Gold Spot Price Research Articles

An Investigation into How Fluctuations in Global Crude Oil Prices Impact Tadawul All-Shares Index (TASI) (January 1995-December 2017)

This paper investigates the effect of fluctuations in the global oil market on the movement of the TADAWUL All Shares Index, TASI, by using monthly data for the period between January 1995 and December 2017. To capture the impact of the global oil market, this paper uses the two main benchmarks in the global oil market, West Texas Intermediate (WTI), and Brent Crude oil. As the stock market is known to be stochastic, a set of secondary explanatory variables is added to this research. It consists of the following: inflation rates, short-term interest rates, gold spot prices, level of imports, level of funds invested in private securities by the private sector, and proxy for global equity markets, FTSE 100. The FTSE 100 has a significant impact on the TASI index, the coefficient is close to 1. The significant impact of the FTSE 100 on the TASI index could signal the existence of many dual investors in both indices. The results show that all variables have an impact on the TASI index at a statistically significant level. Despite the positive impact of spot prices on both benchmarks, the futures strip curve for both benchmarks are inversely related to the predicted variable. Also, the TAIS index is more exposed to changes in the WTI than changes in Brent Crude. It is clearly shown in the coefficient values, .359 and .302, for WTI and Brent Crude respectively. Moreover, over the sample period there is an increase in the level of the explained variation in the TASI index by the explanatory variable. This would indicate that TASI has become a less speculative and more sophisticated market. Finally, as this paper provides evidence for the impact of the global equity market on TASI, it would open a window for future research to examine the impact of other international equity markets on TASI.

A novel prediction model to evaluate the dynamic interrelationship between gold and crude oil

AbstractGlobal events, such as the pandemic and European conflicts, have caused significant inflation and high volatility in gold and crude oil prices. This has garnered substantial international attention while banks, governments, and businesses are devoted to developing strategies to hedge against the potential impacts of economic uncertainties. Focused on addressing economic uncertainties, this study delves into the crucial role of the relationship between gold and crude oil in shaping global financial and economic dynamics. The primary objective of this study is to conduct a comprehensive analysis and construct a rolling SARIMAX model for predicting the rolling 12-months correlation of the Gold–WTI and Gold–Brent relationship. Monthly data on Gold spot prices, WTI futures, and Brent futures is collected from May 1983 to December 2022. Three sophisticated data analysis techniques, the Rolling Correlation method, the SARIMAX model, and the Rolling Model are integrated to develop the Rolling SARIMAX module. R-square values of this newly developed model achieved 89.8% and 88.4% for predicting the rolling correlation for Gold Spot Price-WTI Futures and Gold Spot Price-Brent Futures, respectively, while the mean absolute percentage error was 10.33% and 10.84%, respectively. The higher accuracy in correlation prediction between gold and crude oil prices can present critical insights for risk management, economic planning, strategic investment, economic cycles, and global economic outlook. This newly developed prediction model adeptly handles both linear and non-linear relationships while adapting to external variables in dynamic market scenarios. Its judicious balance between complexity and practicality positions it as a sophisticated analytical tool with real-world applicability, setting a new benchmark in financial market analysis.

A COMPARISON OF ARCH MODELS: THE DETERMINANTS OF BITCOIN’S PRICE

The aim of this study is to determine the number of transactions among the currencies, which will eventually become a part of our lives, cannot be physically held, can move quickly, and emerge as a new shopping and investment tool in the changing world order, as of the year (2023) when this study was conducted. The study focuses on the analysis of the variables that affect the most popular currency, Bitcoin. Although the analysis of variables that influence Bitcoin was determined as the primary aim of the study, the study also attempted to reach a general conclusion about the variables affected by the cryptocurrencies. Since there is no other cryptocurrency that is traded as much as Bitcoin, Bitcoin is thought to be a good model for the analysis of cryptocurrencies. The method used in the study was autoregressive conditional heteroskedastic (ARCH) models. It is believed that the most suitable models for the Bitcoin variable, whose value changes every second, are ARCH and its derivatives. Other models selected from the ARCH models were also added to the analysis as a method. The models used in the study can be listed as follows: linear ARC, generalized ARC (GARCH), exponential GARCH and threshold GARCH. A statistical model called autoregressive conditional heteroscedasticity (ARCH) is used to study the volatility of time series. Through the provision of a volatility model that more closely mimics actual markets, ARCH modeling is utilized in the financial sector to quantify risk. According to ARCH modeling, periods of high volatility are followed by even higher volatility, and periods of low volatility are followed by even lower volatility. In this study, 5 different variables were selected using literature to analyze the variables affecting Bitcoin returns using ARCH models. The dependent variable in the study is the price of Bitcoin. The remaining variables were included in the models as independent variables. These variables are actually variables that are accepted and selected as the best among a set of variables. In other words, 15 variables were first added to the study using the literature. After this, a correlation analysis was carried out. As a result of the correlation analysis, the variables with the highest correlation with the price of Bitcoin, which is the dependent variable, and the lowest correlation with each other were retained in the model. These variables are Bitcoin Price, Crude Oil Spot Price, Euro-Dollar Parity, Gold Spot Price and NASDAQ Composite Index. The study period is between 2020 and 2023 and it was studied using daily data. Days with no data were removed from the daily period from 2020 to 2023 and loss of information was prevented. After removing missing observations, this study examined the remaining 837 observations. During the research, while running the models created using different methods, it was found that the model that gives the best result is the GARCH model. In other words, when modeling the variables affecting bitcoin (cryptocurrency from the perspective of the population), it was seen that the GARCH model gave the best results when comparing linear ARCH, generalized ARCH (GARCH), exponential GARCH, and threshold GARCH of the ARCH model. Comparing the output of the GARCH model with other ARCH models not included in this study can be a recommendation for the future study

