Precious Metal Prices Research Articles

Forecasting the volatility of precious metals prices with global economic policy uncertainty in pre and during the COVID-19 period: Novel evidence from the GARCH-MIDAS approach

Economic policy is a major determinant of investment and financial decisions; Moreover, prices of precious metals are highly influenced by any uncertainty recorded in the global economic policy. Therefore, the prime consideration of the authors is to assess how global economic policy uncertainty influences the volatility of precious metals prices; particularly “gold, palladium, platinum, and silver” in the pre and during the COVID-19 pandemic. This research analyzed the full sample period (the 1997–2022), pre-COVID period (1997–2019), and during the COVID period (2020−2022) to evaluate the impact during different sample periods. Therefore, the GARCH-MIDAS approach is employed at the data set of different frequencies, i.e., monthly data of GEPU and daily data of precious metals. The results reveal a significant nexus between global GEPU and precious metals price volatility. The findings infer that any uncertainty recorded in global economic policy escalate the price volatility of gold, palladium, platinum, and silver prices. The present study increments the existing literature and provides insights for future scholars, investors, and policymakers.

Interpretable design of Ir-free trimetallic electrocatalysts for ammonia oxidation with graph neural networks

The electrochemical ammonia oxidation to dinitrogen as a means for energy and environmental applications is a key technology toward the realization of a sustainable nitrogen cycle. The state-of-the-art metal catalysts including Pt and its bimetallics with Ir show promising activity, albeit suffering from high overpotentials for appreciable current densities and the soaring price of precious metals. Herein, the immense design space of ternary Pt alloy nanostructures is explored by graph neural networks trained on ab initio data for concurrently predicting site reactivity, surface stability, and catalyst synthesizability descriptors. Among a few Ir-free candidates that emerge from the active learning workflow, Pt3Ru-M (M: Fe, Co, or Ni) alloys were successfully synthesized and experimentally verified to be more active toward ammonia oxidation than Pt, Pt3Ir, and Pt3Ru. More importantly, feature attribution analyses using the machine-learned representation of site motifs provide fundamental insights into chemical bonding at metal surfaces and shed light on design strategies for high-performance catalytic systems beyond the d-band center metric of binding sites.

Metal price behaviour during recent crises: COVID-19 and the Russia–Ukraine conflict

Orientation: Commodities are a prominent feature of the global economy. A substantial component of the income and welfare of both commodity-producing and commodity-consuming countries is highly dependent on the prices of commodities, such as metals. Metal prices are driven by economic growth and provide important insight into the state of the global economy: this behaviour is inextricably linked with the transitory shocks.Research purpose: Understanding recent metal price behaviour provides information about the economic implications of the coronavirus disease 2019 (COVID-19) pandemic as well as the Russia–Ukraine conflict. This research engages with the notion of the ongoing upward swing of the post-2000 super-cycle in the metals market.Motivation for the study: An analysis of metal price behaviour during COVID-19 and the Russia–Ukraine conflict describes the consequences of market shocks on commodity prices and examines the role played by super-cycles within metal markets. This work measures the optimal performances of portfolios comprising precious and nonprecious metals.Research approach/design and method: Portfolio optimisation using Lagrangian calculus under various constraints. The article argues that transitory shocks from pandemics and conflicts have a positive relationship with risk–return profiles of relevant portfolios.Main findings: A positive correlation was found between transitory shocks and prices of both precious and nonprecious metals. There is also evidence regarding the continuation of the post-2000 super-cycle in the metals market.Practical/managerial implications: Better knowledge of these relationships allows commodity traders to confidently assert that metal commodities will deliver healthy returns at moderate risk levels during periods that are characterised by different transitory shocks. Metals exist and exhibit safe haven properties during periods governed by economic uncertainty.Contribution/value add: Metals have exhibited strong economic performances in an economic landscape where little has flourished. This consequently affirms their importance and outlines their investment viability during volatile and uncertain periods.

