Silver Prices Research Articles

Where was the global price of silver established? Evidence from London and New York (1878–1953)

This paper utilises a newly compiled, unique data set of silver prices for the London and New York markets, as well as the monthly Dollar-Sterling exchange rate from 1878 and 1953 to investigate the source and flows of information and price discovery. Results indicate that London was the dominant market and pricing leader of silver throughout the period analysed. Further evidence suggests that there existed dynamic information flows both within years and decades. These phenomena can be explained due to the use of silver for agricultural market payments and international silver production flows respectively.

Generalized Autoregressive Conditional Heteroskedastic Model to Examine Silver Price Volatility and Its Macroeconomic Determinant in Ethiopia Market

Like most commodities, the price of silver is driven by supply and demand speculation, which makes the price of silver notoriously volatile due to the smaller market, lower market liquidity, and fluctuations in demand between industrial and store value use. The concern of this article was to model and forecast the silver price volatility dynamics on the Ethiopian market using GARCH family models using data from January 1998 to January 2014. The price return series of silver shows the characteristics of financial time series such as leptokurtic distributions and thus can suitably be modeled using GARCH family models. An empirical investigation was conducted to model price volatility using GARCH family models. Among the GARCH family models considered in this study, ARMA (1, 3)-EGARCH (3, 2) model with the normal distributional assumption of residuals was found to be a better fit for price volatility of silver. Among the exogenous variables considered in this study, saving interest rate and general inflation rate have a statistically significant effect on monthly silver price volatility. In the EGARCH (3, 2) volatility model, the asymmetric term was found to be positive and significant. This is an indication that the unanticipated price increase had a greater impact on price volatility than the unanticipated price decrease in silver. Then, concerned stockholders such as portfolio managers, planners, bankers, and investors should intervene and pay due attention to these factors in the formulation of financial and related market policy.

Detecting long-range dependence with truncated ratios of periodogram ordinates

We propose new tests for testing hypotheses about the memory parameter that are based on ratios of periodogram ordinates. They are highly robust against conditional heteroskedasticity and outliers and are therefore of great value for the detection of long-range dependence in financial data. The robustness is obtained by truncation of a distribution with nonexistent moments. Tables of critical values are provided. The performance of the new tests is assessed by extensive simulations. Applying the tests to daily series of gold price returns and stock index returns, we find no evidence of long-range dependence characterized by a non-vanishing memory parameter. In the case of spread series (differences between interest rates at different maturities, gold prices and silver prices, related stock market indices), we find no evidence of a memory parameter well below 0.5.

Gold price forecasting research based on an improved online extreme learning machine algorithm

Accurate gold price prediction is highly essential for economic and currency markets. Thus, the intelligence prediction models need to be applied to price prediction. On the basis of long-term collected daily gold, the study proposes a novel genetic algorithm regularization online extreme learning machine (GA-ROSELM), to predict gold price data which collected from public websites. Akaike Information Criterion (AIC) is introduced to build the eight input combinations of variables based on the silver price of the previous day (Silver_D1), Standard & Poor. The 500 indexes (S&P_D1), the crude oil price (Crude_D1), and the gold price of the previous 3 days (Gold_D1, Gold_D2, Gold_D3). Eight optimal variable models are established, and the final input variables are determined according to the minimum AIC value. The proposed model (GA-ROSELM) solve the problem that OS-ELM model which is easy to generate singular matrices, meanwhile, experiments demonstrate this model performs better than ARIMA, SVM, BP, ELM and OS-ELM in the gold price prediction experiment. On the test set, the root means square error of this model (GA-ROSELM) prediction compared with five other models which decreased by 13.1%, 22.4%, 53.87%, 57.84% and 37.72% respectively. In summary, the results clearly confirm the effectiveness of the GA-ROSELM model.

