Precious Metal Prices Research Articles

Gold price prediction using the Box-Jenkins methodology

Information on the speculation and trading of gold abounds. Investors are attracted to moving their funds to gold as guaranteed storage of wealth, while traders capitalize on the dynamism of the market to build capital. The ups and downs in the price of gold and other precious metals can be predicted with proven mathematical and artificial intelligent algorithms. This study used machine learning algorithm in the price prediction of gold over a ten-year period. Autoregressive Integrated Moving Average (ARIMA) model was used in the experiment, while Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) evaluation metrics were used in the evaluation of the performance of the various ARIMA models. The results obtained in the study proved that ARIMA could achieve high prediction performance over the entire period of prediction. The best prediction outcome of 98.23% was obtained during the 52-week period.

Comparison performance of Artificial Neural Networks and Fuzzy Inference systems in forecasting precious metals price Case Study: Gold, Silver, Platinum and Palladium

Awareness about the price of precious metals and the correct prediction on the process of taking decision can bring facilities, and purchasing them in the global market and recognizing the specific time of dealing can cause investment. In this article comparison of the performance of Artificial Neural Networks and Fuzzy Inference Systems in predicting the price of the precious metals (Case Study: Gold, Silver, Platinum and Palladium).has been pointed. The information about each of these metals (Gold, Silver, Platinum and Palladium) is monthly considered from 1998 until 2018 including 360 data. Thus, by examining different influential variables, National Product Parameters, Time, getting higher the value of USD dollar against the Canadian dollar, global production and global reserves of precious metals are chosen as influential variables. In this research, implementation of (ANFIS) is made for the prediction model by using Artificial and Fuzzy Neural Model. Evaluation of models by using coefficient values, the average set of squares and the square root of the average set of the squares in order of the values for Gold 0.9964 , 0.000268 & 0.01637 for silver 0.987, 0.000092 & 0.0096, for platinum 0.9976, 0.000209 & 0.01448 and for palladium 0.99, 0.0001 & 0.01 have been achieved. As a result, while the best predictive model for the price of gold and platinum is Artificial Neural Networks, the model of ANFIS is suggested for the silver and palladium.

A shock like no other: coronavirus rattles commodity markets.

The novel coronavirus (2019-nCoV) originated in China has now covered around 213 countries globally. It has posed health calamities which have threatened the world with the emergence. Owing to the number of confirmed cases still rising every day, it has now become a phase of an international health emergency. Sudden outbreak of coronavirus disease 2019 (COVID-19) has brought global declines in the commodity process. This has majorly affected the demand as well as supply of the commodities. The oil market has been severely affected due to the outrageous collapse in the demand majorly due to travel restrictions which has also caused the steepest decline in oil prices. The prices of both precious and industrial metals have also fallen, although the price drop is less than that of oil prices. The agriculture industry is one of the least affected so far by this pandemic due to its indirect relation with economic activities. However, the ultimate impact of COVID-19 pandemic will greatly depend on the severity and duration of its outspread, but it is expected to have long-lasting implications.

Time-varying volatility spillovers between oil prices and precious metal prices

This paper tackles whether there is a return and volatility spillover effect between oil price and precious metal prices such as gold, silver, platinum, and palladium using the causality-in-variance test approach proposed by Hong (2001). This study utilizes the daily data covering the period from 1990 to 2019 and the empirical findings reveal that there is causality-in-mean relation running from the oil return series to precious metal return series. The causality-in-mean test results show that oil price is Granger cause of all precious metals. Similarly, we find a volatility spillover effect from the oil market to the precious metal market according to the causality-in-variance test results. On the other hand, we find evidence in favor of the bidirectional volatility spillover effect between oil and silver return series. Also, the volatility spillover effect from oil to platinum seems to be weak when it is compared to the other precious metals. Moreover, the time-varying causality-in-variance test results provide a different picture because the null hypothesis of no volatility spillover from precious metals to oil is rejected at specific periods. More interestingly, we find that there is a sequential feedback relationship between oil and silver. Finally, the volatility spillover effect from the oil market to the precious metal markets seems to be strong specifically after the 2000s.