H3O-LGBM: hybrid Harris hawk optimization based light gradient boosting machine model for real-time trading.

The gross domestic product (GDP) of a country is mainly dependent on its trade and external sector which improves the country's income. According to FY2021-2022, India's nominal GDP is estimated to be 3.12 trillion US dollars. Overall exports and imports have a year-over-year increase of 49.6% and 68% respectively. Machine learning techniques have the potential to improve India's Current gross value by up to 15% by the year 2035. The integration of data, Technology, and talent helps to create intelligent models that enhance artificial intelligence growth. This paper presents an optimized light gradient boosting machine (Light GBM) model using the hybrid Harris hawk optimization (H3O) algorithm for trade forecasting. The overfitting problem in the conventional Harris Hawk Optimization is overcome using the exclusive feature bundling (EFB) and the gradient-based one-side sampling (GOSS) methodologies. The H3O optimization algorithm offers fast convergence by optimizing different lightGBM parameters such as a number of training iterations, maximum depth, minimal data in the leaf, etc. To improve the performance, one step further, the residual errors of the optimized lightGBM model are corrected using the Markov Chain model. The main aim of the optimized lightGBM model is to extract the crucial input values of certain variables such as imports and exports of goods and services, service trade, and merchandise trade and predict the price movement decision. The proposed model identifies the interrelationship with the external market and future market growth along with analyzing the variation in market conditions. The prediction decision is mainly to hold, but, or sell the stocks. When evaluated using the precious metal price forecast and stock market datasets, the proposed methodology shows that the hybrid approach can enhance the prediction performance. The results show that the input parameters were efficient in predicting the economic growth regarding the Intermarket trading system (ITS) and services with higher accuracy.

Precious Metal Prices: A Tale of Four U.S. Recessions

Precious Metal Prices: A Tale of Four U.S. Recessions

Development of Protocol for Computerized XRF-ED Zari Testing Using Artificial Intelligence Technique

The fine gold and silver zari thread is an integral part of the silk handloom weaving predominantly practiced in the southern part of the Indian peninsula. The zari thread consists of a silk core, wrapped with flattened silver-copper alloy wire and electroplated with gold. In a brocade silk saree, a minimum of 240 g (1 Marc) of zari is used along with 540 g of dyed silk to produce a silk saree of 6 m in length. The cost of zari is up to 60% of the price of the raw material needed to produce the saree. Earlier, the silver purity of zari thread was assessed using the volumetric analysis technique. Recently, due to the sharp rise in the price of precious metals, the gravimetric technique, which is more time-consuming, has been adopted to test all the zari ingredients as a content percentage to help the fair-trade practice of zari thread. The advent of the computerized XRF-ED technique has accelerated the process of estimating the metal purity values instantly. It could not be used as a tool to estimate the cost of the zari thread as it is traded based on the concentration of silk. Thus, this research aimed to develop the computerized XRF-ED zari testing protocol using the artificial intelligence technique to assess the zari content values. The research findings revealed that the results of the zari thread constituents as content percentage estimated using AI technique are comparable and at par with the test results of the gravimetric analysis. Thus, the XRF-ED testing can be popularized in the field to assess the quality of zari used in the handloom silk saree available to consumers. Findings could also benefit the overall development of the handloom industry faced with tough competition from the silk saree woven with zari imitation thread.

A novel three-stage hybrid learning paradigm based on a multi-decomposition strategy, optimized relevance vector machine, and error correction for multi-step forecasting of precious metal prices