The structure of prices in the neo-Sumerian economy (II); the wool:silver price ratio

Le système monétaire de l’économie marchande d’Ur III a imposé une structure de prix relatifs établissant la corrélation entre les valeurs équivalentes de tous les produits de base. La pierre angulaire de cette structure était la relation de valeur entre les deux principales formes de monnaie dans l’économie, à savoir le ratio “prix” bi-monétaire de 1 gur d’orge pour 1 sicle d’argent. Les “prix” de tous les autres produits pourraient être liés à ce ratio “prix”. Mon analyse du ratio du “prix” orge//argent poursuit l’étude des structures de “prix” et aborde plus avant le problème de la détermination des “prix” ou de leurs substituts dans l’économie de la fin du troisième millénaire. L’accent est mis ici sur le prix relatif de la laine, la deuxième matière première de l’ancienne économie mésopotamienne. Les fluctuations et la stabilité relative de ce prix relatif entre la dernière année de Šulgi et les premières années du règne d’Ibbi-Suen sont prises en compte. Une attention limitée est également accordée aux variations, le cas échéant, des prix relatifs introduits par différents types et qualités de laine. Une considération fondamentale consiste à distinguer les variations introduites par différents contextes définis par les fonctions et les provenances différentes des textes Ur III, principalement des bilans comptables, à partir desquels des données sur des valeurs équivalentes ont été extraites. L’influence déterminante de la disponibilité et du “prix” de l’orge sur le prix relatif de la laine est également suggérée.

The Classic Gold Standard: 1870–1914

Debates on issues about the economy, growth, inequality, fiscal responsibility, and financial crises frequently reduce to debates about money. Moreover, debates about money often reduce to the soundness of money, meaning the ability of a unit of currency to retain its value. Eventually, discussions about soundness touch on the classic era of the gold standard. The aims of this note are to (1) describe the mechanism of the international monetary system known as the gold standard during the time that it had the greatest influence on the global economy; (2) place the gold standard in context with other systems that preceded and followed it; and (3) dispel some enduring misconceptions. Excerpt UVA-F-1921 Nov. 7, 2019 The Classic Gold Standard: 1870–1914 Debates on issues about the economy, growth, inequality, fiscal responsibility, and financial crises frequently reduce to debates about money. Moreover, debates about money often reduce to the soundness of money, meaning the ability of a unit of currency to retain its value. Eventually, discussions about soundness touch on the classic era of the gold standard. The aims of this note are to (1) describe the mechanism of the international monetary system known as the gold standard during the time that it had the greatest influence on the global economy; (2) place the gold standard in context with other systems that preceded and followed it; and (3) dispel some enduring misconceptions. The Gold Standard in History The classic gold standard era commenced in the 1870s and ended with the suspension of the gold standard at the outbreak of World War I in 1914. Before this era, the major trading nations of the world relied on silver and gold coins as the basic money stock, supplemented by banknotes that were claims on silver and gold. The monetary standard of silver and gold was called bimetallism. Its chief advantage was that the two metals combined seemed to afford a sufficient supply of money with which to finance the growth of the developed economies. Its chief disadvantage was that it forced the major nations to declare a rate of exchange between the two metals (such as setting 16 ounces of silver equal in value to one ounce of gold), which created an arbitrage opportunity for speculators whenever the market prices of gold and silver diverged from the statutory exchange rate. . . .

Where was the Global Price of Silver Established? Evidence from London and New York (1878-1953)

This paper utilises a newly compiled, unique data set of silver prices for the London and New York markets, as well as the monthly Dollar-Sterling exchange rate from 1878 and 1953 to investigate the source and flows of information and price discovery. Results indicate that London was the dominant market and pricing leader of silver throughout the period analysed. Further evidence suggests that there existed dynamic information flows both within years and decades. These phenomena can be explained due to the use of silver for agricultural market payments and international silver production flows respectively.

Changes in the microstructure and optical properties of doped sodium-calcium silicate glass upon secondary heat treatment

The phase separation in Na 2 O-CaO-SiO 2 system containing four different microcomponents (Fe 2 O 3 , P 2 O 5 , Ag, Au) is studied. Two glass samples of close composition have been synthesized with the same set of the microcomponents and different content of gold and silver. Both glass samples have been melted in an open platinum crucible in an electric furnace at 1400°C for three hours and then poured and quenched on a cast iron plate and annealed at 520°C for one hour. Formation of the nanoparticles occurred upon secondary heat treatment at 550 – 600°C. The phase separation in the glass sample with a composition corresponding to the immiscibility limit in the NCS system occurs through formation of the core – shell type gold-silver nanoparticles, which serve as nucleation centers for the droplets of the second glass phase enriched in silica. Eventually, quartz crystallizes inside the droplets. The impact of these processes on the optical properties of the material including dichroism is discussed. The phase separation and quartz formation are confirmed by SEM and X-ray analysis data, respectively. The size of the nanoparticles was calculated from electron spectra using computer simulation. Iron in the glass samples thus obtained is in oxidation states +2 and +3, which determines the color of the glass before and after the secondary heat treatment in reflected light. The glass color in the transmitted light after the secondary heat treatment is attributed to the light absorption by nanoparticles. The dependence of the nanoparticle shape on the gold-silver ratio in glass is presented. The shape of nanoparticle is close to oblong or oblate ellipsoid within the range of the gold – silver ratios considered in the study.

