid model.

State financing of companiesmetallurgical industry in conditions of volatility of world prices for non-ferrous metals

Currently, in the conditions of continuing sanctions pressure, as well as the consequences of the SARS-CoV-2019 pandemic, the metallurgical industry companies are facing acute issues of forming an effective structure of monetary capital and, in this regard, financial stability, since the provision of fixed and working capital that contribute to generating profits and increasing the value of the company as a whole will directly depend on the balance and cost of funding sources. The structure and cost of sources of financing of the monetary capital of companies in the non-ferrous metallurgy industry are directly influenced by the conjunctural factors of the industry, which determine the value of non-ferrous metals on the London Metal Exchange (LME) and on the Russian market. Due to the cyclical nature of economic development and the associated manifestations of crisis periods, there is a negative trend in terms of providing monetary capital with its own sources of financing against the background of the growth of borrowed capital and an unsatisfactory level of financial stability. In this regard, it seems appropriate to turn to the analysis of measures of state support for strategically important companies in the non-ferrous metallurgy industry in order to maintain domestic industry and maintain competitiveness in the Russian and world markets of non-ferrous metals. At the same time, public financing will contribute to leveling the manifestation of sectoral factors, the formation of an effective capital structure and the achievement of optimal financial stability.

Common factors and the dynamics of industrial metal prices. A forecasting perspective

This study aims to analyze the suitability of factor models in describing the dynamics of real prices for four main non-ferrous industrial metals: aluminium, copper, nickel and zinc. For that purpose, using an extensive dataset of monthly time series covering the years 1980–2019, we extract four different common factors explaining commodity prices, exchange rates, financial and macroeconomic indicators. Next, we examine these factors as potential predictors of the movements of four metal prices with the use of two model classes: direct forecasts (DF) and factor augmented vector autoregressions (VAR). We show that for three out of four metals (aluminium, nickel and zinc) VAR models provide relatively good point and density forecasts, outperforming the random walk benchmark as well as DF models.

Analysis of major properties of metal prices using new methods: Structural breaks, non-linearity, stationarity and bubbles

This study investigates major properties of prices of metals (gold, silver, platinum, palladium and copper) such as structural breaks, non-linearity, stationarity, and bubble incidences during 1990–2021 For robustness of results, both daily and weekly data sets were used, while two conventional unit root tests and two recent stationarity tests (including a new one) that account for structural breaks and non-linearity were also employed. The recently introduced method of identifying and detecting price bubble was also used. Preliminary tests show that prices of the metals exhibit structural breaks and non-linearity during the study period. The traditional unit root tests reveal that prices of all the metals are not stationary I(1). The application of two unit root tests, that account for structural breaks and non-linearity, produced stationary results I(0) for the metals’ prices in all the periods. However, the result of new method is more robust than the earlier one especially when weekly data is considered alongside daily data for Platinum and Silver. Some incidences of price bubbles were also detected in the metal market during the period. A number of policy recommendations deduced from the empirical results are well discussed in the concluding part of the paper.

Detecting speculative bubbles in metal prices: Evidence from GSADF test and machine learning approaches

The importance of metal prices to real economic activity and financial markets has increased the focus on detecting price bubbles in precious and industrial metals. Several studies looked at the influence of macroeconomic factors in the formation of a single metal bubble and tried to identify bubble dates. Our study extends the literature and analyzes monthly gold, platinum, palladium, rhodium, silver, and aluminum, copper, lead, nickel, steel, tin prices over 1980M1-2019M12, and contributes to the literature in two ways: First, the analysis incorporates the Generalized Supremum Augmented Dickey-Fuller (GSADF) test to detect potential bubbles. Second, the study evaluates the impact of potential financial, real, and speculative factors in the likelihood of price bubbles using the random forest method. Our findings indicate that financial factors are more critical in predicting precious metal price bubbles. The monetary policy rate and the production index are important to predict bubbles in industrial metal prices. However, our findings suggest that speculative activity may not adequately predict metal price bubbles.