Accurate forecasting of precious metal prices will greatly assist investors, account managers, and mineral enterprises in making sound decisions and market assessments, while further improvement in the accuracy of precious metal price forecasting is challenging due to its non-linear oscillating characteristics. This paper proposes a novel three-stage hybrid learning paradigm for accurately predicting the prices of three precious metals, i.e., silver, palladium, and platinum. Firstly, the original price series will be decomposed by the complementary ensemble empirical mode decomposition (CEEMD) into several subsequences. Then the subsequences are reprocessed by secondary decomposition (SD) and sequence reconstruction using WPD and permutation entropy to reduce data noise and repetitive modeling. Secondly, all subsequences are fed into the relevance vector machine improved by the African vulture optimization algorithm for prediction to obtain the initial prediction results and the error series. Finally, the error series is further decomposed and predicted by WPD and an optimized relevance vector machine to correct the previously predicted precious metal prices and obtain the final prediction results. The actual precious metal price data are provided as model inputs for multi-step ahead forecasting, and the experimental results show that: the proposed novel learning paradigm achieves MAPE values of 0.2003%, 0.4552%, and 0.2151% for one-step-ahead forecasting of the prices of silver, palladium, and platinum, respectively; the proposed hybrid model performs the best among all the compared models, where hybridization of all the components is essential to improve the prediction accuracy and enables the proposed model to have a higher generalization capability; the results of this paper may provide more scientific insight and reference for precious metals investment and production.

Analysis of Precious Metal Price Movements Using Long Memory Model and Fuzzy Time Series Markov Chain

Precious metals occur naturally and have a high resistance to corrosion or oxidation. These natural resources are used as investment instruments to protect wealth values, such as gold, silver, and palladium. Price movements need to be understood when investing, and it is achieved through a time series model that predicts future prices. Also, autoregressive fractional integrated moving average (ARFIMA) is used to model price movements with long memory effects, while fuzzy time series Markov chain (FTSMC) is employed for performing numerical approach. It was observed that gold price movement has a long memory effect; therefore, it is eligible to be formed into the ARFIMA model. However, the silver and palladium prices do not contain a long memory effect, which means their movements are only formed through the FTSMC numerical model. The ARFIMA modeling results show that the gold price long memory model has the best accuracy with the smallest error value and also demonstrates excellent goodness of fit. Furthermore, the gold price long memory model movement has long-term stability compared to other precious metals. This provides an investment advantage because it is a stable asset, easy to liquidate in cash, free of interest, has an emergency fund role, and protects wealth’s value.

Recent development of nickel based chalcogenides for hydrogen generation

Hydrogen stands out as a clean energy carrier considering future energy systems, and is being given significant attention as legislative priority. A cleaner, safer, efficient and quicker technique for bulk production of pure hydrogen is electrochemical water splitting. Unfortunately, the associated loss of energy (as overpotential) and electrode material prices have made this technique prohibitively expensive, making it impossible for it to replace the dominant industrial hydrogen production methods. Therefore, design of electrocatalysts for electrolysis of water is considered to be of utmost significance by the scientific community. The two half cell processes, i.e., the hydrogen evolution reaction (HER) and the oxygen evolution reaction (OER), are frequently studied using precious metals. Nonetheless, the high price and inadequate abundance of precious metals prevent their widespread use. On the other hand, transition metal based electrocatalysts have excellent electrical conductivity and electrocatalytic activity, . Due to their remarkable physical and chemical characteristics, nickel-based chalcogenides (NiSe, NiS, etc.) are considered as great alternatives to precious metal catalysts. Non-precious metal chalcogenides have been demonstrated to be good for catalysing HER in acidic, neutral as well as alkaline media. The chalcogenides of different metals are the subject of a few review articles. However, there are limited summaries on the development of Ni chalcogenides for electrocatalysis in the literature. Therefore, the focus of this study is solely on describing the current status of nickel chalcogenide electrocatalysts for hydrogen production by water electrolysis.