Price volatility spillovers between supply chain and innovation of financial pledges in China

This study investigates the price volatility spillover of commodities in China using the Granger causality test and the variable structure Copula model, by employing data covering the daily average transaction prices of silver, copper, aluminum, rebar, and fuel oil from 2009 to 2017. The results show that the causal direction of price volatility spillover is uncertain, and that the impact from the correlation coefficients of copper and aluminum is the highest. Hence, there is a clear long-run price volatility spillover between silver and fuel oil as well as between silver and aluminum. Macroeconomic influences on different combinations of commodities present similar changing structure points, such as the debt crisis in Europe, the reduction of the quantitative easing monetary policy proposed by the U.S. Federal Reserve, and the promotion of RMB internationalization. Overall, our findings provide a theoretical reference for commercial banks to make full use of the volatility spillover effect between commodity pledges and their combinations, in order to achieve the goal of avoiding any price volatility risk.

Impact of Macro - Economic Factors on Indian Stock Market- A Research of BSE Sectoral Indices

Macro-Economic factors plays a major role in decision making. Evaluation of macroeconomic environment is required to examine the behaviour of stock prices, which further influences the investor’s investment behaviour. Even though some macro-economic factors are not directly related to the company or industry, but those factors has an impact on stock prices, further economic activity in the domestic and global level has its own impact on stock market. When economy of the country grows hastily, it leads to faster growth in the industry and vice versa. Financial market plays a central role in the performance of financial system of an economy. Stock market is a market where securities of listed companies are exchanged between different investors, it is very responsive market which, gives a stage to investors to invest their money in various securities. Market indices are the tools to measure the performance of various securities of stock market and Investors make use of those market indices to analyse performance of those industries in which, they prefer to invest. This study takes into account six macro-economic factors (Crude oil Price, Gold Price, Silver Price, Exchange Rate, Inflation and Interest Rate) to study & analyse the impact of these variables on selected sectoral indices at BSE, SENSEX, S&P BSE BANKEX, S&P BSE Oil and Gas, S&P BSE Capital Goods, S&P BSE Consumer Durables, S&P BSE Reality, S&P BSE PSU and S&P BSE Power. The study shows that gold price, exchange rate, consumer price index and interest rate are positively correlated with four indices but crude oil price and silver price have positively correlated with 3 indices. So from the result it is clear that investor need to take of all the variables for their investment decision and the investment banker also take care of these indicators before giving suggestion to their clients

Hedging U.S. metals & mining Industry's credit risk with industrial and precious metals

This study examines the conditional correlation and the resulting optimal hedge ratios between the Credit Default Swap (CDS) spreads of the U.S. metal and mining industries, and the prices of copper, platinum, silver and gold using the daily date from December 14, 2007 to August 18, 2018. It compares volatility and conditional correlation of the CDSs and the metal prices by employing multivariate GARCH family models which capture distinct characteristics of financial time series. It utilizes rolling window estimation techniques and constructs the one-step-ahead out-of-sample forecasts for the dynamic conditional correlations and thereafter the optimal hedge ratios. In general, our results show that copper provides the best possible hedge for dealing with the U.S. metals and mining industries’ credit risks. Our results are robust under alternate model specifications, choice of model refits and distributional assumptions.

Energy and non-energy commodities: An asymmetric approach towards portfolio diversification in the commodity market

We investigate the presence of short- and long-run asymmetric relationships between energy and non-energy commodities for weekly data from January 2010 to June 2018. Using the non-linear ARDL methodology, we show that oil prices have significant long-run negative effects on the prices of gold and silver, indicating that for both metal commodities, oil price increases lead to more decrease than the subsequent oil prices decrease. Crude oil, among other energy commodities, is found to offer more diversification benefits when combined with gold or silver; however minimal diversification benefits can result from combining crude oil with wheat or platinum. Gas futures, among other energy markets, offer more diversification opportunities when combined with copper, wheat, platinum or palladium, while coal offers maximum diversification benefits when combined with gold, silver or wheat. Our results are robust when NARDL is applied to the daily data and also when the analysis is conducted using the causality-in-quantiles test.

