Metal Prices Research Articles

Устойчивость экономик российских регионов к пандемии 2020

The study is devoted to assessing the resilience of Russian regions’ economies to the 2020 pandemic and analysing its factors. We evaluate the regional economies’ resilience using a set of per capita indicators, such as budget revenues and household incomes, production in the main sectors of the economy, and investments in fixed assets. We use two alternative methods for assessing the resilience of a region to an external shock: the modified method of R. Martin and R. Lagravinese, based on a comparison of regional trends with country-wide ones, and the author’s approach, based on a comparison of pandemic indicators with non-pandemic forecasts of the region. All particular resilience indicators are normalized and reduced to an integral indicator. The study revealed the most significant resilience to the pandemic of some underdeveloped Russian regions, which received substantial government support and several border territories and Far Eastern regions, which benefited from the increase in metal prices. The more developed and large economies and the centres of the extractive industry demonstrated the lowest resilience. The calculation of correlations between resilience indices and regional development indicators confirmed the impact of the sectoral and institutional structure of the economy on its resistance to the shock. By building an econometric model estimated by the OLS method, we confirmed the positive impact of the share of employment in government-owned and municipal enterprises and the negative impact of the degree of openness, the size of the economy and the share of the mining industry on the regional economies’ resilience. The conducted research is applicable in developing a balanced inter-budgetary, regional and sectoral policy in the context of economic crises of a pandemic type

A quantile regression approach to study the impact of aluminium prices on manufacturing sector of India during COVID era

Metal prices have a significant impact on the manufacturing sector. Aluminium as raw material and an essential component of various manufacturing processes have a considerable effect on the cost and price of manufactured items. Therefore, the present study focuses on the influence of aluminium price movements on India's manufacturing sector during the COVID period. The current research is empirical and examines the causal relationship between aluminium prices and the manufacturing index in India during the COVID era. Different methodologies are used to study aluminium price fluctuations. Many earlier studies focused on forecasting aluminium prices, while others looked at the relationship between aluminium and other indices or commodities. The current research uses a quantile regression approach to accomplish the study's objective. Quantile regression is a nonlinear data model that may provide lower or higher results than the conditional mean. This model is appropriate in situations when linearity is not expected. The study's findings are helpful for companies processing or using aluminium in their production processes, all manufacturing industries where aluminium is a crucial component of raw material, and regulators and policymakers working on boosting the aluminium industry and other stakeholders.

Experimental Investigations into Machining Characteristics of Niobium Carbide Cermet with µECM / hybrid laser-µECM.

Niobium carbide (NbC) is a high hardness 1960 (HV30) cermet material which finds applications in the moulds for ceramic injection moulding as well as inserts for cutting tools. This material possesses excellent combination of hardness, toughness and thermal shock resistance. The use of nickel (Ni) binder makes it a promising material for such applications, especially in view of rising cobalt (Co) metal prices used as binder in conventional tungsten carbide (WC-Co) cermet. The tool-wear and tool-failure issues make it difficult for mechanical machining technologies to process this material, especially in the micromachining regime (< 2 mm). To address machinability challenges, electrically conductive Nb cermet has been developed inhouse with Ni as the binder material. This makes it a suitable workpiece candidate for electrochemical machining (ECM) and electrical discharge machining (EDM) processes.Therefore, experimental investigations are presented in this work to evaluate the machining characteristics of NbC cermet with micro-ECM process. Channels have been used as test-features for evaluating machinability. Furthermore, two different grades of NbC cermets have been machined to identify the effect of WC addition on the dissolution characteristics and possible passivation during ECM. A micro-second pulsed voltage source has been utilized for controlled and localized material removal. To further enhance removal mechanisms in terms of processing speed and weakening of passivating layer, the micro-ECM process has been assisted with a nano-second pulsed laser on an in-house developed hybrid machine-tool. The aim is to uniformly dissolve the NbC matrix and Ni binder with ECM process for a high integrity machined surface. The dissolution and passivation characteristics have been further analysed through a combination of current pulse acquisition and channel profile characterisation. Linear sweep voltammetry and channel machining revealed that the addition of WC has a significant impact on the stability of passive layer. Laser assistance could enhance the material removal for both the NbC grades and promote uniform dissolution, with the effect being more pronounced in the NbC grade without WC (41 % increase in average pulse peak current) due to the weaker passive film. The material dissolution was more localized in LECM of NbC grade with WC due to the strong passivation along the channel walls.