Price Stability Properties and Volatility Analysis of Precious Metals: An ICSS Algorithm Approach

This paper investigates the price stability properties of precious metals during the 1997 Asian Financial Crisis, 2007–2008 Global Financial Crisis, and 2010 Eurozone Crisis. To analyse the interaction between precious metal prices and the US stock market stock performances, we use the ICSS algorithm along with the GARCH model to evaluate how the number of rapid changes in volatility of precious metals has been reduced. The results suggest gold is the most stable of the precious metals. However, silver, platinum, and palladium showed positive price correlation when the US Dow Jones market was unstable. These results imply that: (1) the correlation among stocks market returns has little to no significant impact on the price movement of precious metals, but the US Dow Jones has some influence on precious metal markets except gold, which means investors can reap this benefit from diversification; (2) investors can systematically increase their portfolio returns by going short with the gold investments with low price co-movement and long on silver, platinum, and palladium with high co-movement with stock prices.

Time-variation between metal commodities and oil, and the impact of oil shocks: GARCH-MIDAS and DCC-MIDAS analyses

Extant literature establishes co-movements among commodity (metal and oil) prices; whereas oil price/shocks aggregate, as a lone predictor, has relative predictability for most financial assets. We assess the predictability of Baumeister and Hamilton's (2019) decomposed oil shocks (economic activity shocks, oil consumption demand shocks, oil inventory demand shocks, and oil supply shocks) for conditional volatilities of prominently traded precious metals (gold, palladium, platinum, and silver) using GARCH-MIDAS-X framework. The asymmetric effect of decomposed oil shocks on precious metals' volatilities is examined. The DCC-MIDAS framework allows to investigate the conditional correlations and volatility between oil and precious metal prices. Results show that precious metals exhibit hedging potentials against oil demand and supply shocks, with heterogeneity observed in the precious metal-oil shocks nexus. Asymmetry is evident in the responses of metals' volatility to oil shocks. DCC-MIDAS results reveal significant dynamic correlations between oil prices and precious metals (except for platinum). Our results are robust (sensitive) to precious metals (oil shocks) proxies. The findings are insightful for commodity market stakeholders.

Apply big data analytics for forecasting the prices of precious metals futures to construct a hedging strategy for industrial material procurement

AbstractThis study explores the critical factors affecting the prices of precious metals by using Granger causality tests through collecting financial market information and network traffic volume of Google Trend keywords and the traditional financial index. It establishes a forecasting model of precious metal price to assist the procurement of precious metals as raw materials which help to reduce material costs and control risks, thus lifting a company's profits. The results reveal that the prices of precious metals have a causality relation with their own exchange‐traded funds, US 10‐year treasury rate, and the Dow Jones Industrial Average but not Google Trends.

Fabrication of manganese borate/iron carbide encapsulated in nitrogen and boron co-doped carbon nanowires as the accelerated alkaline full water splitting bi-functional electrocatalysts

With high prices of precious metals (such as platinum, iridium, and ruthenium) and transition metals (such as cobalt and nickel), the design of high-efficiency and low-cost non-precious-metal-based catalysts using iron (Fe) and manganese (Mn) metals for hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) are critical for commercial applications of water splitting devices. In the study, without using any template or surfactant, we successfully designed novel cross-linked manganese borate (Mn3(BO3)2) and iron carbide (Fe3C) embedded into boron (B) and nitrogen (N) co-doped three-dimensional (3D) hierarchically meso/macroporous carbon nanowires (denoted as FexMny@BN-PCFs). Electrochemical test results showed that the HER and OER catalytic activities of Fe1Mn1@BN-PCFs were close to those of 20 wt% Pt/C and RuO2. For full water splitting, (-) Fe1Mn1@BN-PCFs||Fe1Mn1@BN-PCF (+) cell achieved a current density of 10 mA cm−2 at a cell voltage of 1.622 V, which was 14.2 mV larger than that of (-) 20 wt% Pt/C||RuO2 (+) benchmark. The synergistic effect of 3D hierarchically meso/macroporous architectures, excellent charge transport capacity, and abundant active centers (cross-linked Mn3(BO3)2/Fe3C@BNC, BC3, pyridinic-N, MNC, and graphitic-N) enhanced the water splitting catalytic activity of Fe1Mn1@BN-PCFs. The (-) Fe1Mn1@BN-PCFs||Fe1Mn1@BN-PCF (+) cell exhibited excellent stability owing to the superior structural and chemical stabilities of 3D hierarchically porous Fe1Mn1@BN-PCFs.