Modeling and forecasting time series of precious metals: a new approach to multifractal data

We introduce a novel approach to multifractal data in order to achieve transcended modeling and forecasting performances by extracting time series out of local Hurst exponent calculations at a specified scale. First, the long range and co-movement dependencies of the time series are scrutinized on time-frequency space using multiple wavelet coherence analysis. Then, the multifractal behaviors of the series are verified by multifractal de-trended fluctuation analysis and its local Hurst exponents are calculated. Additionally, root mean squares of residuals at the specified scale are procured from an intermediate step during local Hurst exponent calculations. These internally calculated series have been used to estimate the process with vector autoregressive fractionally integrated moving average (VARFIMA) model and forecasted accordingly. In our study, the daily prices of gold, silver and platinum are used for assessment. The results have shown that all metals do behave in phase movement on long term periods and possess multifractal features. Furthermore, the intermediate time series obtained during local Hurst exponent calculations still appertain the co-movement as well as multifractal characteristics of the raw data and may be successfully re-scaled, modeled and forecasted by using VARFIMA model. Conclusively, VARFIMA model have notably surpassed its univariate counterpart (ARFIMA) in all efficacious trials while re-emphasizing the importance of co-movement procurement in modeling. Our study’s novelty lies in using a multifractal de-trended fluctuation analysis, along with multiple wavelet coherence analysis, for forecasting purposes to an extent not seen before. The results will be of particular significance to finance researchers and practitioners.

Sensitivity Analysis of Economic Variables using Neuro-Fuzzy Approach

Sensitivity analysis (SA) is a vital task for decision making in economic management. In this paper, a novel fuzzy sensitivity analyzer (FSA) is proposed to analyze the sensitivity of economic variables. The proposed FSA algorithm consists of an adaptive neuro-fuzzy inference system (ANFIS) that is adjusted for forecasting economic time series. Based on the output of ANFIS, FSA can determine the importance degree of parameters. In the numerical studies, the proposed method is applied for the sensitivity analysis of oil and gold time series. According to the results, FSA indicates that oil price is highly dependent upon the inflation rate, dollar index and market index while OPEC production level and gold price have less impact. Furthermore, in the gold price modeling, the highest sensitivity is obtained from silver price while demand for gold is more a function of market index and inflation rate. The proposed method can be used in many SA applications.

Impact of silver price uncertainty on solar energy firms

To the best of our knowledge, we are the first to investigate if silver market uncertainty impacts the stock price performance of solar energy firms. Such investigation could be of vital importance to investors and policymakers given that silver is heavily utilized in photovoltaic (PV) process for producing solar energy. Methodologically, we apply the GARCH-jump process to estimate the effect of silver market uncertainty, proxied by silver volatility index (VXSLV), on the global solar energy stock indexes. Using a recent data set, we document the following key findings. First, there exists a negative effect of VXSLV on the equity indexes under study. This result suggests that an upsurge in silver market volatility would create a chaos in the solar energy business. Second, the impact of silver volatility index on renewable energy firms holds even when we control for the impact of oil price volatility (OVX), albeit OVX, when compared to VXSLV, transmits higher risk to alternative energy stocks as evidenced by the magnitudes of the estimated parameters. Third, the impact of silver market uncertainty appears to be symmetric implying that increases and decreases in VXSLV would have a homogeneous impact on the solar energy stock returns. We finally show that equity prices of solar energy firms are characterized by time-varying jumps. The implications of the results are discussed as well.

The dynamic causality between gold and silver prices in India: Evidence using time-varying and non-linear approaches

This paper explores the dynamic causal relationship between the returns of gold and silver in Indian market using monthly data for the period June 1991–June 2018. In doing so, we use three methods to obtain robust results and draw solid implications. Firstly, a rolling window bootstrap approach is employed to examine the dynamic causal relationship. Secondly, we use wavelet based time-varying Granger-causality test and finally a non-linear Granger-causality test is also applied to know the causality between the variables in a non-linear framework. Our results demonstrate the existence of significant time-varying negative and positive effects from gold to silver while the reverse is not true. The time varying causality results also commensurate with the macro-economy conditions and diverse monetary and fiscal policies.