Cs2HfCl6 doped with Zr: Influence of tetravalent substitution on scintillation properties

Homeland security is an emerging application field for inorganic scintillators. Detection and identification of radioactive materials in both individual and cargo transport requires scintillators with excellent energy resolution and high light output. On the other hand, the time characteristics of the scintillation are less crucial. Furthermore, price is a key parameter due to the large volume of the detectors. State-of-the-art halide scintillators e.g. SrI2:Eu or LaBr3:Ce can satisfy the requirements for light yield and energy resolution, but the production costs are very high due to complicated crystal growth and the high prices of rare earth metals. Cesium hafnium chloride (Cs2HfCl6) is a promising intrinsic scintillator with high light yield and excellent resolution. Moreover, its low hygroscopicity and cubic crystal structure could allow lower production costs compared to SrI2 or LaBr3. Intrinsic luminescence of Cs2HfCl6 is commonly attributed to self-trapped excitons. However, the luminescence of Cs2HfCl6 shows complex structure and temperature dependence. This might be explained by the presence of zirconium which is a common impurity in hafnium compounds. We present a study of the influence of zirconium doping on the scintillation properties of Cs2HfCl6. Single crystals of Cs2HfCl6 with zirconium doping from 0 to 1 mol% were grown by the vertical Bridgman method. Their phase purity, radioluminescence, scintillation decay, light yield, and afterglow were evaluated. Results show that zirconium doping can affect the scintillation properties of Cs2HfCl6 even at concentrations comparable to impurity levels of zirconium in hafnium compounds.

Electron Density Modulation of MoO2/Ni to Produce Superior Hydrogen Evolution and Oxidation Activities.

Hydrogen evolution reaction (HER) and hydrogen oxidation reaction (HOR) have aroused great interest, but the high price of platinum group metals (PGMs) limits their development. The electronic reconstruction at the interface of a heterostructure is a promising strategy to enhance their catalytic performance. Here, MoO2/Ni heterostructure was synthesized to provide effective HER in an alkaline electrolyte and exhibit excellent HOR performance. Theoretical and experimental analyses prove that the electron density around the Ni atom is reduced. The electron density modulation optimizes the hydrogen adsorption and hydroxide adsorption free energy, which can effectively improve the activity of both HER and HOR. Accordingly, the prepared MoO2/Ni@NF catalyst reveals robust HER activity (η10 = 50.48 mV) and HOR activity (j0 = ∼1.21 mA cm-2). This work demonstrates an effective method to design heterostructure interfaces and tailor the surface electronic structure to improve HER/HOR performance.

ҚАЗАҚСТАН РЕСПУБЛИКАСЫ ҰЛТТЫҚ ЭКОНОМИКАСЫНЫҢ ҚАЗІРГІ ЖАҒДАЙЫН БАҒАЛАУ

Abstract: despite the relative decline in Kazakhstan's gross domestic product in recent years, more than half of the increase is directly or indirectly attributable to the commodity economy, including transportation, primary processing and trade in raw materials. The growth of the commodity economy is promoted by favorable prices for oil and metals, as well as the growth of their production, which, in turn, allows growth in related and other sectors of the economy. From an economic point of view, the current situation in Kazakhstan is stable. Although the quality is not in full measure, but it has economic growth, that is, in the article the authors are looking for answers to these questions. These questions were justified in the article. In addition, the article evaluates statistical data for the period from 1995 to 2020 on the share of GDP per capita in the Republic of Kazakhstan and the dynamics of GDP at current prices. The article considers that in the development of the national economy of Kazakhstan, the manufacturing sector of the economy requires more state support than the extractive sector, and today the state of the world economy and the conditions of its functioning are evaluated as variables. He identifies three main features underlying the analysis of the economies of the most developed and competitive countries, i.e. the presence of a highly developed financial sector; a high-tech economy based on the improvement of technologies; and the presence of powerful multinational corporations in the country. In addition, the article analyzes the impact of increased volatility in the external sector on the domestic economy in February 2020 against the background of warning of the negative impact of the Covid-19 coronavirus pandemic on the global economy. The article also highlights the individual elements of the structure of the national economy, characterized by the actual system of relations, and considers the main factors that influence the formation of the structure of the national economy. In the article, the authors also emphasize that the main key parameters of the budget policy have been revised within the framework of anti-crisis measures, and significant expenditures are provided for the implementation of these measures.