Precious metals as safe-haven for clean energy stock investment: Evidence from nonparametric Granger causality in distribution test

The study aims to examine the connectedness between clean energy stocks and precious metals prices under the different market episodes. We employ the Granger causality-in-the distribution test proposed by Candelon and Tokpavi (2016) to investigate the presence of a causality relationship between the variables for the whole distribution because the test has superior power even if the sample size is small. WilderHill Clean Energy Index is considered a benchmark for the clean energy stock market and gold, silver, platinum, and palladium prices are used for the precious metals. By using daily data from January 1, 2001, to December 12, 2021, we find that there is a unidirectional causal link running from the clean energy stock returns to the precious metal prices in the center and the left tail of the distribution. On the other hand, there is strong feedback between the variables in the right tail of the distribution. These results show that clean energy stock prices have an edge in affecting precious metal prices and precious metals cannot be used to hedge the downside risk of clean energy stock investments.

Impact of the COVID-19 pandemic on return and risk transmission between oil and precious metals: Evidence from DCC-GARCH model

It is frequently discussed in the literature that the correlation between low-correlation assets under ordinary market conditions may increase during crisis periods. To contribute to the ongoing debates, this paper empirically examines risk transmission between oil and precious metal markets induced by the COVID-19 pandemic using the DCC-GARCH model. The findings reveal evidence of a significant risk transmission between oil prices and precious metal prices, particularly during the onset of the COVID-19 pandemic. The findings point out that the negative relationship between oil and all precious metals returns in the pre-COVID-19 period has changed with the effect of the pandemic. In this process, it is revealed that the negative relationship between oil and gold has strengthened, but the negative relationship between oil and silver has weakened. In addition, the correlations between oil and platinum and palladium turn positive. The empirical findings imply that investors and portfolio managers seeking portfolio diversification and hedging opportunities in a high-risk environment such as the COVID-19 pandemic should consider gold and silver assets for investment.

How COVID-19 influences prices of oil and precious metals: Comparison between data extracted from online searching trends and actual events

The investigation explores whether COVID-19 influences oil and precious metals prices by comparing data extracted from online searching trends and actual events. The study utilizes the Linear Granger causality test & non-parametric causality-in-quantiles method and uses data from January (2020) to March (2021). We have incorporated four mostly trading metals (i.e., Gold, Palladium, Silver, & Platinum) & Crude oil. Although outcomes of Linear Granger causality test show no causal relation between COVID-19 & oil and precious metal prices for both cases (i.e., online searching trends and actual events), the outcomes of the non-parametric test revealed the existence of non-linear association among constructs. Non-parametric test results revealed that COVID-19 significantly influences the prices of oil and precious metals. Therefore, we conclude that policymakers need to contemplate pandemic risk as most critical risk factor for stability of market when developing policies for the market and economy. Furthermore, through this study, investors and policymakers will get noteworthy awareness for thinking out of the box during the crisis.

A novel hybrid model integrating modified ensemble empirical mode decomposition and LSTM neural network for multi-step precious metal prices prediction

Accurately predicting precious metal prices is of extreme significance as they possess an essential position in both financial and industrial fields. To achieve higher prediction accuracy, in this paper, a modified ensemble empirical mode decomposition (MEEMD) method integrated with long short-term memory neural network (LSTM) is used for precious metal price prediction. The multi-scale permutation entropy (MPE) analysis demonstrated that MEEMD has better decomposition effect than ensemble empirical mode decomposition (EEMD). Then input each intrinsic mode function (IMF) obtained by MEEMD into LSTM for prediction. Finally add each IMF forecasting value to get the final prediction results. Compared with traditional multilayer perceptron neural network (MLP), support vector regression (SVR) and a combination forecasting model super learner (SL), MEEMD-LSTM improves the prediction performance in one-step ahead prediction and multi-step ahead prediction. The multi-horizon model confidence set (MCS) test are adopted to comprehensively and statistically testify the best prediction performance of MEEMD-LSTM. Furthermore, this research indicates that the model still shows better prediction accuracy either under different proportions of the training set to the test set or in different periods of business cycle. This competitive precious metal price forecasting model is a promising technique for government agencies, investors and related enterprises.