Comparison of Exponential Smoothing Methods in Forecasting Global Prices of Main Metals

Comparison of Exponential Smoothing Methods in Forecasting Global Prices of Main Metals

Внешняя торговля Дальневосточного федерального округа: региональные особенности в условиях пандемии COVID-19

The article reviews estimates of foreign trade indicators of the Far Eastern Federal District during the COVID-19 pandemic in 2020–2021. The factors of the dynamics of foreign trade indicators of the macroregion are determined, the commodity, territorial and geographical structures of exports and imports are examines. COVID-19 pandemic restrictions caused a large-scale decrease in economic activity and in volume of foreign trade of the Far Eastern Federal District at the beginning of the pandemic. The situation stabilized in late 2020 – early 2021. Dramatic reduction in the volume of exports of the Far Eastern Federal District in 2020 was due to a decrease in the prices of oil, natural gas, and metals. A decrease in supplies, in the level of economic activity in the country and effective demand under COVID-19 pandemic restrictions caused a decrease in the volume of imports of the Far Eastern Federal District in 2020. The volume of export of the Far Eastern Federal District recovered in 2021 due to a significant increase in prices for most goods exported by the macroregion. The volume of imports increased due to high prices and increased demand for imported products. The commodity, territorial and geographical structures of foreign trade of the Far Eastern Federal District have not changed in 2020–2021. The export of the macroregion mainly consisted of the products of fuel and energy, mineral resources, fisheries and forestry complexes. Machinery and electrical equipment, vehicles, metals, chemical and food products were the basis of the macroregion’s imports. The leaders in foreign trade turnover were the Sakhalin Region, Primorsky Krai, the Republic of Sakha (Yakutia), Khabarovsk Krai. China, the Republic of Korea and Japan have been the main partners for many years, which determined the volume of exports and imports and the structure of foreign trade of the Far Eastern Federal District. A significant redistribution of export supplies from the Far Eastern Federal District between the countries occurred in 2020–2021 due to the China’s restrictive measures on imported products, as well as geopolitical problems of China

Policy Uncertainty, Oil Price, Stock Market and Precious Metal Markets Volatility Spillovers in the Russian Economy

The Russian economy is emerging, meaning that natural resources play a dominant role in economic development. Given the considerable volatility in resource prices, we investigate the volatility spillovers among policy uncertainty, international oil prices, exchange rate, stock index and metal prices covering the period of 2 July 2008 to 15 May 2020 for the Russian economy applying Dynamic Connectedness based on Time-Varying Parameter Vector Autoregression (TVP-VAR). Our empirical investigation demonstrates that gold price, Russian policy uncertainty, oil price and stock index are net volatility contributors, whereas palladium, platinum, silver and exchange rate are net volatilities receivers. Market capitalisation and silver market are found to be the highest net contributor and net receiver, respectively. The palladium appears as a net volatility receiver initially, just after the global financial crisis. The Russian economic policy uncertainty appears to be the dominant volatility contributor from 2008 to 2014, but onward it turned to be a net volatility receiver. Over the year 2014, gold price was the prominent volatility contributor to another market when the oil price dropped significantly. The total connectivity of the markets are highly anchored with several exogenous shocks, including economic sanction, adoption of floating exchange rate, oil price plunge. Our empirical findings provide several policy implications to portfolio managers and Russian regional stakeholders.

Asymmetric nexus between energy prices, precious metal prices and stock market prices in the Indian context: evidence from a nonlinear ARDL model approach

Asymmetric nexus between energy prices, precious metal prices and stock market prices in the Indian context: evidence from a nonlinear ARDL model approach

Non-ferrous metal prices forecast on the london metal exchange using a neural network

Non-ferrous metal prices forecast on the london metal exchange using a neural network

Energy and Metal Price Uncertainties and the Global Economy

Energy and Metal Price Uncertainties and the Global Economy

Energy and Metal Price Uncertainties and the Global Economy

Energy and Metal Price Uncertainties and the Global Economy

Evaluating risk in precious metal prices with generalised lambda, generalised pareto and generalised extreme value distributions

In this study we investigate the performance of the generalised lambda distribution (GLD), the generalised Pareto distribution (GPD) and the generalised extreme value distribution (GEVD) in modelling daily platinum, gold and silver price log-returns. Our primary goal is to compare GLD against GPD, and GEVD, in the estimation of Value-at-Risk (VaR) and expected shortfall (ES) as per the international Basel regulatory framework. Our analyses show that GPD and GLD generally outperform GEVD for VaR and ES estimation for negative precious metal returns. For gold, the GPD stands out as the most suitable model. For platinum, GPD and GLD are equally adequate, especially at the 1% VaR level. For silver, GLD is the most suitable at 1% VaR level, whereas GPD is the best model at 0.1%. This study has shown that GLD is a suitable model for extreme risk in precious metal prices and can be used for the estimation of VaR and ES values. Keywords: Expected shortfall, Generalised extreme value, Generalised lambda, Generalised Pareto, Precious metal returns, Value-at-Risk