Forecasting of the U.S. Steel Prices with LVAR and VEC Models

Base metal prices, especially steel, play a significant role in industrial economics, making them worth knowing about future values. In most cases, we expect superior performance from multivariate forecasting models comparing univariate methods due to the involvement of explanatory variables in the system. Standard vector auto regressive model can only capture short-run dynamics because of the differencing process for non-stationary series that eliminates the possible long-run relationship. Instead, performing non-stationary series on levels through the vector auto-regressive framework does not suffers such loss. Moreover, the vector error correction model can define both short-term and long-run dynamics explicitly. These models can yield more robust forecasts in the mid-term and long-term by investigating short-run and long-run relationships simultaneously. The current study aims to perform an out-of-sample forecast for the United States steel prices index 18 months ahead using cointegrated variables. The results suggest that the non-stationary vector auto-regressive model outperforms the vector error correction model regarding mean absolute percentage error and root mean square error as forecast accuracy measures.

Effects of non-ferrous metal prices and uncertainty on industry stock market under different market conditions

Increasing financial attributes of non-ferrous metals after financialization and higher uncertainty lead to a more complicated stock market of non-ferrous metal industry, while no study has explored the relations among the three under a unified framework. This paper attempted to investigate the effects of non-ferrous metal prices and uncertainty on stock indices of non-ferrous metal industries in China under different market conditions. Results show that: (1) non-ferrous metal prices have positive impacts, indicating that the hedging effects of non-ferrous metals are weaker after financialization; (2) the impacts of uncertainty are negative at the mean level or bearish market, but positive under the bullish markets; (3) the impact strength of non-ferrous metal prices and uncertainty are stronger under lower and upper quantiles than intermediate quantile; (4) effects of non-ferrous metal prices and uncertainty are more significant and stronger after the global financial crisis in 2008. These findings imply the importance of non-ferrous metal prices and uncertainty for coping with fluctuations of non-ferrous metal stock markets, making investment portfolios, formulating targeted strategies, and maintaining financial security under different market conditions.

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.

Commodity price pass-through along the pricing chain

This paper analyses the commodity price pass-through along the pricing chain for the global commodity price index and the indices of its main categories (i.e., agricultural raw materials, food and beverages, energy and metals) in the world, advanced and emerging economies. To do so, the study considers country-by-country vector autoregression models and pool the results by taking weighted means for 18 advanced economies and 19 emerging countries, as well as for the world (defined as the sum of advanced and emerging economies). The results show the following: (i) there is evidence in favour of partial pass-through from commodity prices to producer prices, although the evidence for the pass-through to consumer prices is less evident; (ii) the pass-through in the world seems to be led by both advanced and emerging countries for producer prices and only by advanced economies for consumer prices; (iii) higher prices in the four categories (agricultural raw materials only in the short-run) induce significant higher producer prices in almost all cases, with shocks in the prices of energy and metals showing the largest effects; and (iv) energy prices explain the highest variability of producer and consumer prices.

Using the Three Co's to Jointly Model Commodity Markets: Co-Production, Co-Consumption and Co-Trading

In this study, we develop a framework, based on the Global Vector Autoregression Model (GVAR), to unite two differing perspectives on commodity markets, the single-market centered approach, investigating the micro- and macroeconomic drivers of commodity prices, as well as the inter-market perspective, which observes joint movements of commodity prices, beyond macroeconomic variables, on exchanges. The strong co-movement between industrial metal prices is represented exceptionally well by our framework, which connects the markets using co-consumption, co-production and co-trading data. While the microeconomic supply and demand attributes have no significant impact on individual market level, indicating a contradiction to the single-market centered approach, the cross-commodity dimension reveals these attributes are determining, even on price variables.