Quantile causality and dependence between crude oil and precious metal prices

AbstractThis paper examines long‐run dependence and causality between oil and precious metal (gold, silver, platinum, palladium, steel, and titanium) prices across quantiles by exploiting their time series properties with the help of novel econometric techniques. The empirical results for the period 1990–2019 indicate that oil and metal prices are nonstationary across different quantiles and that cointegration patterns differ widely across quantiles. Causality running from oil to metal prices is quantile‐dependent and differs according to the metal, whereas upward and downward movements in metal prices have no causal effect on oil prices. These results have implications for investors and policymakers in terms of portfolio and risk management decisions.

The role of the threshold effect for the dynamics of futures and spot prices of energy commodities

Abstract This study examines whether threshold models allow to better understand the dynamic relationship between spot and futures prices for crude oil and natural gas. Our findings are threefold. First, we show that the futures curve delivers relatively accurate forecasts for energy commodity prices. Second, we provide evidence that the relationship between spot and futures prices is regime dependent but accounting for this property does not improve the quality of out-of-sample forecasts. Third, we demonstrate that using information on the dynamics of financial variables (exchange rates, stock and uncertainty indices, interest rates or industrial and precious metal prices) does not contribute to the quality of futures-based forecasts. This suggests that the predictive content of these variables is already contained in futures prices.

Analysis of EEMD-based quantile-in-quantile approach on spot- futures prices of energy and precious metals in India

By using the daily data for spot and future prices for India, we examine the frequency-dependent asymmetric relationship between futures and spot markets of crude oil, gold, and natural gas (GON). We use the novel asymmetric, noise-reducing frequency-domain EEMD-based quantile-on-quantile regression (QQR) technique. We contribute to the literature on the Indian commodity market by performing the analysis in a noise-free frequency-varying asymmetric framework where dynamic hedging and portfolio diversification is possible. The study appeals to the nature of market dynamics as well as to different investor risk and reward preferences. The empirical results show that both QR and QQR are able to adequately capture the asymmetric link between GON spot and futures prices across the short-, medium-, and long-terms. It also allows ranging from weak to very strong dependencies albeit both negative and positive across different quantiles. Specifically, we find that hedging strategies are feasible in the medium-terms and long-terms of crude oil returns at all quantiles above 0.05. Moreover, our results also show that natural gas and gold futures can only be a weak hedge for a spot in the short-term, but not in the medium-terms and long-terms. The policy implications are also discussed from the findings.

Pt Nanoparticles with High Oxidase-Like Activity and Reusability for Detection of Ascorbic Acid.

Noble metal nanoenzymes such as Pt, Au, Pd, etc. exhibit magnificent activity. However, due to the scarce reserves and expensive prices of precious metals, it is essential to investigate their enzyme-like activity and explore the possibility of their reuse. In this work, the oxidase-like activity and reusability of several Pt nanoparticles with different morphologies were detected. We compared the Pt nanoparticles (NPs) with a size of about 30 nm self-assembled by 5 nm Pt nanoparticles and Pt nanoparticles (Pt-0 HCl) with a diameter of about 5 nm, and found that their Michaelis−Menten constants (Km) were close and their initial performance similar, but the Pt NPs had better reusability. This was probably attributed to the stacked structure of Pt NPs, which was conducive to the substance transport and sufficient contact. At the same time, it was found that the size, dispersion, and organic substances adsorbed on the surface of Pt nanoparticles would have a significant impact on their reusability. A colorimetric detection method was designed using the oxidase-like activity of Pt NPs to detect ascorbic acid in triplicate. The limits of detection were 131 ± 15, 144 ± 14, and 152 ± 9 nM, with little difference. This research not only showed that the morphology of the catalyst could be changed and its catalytic performance could be controlled by a simple liquid phase synthesis method, but also that it had great significance for the reuse of Pt nanoenzymes in the field of bioanalysis.