Precious Metals Price Forecast with ARCH Family Models in COVID-19 Pandemic Period

The COVID-19 virus, which was detected for the first time in Wuhan, China in December 2019, spread to all countries of the world and, therefore, became a global epidemic. Although more than two years have passed since the outbreak of the COVID-19 pandemic, the economic effects of it continue. One of these is the effect of the pandemic on precious metal prices. Precious metals, which are called safe harbours and used as investment tools, have had a serious volatility in the last century as a result of the economic, political and pandemic factors changing the international balances. From this point of view, in this study, it is aimed to determine the appropriate forecasting model to predict the volatility of gold, silver, platinum and palladium prices, which are called precious metals, during the COVID-19 pandemic period. The econometric analysis covers the period between March 11, 2020, when the global epidemic was declared by the World Health Organization, and September 13, 2021, and includes 326 days of observation. To determine the appropriate forecasting model, ARCH, GARCH, T-GARCH, E-GARCH and PARCH are used as symmetrical and asymmetrical volatility models.

The Economic Policy Uncertainty and Its Effect on Sustainable Investment: A Panel ARDL Approach

This study examines the effect of economic policy uncertainty (EPU) on sustainable investment returns by using panel data of stock market returns and the EPU index from twelve countries for the period from April 2015 to December 2020. In addition, precious metal prices, energy prices, and cryptocurrency prices are used as control variables. To do so, we investigate the impact of EPU, gold prices, oil prices, and Bitcoin prices on stock market returns by using the panel autoregressive distributed lag (ARDL) model to examine both the long-run correlation and short-run effect. Our findings show that EPU, gold prices, oil prices, and Bitcoin prices have a time-varying significant impact on sustainable stock market returns. We discovered that EPU has a significantly negative impact on the returns of the sustainable stocks in the markets over the long run. In contrast, the rise of the gold price, oil price, and Bitcoin price have a significantly positive impact on the returns of the sustainable stocks in the twelve sustainable markets in the long run. On the other hand, EPU in Singapore, Spain, the Netherlands, and Russia has a significant short-run impact on market returns in each country. Based on the findings, managers and investors in the sustainable stock markets are highly recommended to pay more attention to the volatility of EPU, gold prices, oil prices, and Bitcoin prices in the short run to control the risk of returns in the sustainable stock market. Furthermore, policymakers must closely monitor the movement of the EPU index, as it is a major driver of sustainable stock market returns.

Iron-gelatin aerogel derivative as high-performance oxygen reduction reaction electrocatalysts in microbial fuel cells

Currently, precious based materials are known as highly efficient and widely used catalysts for oxygen reduction reaction (ORR). The expensive price and scarce resource of precious metals have stimulated researchers to explore low-cost and high-performance non-precious metal catalysts. Gelatin is a promising precursor to prepare cost-effective and high-performance catalysts because of abundant micropores and nitrogen self-doping sites after pyrolysis. Herein, we developed a new highly active ORR catalyst (G/C–Fe-2) containing Fe–N coordination sites and Fe/Fe3C nanoparticles. G/C–Fe-2 exhibited excellent ORR electrocatalytic activity (onset potential: 0.21 V, and limiting current density:7.36 mA cm−2) and high-performance in air-cathode MFCs (Output voltage: 660 mV, and maximum power density: 560 mW m−2). It is significant for synthesizing low-cost and high-activity ORR electrocatalysts through this strategy.