Koronavirüs Salgını ve Bilinirliğinin Bitcoin ile Kıymetli Metal Fiyatları Üzerindeki Etkisi: Bir Nedensellik Analizi

Coronavirus (COVID-19), which emerged as an epidemic in China in December 2019, has been recognized as a pandemic by the World Health Organization as of March 2020. Events regarding the coronavirus shocked the markets and were seen as a threat to the markets. In this context, this study aims to examine the effect of the COVID-19 on Bitcoin prices and precious metals which are seen as low-risk assets in global markets. In the study, the causality relationship between the daily number of COVID-19 cases approved by the WHO and Google trends, and the price series of Bitcoin, Gold, Silver, Platinum, Palladium was investigated to determine the effects of the developments in the course of the epidemic on the prices of Bitcoin and precious metals. Toda-Yamamoto causality test was performed in the study where daily data were used between 19.01.2020-31.03.2021. According to the findings, a causality relationship could not be determined between the number of COVID-19 cases with Bitcoin and precious metals while it was observed that the recognition of COVID-19 has a very strong causal effect on Bitcoin prices and the prices of other precious metals except silver. In addition, a reciprocal causality relationship has been identified between the confirmed COVID-19 cases and the recognition of COVID-19.

Forecasting fuel prices with the Chilean exchange rate: Going beyond the commodity currency hypothesis

In this paper we show that the Chilean exchange rate has the ability to predict the returns of oil and of three additional oil-related products: gasoline, propane and heating oil. We show this using both in- and out-of sample exercises at multiple horizons. Natural explanations for our findings rely on the well know “dollar effect” and on the present-value theory for exchange rate determination in combination with the strong co-movement displayed by fuel and metal prices. Given that the Chilean economy is heavily influenced by copper, which represents nearly 50% of total national exports, the floating Chilean Peso is importantly affected by price fluctuations in this metal. As oil-related products display an important co-movement with base metal prices, it is reasonable to expect evidence of Granger causality from the Chilean peso to these oil-related products. Interestingly, we provide sound evidence indicating that the predictive ability of the Chilean Peso goes beyond these natural explanations. In particular, we show another plausible predictive channel: volatility in combination with a negative contemporaneous leverage effect in fuel returns. Finally, we compare the Chilean peso with other commodity-currencies in their ability to predict fuel returns. The Chilean peso fares extremely well in this competition, especially at short horizons of one, three and six months.

Enabling ultrafast lithium-ion conductivity of Li2ZrCl6 by indium doping

Solid-state batteries with high energy density and safety are promising next-generation battery systems. However, lithium oxide and lithium sulfide electrolytes suffer low ionic conductivity and poor electrochemical stability, respectively. Lithium halide solid electrolyte shows high conductivity and good compatibility with the pristine high-voltage cathode but limited applications due to the high price of rare metal. Zr-based lithium halides with low cost and high stability possess great potential. Herein, a small amount of In3+ is introduced in Li2ZrCl6 to synthesize Li2.25Zr0.75In0.25Cl6 electrolytes with a high room temperature Li-ion conductivity of 1.08 mS/cm. Solid-state batteries using Li2.25Zr0.75In0.25Cl6/Li5.5PS4.5Cl1.5 bilayer solid electrolytes combined with Li-In anode and pristine LiNi0.7Mn0.2Co0.1O2 cathode deliver high initial discharge capacities under different cut-off voltages. This work provides an effective strategy for enhancing the conductivity of Li2ZrCl6 electrolytes, promoting their applications in solid-state batteries.

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).

Post-pandemic metal supercycle and effectiveness of fiscal instruments

The paper has two main objectives. On the one hand, to assess whether the recent rise in the international price of metals is of similar importance to that registered during the “super cycle” of the decade 2003-2012. On the other, analyze the effectiveness of fiscal instruments in capturing the rent related to extraordinary income from mining in Peru. Based on a statistical analysis it is found that the recent rise in the price of metals, being more intense and having started from a higher threshold compared to the past boom, translates into particularly hight extraordinary income for mining companies. It is also concluded that the new mining tax regime, implemented since 2012, has not been showing the same dynamism as the old tax regime in order to ensure optimal appropriation of mining rent by the State.