Co-movements and spillovers between prices of precious metals and non-ferrous metals: A multiscale analysis

This study examines the co-movements between price returns of precious metals (gold and silver) and non-ferrous metals (aluminium, copper, Lead and zinc). To do this, we use the spillover index developed by Diebold and Yilmaz (2012) and various wavelet. The results show that aluminum is the highest contributor to shocks in the other metal markets whereas Lead and copper are the least contributors. Moreover, silver, copper, zinc and Lead are net receivers of shocks while gold and aluminium are net contributors to shocks in the other markets. In addition, the trajectory of the spillover index among all metals under investigation is influenced by economic crises. On the other hand, the results of wavelet methods reveal strong co-movements between precious and non-ferrous metals under medium and low frequencies where gold and silver are leading the non-ferrous metals. Besides, precious metal-non-ferrous metal pairs are in phase. The results show strong multiscale co-movements among non-ferrous metals. Gold offers better diversification benefits to non-ferrous metals under 2–4 days. The diversification benefits decrease as scale rise. These findings are interesting and entail significant implications to investors, portfolio managers, and policy-makers to inform their decision-making.

Carbocatalytic Acetylene Cyclotrimerization: A Key Role of Unpaired Electron Delocalization

Development of sustainable catalysts for synthetic transformations is one of the most challenging and demanding goals. The high prices of precious metals and the unavoidable leaching of toxic metal species leading to environmental contamination make the transition metal-free catalytic systems especially important. Here we demonstrate that carbene active centers localized on carbon atoms at the zigzag edge of graphene represent an alternative platform for efficient catalytic carbon-carbon bond formation in the synthesis of benzene. The studied acetylene trimerization reaction is an efficient atom-economic route to build an aromatic ring-a step ubiquitously important in organic synthesis and industrial applications. Computational modeling of the reaction mechanism reveals a principal role of the reversible spin density oscillations that govern the overall catalytic cycle, facilitate the product formation, and regenerate the catalytically active centers. Dynamic π-electron interactions in 2D carbon systems open new opportunities in the field of carbocatalysis, unachievable by means of transition metal-catalyzed transformations. The theoretical findings are confirmed experimentally by generating key moieties of the carbon catalyst and performing the acetylene conversion to benzene.

Precious Metal Price Prediction Based on Deep Regularization Self-Attention Regression

It is non-trivial to predict the prices of precious metals since a number of factors can affect the fluctuations of precious metal prices. Either parametric models or machine learning models cannot accurately forecast the precious metal prices. Though deep learning approaches show their strengths in extracting key features from complicated data, they have the limitations of learning localization and losing some temporal and spatial features. The recent advances in attention mechanisms bring the opportunities to overcome the limitation of deep learning models. In this paper, we originally propose a Regularization Self-Attention Regression Model for precious metal price prediction. In particular, the proposed RSAR model consists of convolutional neural network (CNN) component and Long Short-Term Memory Neural Networks (LSTM) component. Integrating with self-attention mechanism, this model can extract both spatial and temporal features from precious metal price data. Meanwhile, the proper configuration of regularization functions can also lead to the further performance improvement. Extensive experiments on realistic precious metal price dataset show that our proposed approach outperforms other conventional machine learning and deep learning methods.

Korelacioni efekat uticaja promene cena baznih i plemenitih metala na faktore kreditnog rizika

The changes in the prices of base and precious metals on the global metal market have a significant impact on credit risk factors. The link between these factors has been neglected over the years by traditional credit risk models. The inclusion of correlation coefficients within the set credit risk model will show the impact of these changes on other variables of credit risk over the years under review and the impact of these changes on the probability of default and the recovery rate. Changes in base metals prices on the London Metal Exchange (LME) for lead and zinc and the London Bullion Metal Association (LBMA) for gold and silver as precious metals were used in the proposed credit risk model for the period of ten years. The research was done by using the multivariate regression analysis model and based on the statistical model evaluation,the significant impact of all observed independent variables on the dependent variable of the proposed model was proved. The construction of the proposed model with proven predictability gives a scientific significance to the research that includes variables of models from different markets, which have a significant impact on the variables from the financial market.