Toward the Operability of Flotation Systems under Uncertainty

The purpose of this work was to analyze the requirements for the operational feasibility of flotation systems as well as the effects of the selection of flotation equipment and metal price uncertainty. A procedure based on mathematical optimization and uncertainty analysis was implemented to achieve this aim. The optimization included flotation and grinding stages operating under uncertainty, whereas the uncertainty analysis considered the Monte Carlo method. The results obtained indicate a small number of optimal flotation structures from the economic point of view. Considering the relationship between the economic performance and metallurgical parameters, we established that these structures exhibited favorable conditions for operating under uncertainty. Such conditions were proportional to the percentages representing each structure in the optimal set; i.e., a higher percentage of a structure implied a greater capacity to face operational and metal price changes. The set of optimal structures included configurations implementing cell banks, flotation columns, or both, indicating the influence of the flotation equipment type on the optimal structures. We also established the influence of metal price on the number of optimal structures. Therefore, the results obtained allowed us to separate the design of the flotation systems into two stages: first, a set of optimal structures exhibiting favorable conditions for facing uncertainty is determined; second, the optimal operation is established via resilience/flexibility approaches after the previous determination of the equipment design parameters.

Anti-Corrosive Effect of Jordanian-Bay- Leaves Aqueous Extract on Mild Steel in 1.0 M Hydrochloric Acid Solution

Natural inhibitors are the most crucial manner to lessen the corrosion price of various business metals. There are several strategies being used with corrosion inhibitors. One technique being used is the Electrochemical strategies. The gain with this technique is their brief size time and mechanistic information. It is beneficial in the corrosion's layout safety techniques besides the layout of the brand new inhibitors, there are 3 styles of corrosion inhibitors as anodic inhibitors, cathodic inhibitors, over one inhibitor. The corrosion inhibition of slight metallic in 1.0 M HCl solution with the aid of using Jordanian -Bay- leaves extract has been studied with the use of potentiodynamic polarization technique. Results received a display that Bay- leaves aqueous extract behaves as an anodic inhibitor for slight metallic in 1.0 M HCl solution. The inhibitor capabilities thru adsorption following Temkin adsorption isotherm. The impact of parameters like temperature and inhibitor awareness at the corrosion of slight metallic has additionally been studied.

Does the risk aversion of crude oil market investors have directional predictability for the precious metal and agricultural markets?

PurposeRisk aversion is considered as an important factor in predicting asset prices. Many studies have proved that there exists important price information spillover among crude oil, precious metals and agricultural markets. Then there naturally follows the question: Is the risk aversion of investors in crude oil market predictable for the returns of precious metals and agricultural products? The purpose of this paper is to answer this question. For this reason, the authors explore the directional predictability and the cross-quantile dependence between risk aversion of crude oil market investors and returns of precious metals and agricultural products.Design/methodology/approachTo better describe the risk aversion of investors, this paper uses high-frequency data and model-free calculation method to obtain variance risk premium of crude oil. Then, this paper uses the cross-quantilogram method to investigate the directional predictability and cross-quantile dependence between risk aversion of crude oil market investors and returns of precious metals and agricultural products. Meanwhile, it employs the partial cross-quantilogram (PCQ) method to test the impact of control variables on the empirical results.FindingsFirstly, risk aversion of crude oil market investors has directional predictability for returns of precious metals and agricultural products. Secondly, different degrees of risk aversion of crude oil market investors have different impacts on returns of precious metals and agricultural products. A low (high) degree of crude oil market investors' risk aversion has negative (positive) predictability for returns of precious metals and agricultural products. Finally, during the sample period, the returns of precious metals are more affected by risk aversion of crude oil market investors than returns of agricultural products.Originality/valueFirst of all, this paper studies the impact of risk aversion of crude oil market investors on returns of precious metals and agricultural products. It updates previous relevant studies on the factors influencing the prices of precious metals and agricultural products, and provides a new idea for the forecast of those commodity returns. Secondly, this paper provides the evidence that different degrees of risk aversion of investors have different effects on the returns of commodities, and expands the research on the topic of commodity returns prediction. Finally, high-frequency data are employed in this paper to better capture the risk aversion of investors than commonly used daily data.