Using internet search keyword data for predictability of precious metals prices: Evidence from non-parametric causality-in-quantiles approach

This investigation examines how Google trends-based information can influence predictability of precious metals prices by utilizing Linear Granger causality & non-parametric causality in quantiles approach. Data ranges from January (2004) to March (2021). We have incorporated the four most popular metals (i.e., Gold, Platinum, Palladium, & Silver) & Crude oil. Although findings obtained from linear Granger causality showed no causal link between Google trends series & oil and precious metals prices, rather findings obtained from the non-parametric test show the existence of a non-linear association among constructs. Non-parametric test results show Google trends series can predict the prices of precious metals. Therefore, we conclude that investors, before making investment decisions, first seek information available online, i.e., on Google Trends, to gain some insights about future price movement that would be ideal for any investor. Moreover, investors, policymakers can get noteworthy awareness from this research for thinking out of the box while making investments.

How the price dynamics of energy resources and precious metals interact with conventional and Islamic Stocks: Fresh insight from dynamic ARDL approach

The study aims to determine the short and long run relationships, and impulsive response effect of energy resources and precious metals on conventional and Islamic stocks through the Dynamic stimulated autoregressive distributed lag error correction model. Monthly time series data from Jun 2002 to Sep 2019 of Dow Jones Conventional and Islamic Stock indices, and commodity prices of gold, silver, platinum, crude oil, gasoline, and natural gas are analyzed. In the case of conventional stocks, results suggest that gasoline and platinum prices have a significant positive impact, whereas gold prices have a significant negative impact in the long run. As far as Islamic stocks are concerned, energy resources prices are found to be insignificant, however, among precious metals, platinum has a significant positive effect, and gold has a negative effect in the long run. Hence, we can infer that energy resources are affecting conventional and Islamic indices differently in the long run, but the impact of precious metal prices on both the indices is the same. Whereas short-run effect and impulse responses of energy resources as well as precious metals are found to be similar on both the indices. The outcomes of this endeavor are substantial for investors, portfolio managers, and policymakers with respect to their investments in conventional and Islamic stock markets.

Porphyry copper deposit formation in arcs: What are the odds?

AbstractArc magmas globally are H2O-Cl-S–rich and moderately oxidized (ΔFMQ = +1 to +2) relative to most other mantle-derived magmas (ΔFMQ ≤ 0). Their relatively high oxidation state limits the extent to which sulfide phases separate from the magma, which would otherwise tend to deplete the melt in chalcophile elements such as Cu (highly siderophile elements such as Au and especially platinum-group elements are depleted by even small amounts of sulfide segregation). As these magmas rise into the crust and begin to crystallize, they will reach volatile saturation, and a hydrous, saline, S-rich, moderately oxidized fluid is released, into which chalcophile and any remaining siderophile metals (as well as many other water-soluble elements) will strongly partition. This magmatic-hydrothermal fluid phase has the potential to form ore deposits (most commonly porphyry Cu ± Mo ± Au deposits) if its metal load is precipitated in economic concentrations, but there are many steps along the way that must be successfully negotiated before this can occur. This paper seeks to identify the main steps along the path from magma genesis to hydrothermal mineral precipitation that affect the chances of forming an ore deposit (defined as an economically mineable resource) and attempts to estimate the probability of achieving each step. The cumulative probability of forming a large porphyry Cu deposit at any given time in an arc magmatic system (i.e., a single batholith-linked volcanoplutonic complex) is estimated to be ~0.001%, and less than 1/10 of these deposits will be uplifted and exposed at shallow enough depths to mine economically (0.0001%). Continued uplift and erosion in active convergent tectonic regimes rapidly remove these upper-crustal deposits from the geological record, such that the probability of finding them in older arc systems decreases further with age, to the point that porphyry Cu deposits are almost nonexistent in Precambrian rocks.A key finding of this paper is that most volcanoplutonic arcs above subduction zones are prospective for porphyry ore formation, with probabilities only falling to low values at late stages of magmatic-hydrothermal fluid exsolution, focusing, and metal deposition. This is in part because of the high threshold required in terms of grade and tonnage for a deposit to be considered economic. Thus, the probability of forming a porphyry-type system in any given arc segment is relatively high, but the probability that it will be a large economic deposit is low, dictated to a large extent by mineral economics and metal prices.

(Invited) Commercialization of Catalytic Materials for Chemical and Electrochemical Energy Conversion Technologies

The incredible growth in Electrochemical Energy Conversion Technologies is leading to highly accelerated innovation in the materials at the heart of these technologies. Electrocatalysts for hydrogen generation and conversion, in particular, are experiencing an accelerated adoption cycle when compared to previous deployment, influenced both by volatility and dramatic growth (>60% annually) in precious metal prices. Pajarito Powder has developed and commercialized novel electrocatalysts for fuel cells and electrolyzers. Different applications, the path to commercialization, and both opportunities and challenges to commercialization will be presented, described, and discussed.