Stationarity of prices of precious and industrial metals using recent unit root methods: Implications for markets’ efficiency

This study investigated stationarity properties of prices of four precious metals (Gold, Palladium, Platinum and Silver) and seven industrial metals (Aluminium, Copper, Lead, Nickel, Steel, Tin, and Zinc) during 1960–2017 using diverse unit root test approaches, especially the recent ones that take cognisance of both non-linearity and structural break(yet to find applications). Prices of seven of the eleven metals are stationary based on the results of most of the conventional unit root tests used. However, prices of all metals are stationary based on the results of at least three of five unit root tests with structural break performed. Further, prices of both the precious and industrial metals are stationary based on the results of all the three non-linear unit root tests performed including one with structural break. Also, when linearity structures of metal prices together with structural break are taking into account, prices of the four precious and seven industrial metals are stationary. Findings imply that policy makers can forecast and formulate policies pertaining to metals, particularly revenue forecasting and risk management. It may be uneasy for metals’ market participants to reap abnormal returns. Also, research should consider the issues of structural break and non-linearity in econometrics modelling.

Characterization of LaFe0.6Co0.4O3 washcoat layer on a monolithic substrate

Development of automotive three-way catalysts (TWC) is a critical research topic owing to the increasingly stringent emission regulations together with the price and scarcity of precious metals. Among different materials classes, perovskite-type oxides are known to be valuable alternatives because of their high catalytic activities and thermal stability. The interest in doped lanthanum ferrite perovskite as a catalyst is the concept of regeneration of catalyst nanoparticles, which makes it be used efficiently in automobile exhaust for longer duration in comparison to the conventional TWCs. B-site cation regeneration prevents particle growth and sulfur poisoning; hence, the consumption of precious metals will be reduced without lowering the catalytic performance. The aim of this study is to optimize the characteristics of a LaFe0.6Co0.4O3 (LFC) washcoat layer on a cordierite (2MgO.2Al2O3.5SiO2) monolithic substrate through dip coating of the slurry of amorphous and calcined nano-sized LFC particles. Experiments have been conducted to find the optimum amount of viscosity of the slurry, number of dipping repetitions, and suspension time in order to get the highest specific surface area and adherence to the substrate. Also, differences between a slurry with amorphous and calcined powder, precipitation agent and calcination temperature are investigated. It was found that a slurry with 8% w/w of LFC powder, 2% w/w of PVA, 1% w/w of HNO3, and 7.8% w/w of glycerol in water yields suitable slurry characteristics for the dip-coating purpose. SEM pictures were acquired to find out the morphology of the washcoat layer. By repeating the dip-coating process for two times with the calcined powder, a uniform and porous layer is formed on the monolithic substrate with particle size of around 200 nm after sintering at 900 °C.