Geochronology and ore genesis of the Shuangjianzishan Ag-polymetallic deposit, Inner Mongolia, China

The newly discovered Shuangjianzishan Ag-polymetallic deposit is in the southern part of the Great Xing’an Range of Inner Mongolia in NE China, and is China’s largest Ag deposit with a 21,665 t silver reserves. Three types of hydrothermal mineralization are evident: relatively early disseminated and stringer mineralization, succeeded by a breccia-hosted and younger vein-type mineralization. Preliminary studies show that the deposit might represent a mixed ore system where the early disseminated and stringer mineralization is overprinted by the breccia-hosted and vein-type mineralization.Muscovite Ar–Ar and Laser Ablation-Inductively Coupled Plasma-Mass Spectrometry (LA-ICP-MS) U–Pb dating of zircon, and geochemical analyses were completed at the deposit. The zircon U–Pb dating for the argentiferous quartz-veins yield a crystallization age of 148 ± 1 Ma, and muscovite coexisting with sulfides yields an Ar–Ar plateau age of 147 ± 2 Ma. Both dates, being the same within error indicate that the mineralization is ca. 148 Ma, which is a date that coincides with a wide-spread ca. 159–144 Ma magmatic event in the region.Stable isotopic data (δ18OH2O and δD) collected from fluid inclusions from the quartz vein-type mineralization are characterized by systematic and small variation, and this difference reflects the higher fluid fluxes and smaller amount of rock-buffering for the quartz veins. The δ13C values of −10.6 to −14.3‰ and δ18O values of 0 to 5.1‰ for calcite from the veins indicate that the carbon in the mineralizing fluid had a mixed magmatic and highly evolved meteoric fluid source.In addition, the δ34S values of 28 sulfides range from −4.70 to 1.40‰, which are values markedly close to that of granitic rocks and the mantle, indicative of a magmatic source. Lead-isotopes values for sulfides are indicative of a mixed mantle and crustal source.Many features of intermediate-sulfidation epithermal deposits are present at Shuangjianzishan, which is therefore interpreted as an intermediate sulfidation deposit derived from a mixed source during a phase change in temperature and pressure interpreted as being related to fluid mixing and boiling.

Prediction of Gold and Silver Prices in an Emerging Economy: Comparative Analysis of Linear, Nonlinear, Hybrid, and Ensemble Models

This study compares the accuracy of different forecasting techniques for gold and silver returns in a leading emerging economy. The study employs four forecasting models: autoregressive integrated moving average (ARIMA), artificial neural network (ANN), hybrid, and ensemble models. The study takes data of more than 7 years and forecasting is carried out for different forecast horizons varying from 1- to 20-steps ahead. The results reveal that ARIMA model is the best model to predict the gold returns, whereas, the ANN model along with the ensemble model are the best to predict the silver returns. The results also indicate that there exists nonlinear patterns in the time-series data of gold and silver returns. The study has significant implications for investors, academia, and policymakers.

Silver prices and solar energy production.

The goal of this paper is to identify, for the first time, the role of solar production in driving silver prices. The empirical analysis makes use of the ARDL model and the combined cointegration. The results, spanning the period 1990-2016, document that stronger solar installed capacities, as well as higher gross electricity production from solar sources, lead to higher silver prices. The findings could be of great importance to silver suppliers and to energy policymakers and regulators, as well as to solar panel manufacturers.

The effect of commodity price changes and USD/IDR exchange rate on Indonesian mining companies’ stock return

There are many variables that will be influencing stock return. Some of those variables are come from the commodity market, such as gold, price and crude oil price changes, and also come from the exchange rate market. This study is about to test the effect of changes prices of gold, silver, crude oil, and exchange rate to the stock return of mining sector companies in Indonesia. There are 48 companies engaged in the mining sector listed on the Indonesia Stock Exchange (IDX). From 48 companies this study uses the purposive sampling method to choose a sample within the criteria, and there are 13 selected companies that will be the object of this study. This study also used an analysis tool GARCH (1,1) to avoid abnormal data. Before advancing to GARCH (1,1) analysis, the data must be tested the stationary first by using the Augmented Dickey-Fuller Test (ADF) to make sure the data stationary or not by using level, 1st difference, and two difference. The result shows that gold has a significant positive effect on stock return mining sector companies such as INCO, KKGI, PTBA, and TINS. Silver has a significant positive effect on HRUM companies and negatively on RUIS. Crude oil has a significant positive effect on HRUM and PTBA firms. Exchange rates have a significant negative impact on companies. ANTM, CTTH, DOID, ELSA, HRUM, ITMG, and KKGI. JEL Classification : E03, F31, G11, G23 DOI : https://doi.org/10.26905/jkdp.v23i1.2084