A Neural Network Approach to Pricing in the Precious Metals Market

The modern development of the science of artificial neural networks (ANN) has allowed to use their nature and properties in various applied fields of science. One of the most important applications of ANN is the modeling of prices in the precious metals market. Just like in any other market, based on the prediction of current prices, because the ability of ANN to learn like a true biological neural network, relying on the input with subsequent testing of the output, provides a significant advantage in the prediction tasks compared to the classical mathematical algorithms. Predicting the price of precious metals with relatively high precision and low error is in great demand among all individuals and legal entities that carry out transactions which are directly related to the purchase and sale of these precious metals, since accurate knowledge of the future price of a particular metal will bring maximum benefits of these operations. Numerous methods have been developed [2-4] for the use of neural networks in the modeling of price forecasts, which make the prediction of the rate of exchange for a particular currency (rather objective). The applied methods make the prediction using the classical perceptron along with astrological cyclic indices [2], recursive neural networks [3], and/or using elements of mathematical statistics (for example, use of U-statistic and the coefficient of determination ) [4]. The goal of this paper is the attempt to usethe ANN in the forecasting problem that allows predicting the price of precious metals in the near future, based on an algorithm that makes predictions by learning based on an array of input data and does not depend on the said elements of mathematical statistics. The paper presents a new method for using an artificial neural network in forecasting problems. Experimental studies of this method were carried out on the basis of the precious metals pricing rate on the Ukrainian Interbank Exchange. The corresponding conclusions are made regarding the effectiveness of the method and the possibilities for its further improvement based on the results of these studies. It is expected that such an algorithm will give a prediction as close as possible to the real value.

Dynamics of oil price, precious metal prices and the exchange rate in the long-run

We examine the relationship between the oil price, prices of precious metals (gold, silver, and platinum) and the US dollar/British Pound exchange rate using parametric and non-parametric modelling over a 135-year period. For the parametric model, we employ a two-regime threshold vector error correction model (TVECM) and find non-linearity and asymmetries in the long-term relationship between the oil-gold price and oil-silver price pairs during the ‘typical regime’, in which the majority of observations lie. Non-linear Granger causality suggests evidence of bidirectional and unidirectional causality. For the non-parametric model, we employ Local Linear (LL) non-parametric regression to relax the assumptions regarding functional form. The relationship between the oil price and each of the precious metal prices and the exchange rate exhibit non-linearities. The relationship between precious metal prices and the oil price is positive and generally increasing over time, while the LL estimates for the exchange rate are negative and then positive and highly non-linear.

Single-Atoms Synthesized Via a Novel Method As the Active Site with Highly Selectivity Electrocatalytic Conversion of CO2 to Ethanol

Single-atom catalysts (SACs) are recently emerging as a new frontier in heterogenous catalysis for their effectiveness in promoting catalytic reactions. We report herein the study of carbon-supported single-atom catalysts synthesized by a novelty method exhibit distinctive selectivity for some special electrochemical reactions during the electrolysis process. The aberration-corrected HAADF-STEM images combined with X-ray absorption spectroscopy identified the presence of single atoms. Theoretical density functional theory simulations of the best performing catalytic activity and selectivity could be attributed to the existence of single atoms in the catalysts. Introduction CO2 is a cheap resource of carbon that can be used as raw materials for fuel synthesis using renewable energy In 2005, 36.2 billion metric tons of CO2 was emitted to the atmosphere from anthropogenic sources, [1] which is corresponding to an increase in global mean temperature of around 0.1oC. New carbon capture, sequestration and conversion technologies are widely pursued as potential solutions to mitigate the concerns. The electrochemical CO2 reduction reaction (CO2RR) can combine carbon capture storage with renewable energy to convert CO2 into fuels and chemical feedstocks. Which is extraordinary significance for industry and is highly competitive with water electrolysis. Therefore, efficient electrocatalysts are required to reduce the reaction overpotential and improve the faradaic efficiency and selectivity for high value chemical products further. [2] The earth-rare, high price and low selectivity of precious metals prevent further large-scale commercial applications. Furthermore, these precious metal catalysts exhibit desirable products at highly cathodic potential window with high overpotential, which causes lot of energy consuming problem in application. Recently, single-atom catalysts (SACs) are emerging as a new frontier in heterogenous catalysis for their effectiveness in promoting catalytic reactions. [3] The unique electronic structure and unsaturated coordination environments in single atom catalysts (SACs) have been proven to enhance catalytic activity and excellent selectivity in a variety of reactions. In this work, the catalyst was prepared by a proprietary method currently under patent examination. Results and Discussion We report herein the study of carbon-supported SACs synthesized by a novel method that exhibit distinctive selectivity in electrochemical synthesis. The aberration-corrected HAADF-STEM images combined with X-ray absorption spectroscopy identified the presence of single atoms. Theoretical density functional theory simulations of the best performing catalytic activity and selectivity could be attributed to the existence of single atoms in the catalysts. The prepared metal single atoms dispersed on substrate catalysts showed high faradaic efficiency and selectivity of CO2 to C2 products conversion with low onset potential. Fig. 1a shows linear sweep voltammetry (LSVs) over M/C-0.1 before and after chronoamperometry measurements at different potentials. CV curves (Figure 1b) also displayed obvious activity toward CO2RR in the potential range from 0.5 V to -1.5 V vs RHE, and the current density remained stable with no apparent decay before and after electrolysis in CO2 saturated KHCO3 solution. The FE reached to as high as ~91.8% for ethanol at a low potential of -0.6 V when the metal loading is 0.1 wt% on carbon support. Figure 1. Electrochemical CO2RR over M/C-0.1. (a) LSV in a potential range of 0 to -1.5 V vs RHE in Ar and CO2 saturated 0.1 M KHCO3. (Scan rate: 50 mV s-1). (b) CV in a potential range of 0.5 to -1.5 V vs RHE in Ar and CO2 saturated 0.1 M KHCO3. (Scan rate: 50 mV s-1, (c) FEs of key byproduct as the function of potential over the catalysts of M/C-0.1 Significance Understanding the role of metal single atoms on the CO2 electrochemical reduction reaction is fundamental for design of an appropriated catalyst for the large-scale application of CO2 RR that aims the production of high energy liquid chemicals.

Can oil and precious metal price forecast exchange and interest rate movement in Bangladesh?

ABSTRACTThis paper investigates the predictive power of world oil and precious metal price for exchange rate (BDT/US dollar) and interest rate movements using monthly data spanning from January 1990 to April 2016. Using a structural VAR with identification restriction, our impulse response and variance decomposition analysis suggest that oil price has a significant influence in forecasting both exchange and interest rate in short-run. Whereas, precious metal (i.e. gold and silver) price tends to display overshooting behavior on interest rate in both short and long horizons. This evidence also suggests that shocks to exchange rate account for a substantial share of fluctuations in interest rate. The findings offer major quantitative evidence for central bank policy makers, investors, hedge and portfolio managers.

The volatility effect on precious metals price returns in a stochastic volatility in mean model with time-varying parameters

The purpose of this study is to examine how volatility effects precious metals price returns. We use the time-varying parameter stochastic volatility in mean (TVP-SVM) model to estimate the volatility and incorporate strong time varying dynamics in commodity markets. The study employs monthly data on gold, silver, copper, platinum, and palladium from 1962 to 2017. We find evidence that volatility has a largely time-varying impact on the precious metal price returns. The results show that volatility has a significant negative impact on the precious metal price returns and the impact is more negative during higher volatility periods. The market volatility is also found to be extremely persistent, implying that strong policy measures might be required to restore equilibrium. The estimates also show that metal price returns have a positive and significant effect on the volatility and, thus, higher precious metal price returns generate increased future volatility.

MULTIFRACTAL BEHAVIOR IN PRECIOUS METALS: WAVELET COHERENCY AND FORECASTING BY VARIMA AND V-FARIMA MODELS

We introduce a new approach to improve the forecasting performance by investigating the multifractal features and the dynamic correlations of return on spot prices of precious metals, namely, gold and platinum. The Hölder exponent of multifractal time series is employed to detect the critical fluctuations during the financial crises through measuring the multifractal behavior. We also consider co-movement of Hölder exponents and forecast the Hölder exponents of multifractal precious metal time series on coherent time periods. The results indicate that forecasting of multiple wavelet coherence of Hölder exponents of multifractal precious metal time series is efficiently improved by using Vector FARIMA and VARIMA models.