Will Gold Prices Persist Post Pandemic Period? An Econometric Evidence

Recurrent stock market fall and rise sequel by COVID-19, rising global inflation, increase in Fed interest rates, the unprecedented meltdown of technology stocks, fear of trade wars, tightening of governments’ fiscal policies call for a new trend in international investing. It is time for the investors to rethink, rebalance and reset their investment strategies to position and protect their portfolios during and post-pandemic period. This paper attempts to forecast the gold prices for the post-pandemic era and explores whether gold will serve as a decisive hedge during this transition period. The techniques of ARCH, GARCH, E-GARCH, A-PARCH, and GARCH-M is employed in forecasting the conditional volatility of gold spot price from Multi Commodity Exchange (MCX) of India. A total of 3631 observations were collected from the daily spot prices of gold from January 2009 to December 2022. The findings show that the gold prices in India are highly persistent similar to other emerging markets and that gold will remain a safe haven for investors and institutional investors in the post-pandemic period. This paper is the first of its kind to forecast gold prices for the post-pandemic period. The forecast price of 10-gram gold is expected to trade for 65,948 ₹ in the Indian MCX by 2026 if the gold prices behold its previous momentum. This forecast will help the investors to plan their portfolio diversification for the post-pandemic period.

Gold spot and futures market cross-correlation and time-frequency volatility: an application of MF-ADCCA-AFD

ABSTRACT This study employs the MF-ADCCA-AFD method to investigate the multiple fractal characteristics of the interrelationship between the gold spot and futures, and to explore the time-frequency distribution of gold prices. The price decline is found to have a more persistent impact on the relationship between gold futures and spot prices, and its cross-correlations are asymmetric. Compared to gold spot prices, gold futures prices have more volatile phases, and their ranges are larger. By performing time-frequency analysis, investors can predict when a price is about to fluctuate, thereby providing them with valuable investment advice.

Return Volatility Spread in Commodity Volatility Indices: Spot and Future Market Research

Volatility Indices are an important indicator for investors to accurately predict returns and risks in case of uncertainty in the markets. In this study, the effects of the gold, silver, and oil volatility indices (GVI, SVI and OVI) on the returns and volatility of both spot and futures assets were investigated using the VAR-EGARCH procedure. The findings of the study reveal that both the GVI and gold futures prices have a positive effect on gold spot prices. At the same time, it has been determined that gold futures prices are obtained from the GVI and gold spot prices. On the other hand, although SVI and future prices were effective on silver spot prices, only SVI lagged prices were effective on silver futures. Finally, OVI and oil future returns were ineffective on oil spot prices, and only OVI returns were effective on oil future prices.

Attention-return relation in the gold market and market states

We document that the attention-return relation in the gold spot market is sensitive to market states since we identify a positive (negative) association between returns and contemporaneous attention during bull (bear) markets. This sensitivity is influenced by the magnitude of the returns, since the significance of the relation, based on a quantile model, increases almost monotonically as the return distribution moves away from the median. We further find that investor attention is influenced by recent gold spot price trends in both market states. Overall, these results suggest that gold return past performance attracts the attention of individual investors who, in turn, trade to profit from the trend, reinforcing the price movement, which is consistent with the time-series momentum framework.

A study of excess volatility of gold and silver

In this paper, we discuss the case of strong path dependency in asset prices from the theoretical and empirical standpoints. We demonstrate persistence of excess volatility in the gold spot price data that engenders excessive path dependence, whereas it is not the same with silver. We use the extreme value estimator (Rogers & Satchell, 1991) and the VRatio (Maheswaran, Balasubramanian, & Yoonus, 2011). The data for the study is for the period January 2001 – December 2016. We observe that the strong mean-reverting characteristic in gold makes it a better investment choice than silver, in general.

Statistical properties of the detrended multiple cross-correlation coefficient

Cross-correlations are ubiquitous in nonstationary multivariable systems. By using detrended cross-correlation (DCCA) technique, recently a new multiple cross-correlation coefficient DMC was proposed to quantity the correlation between multivariate and a target variable. We studied the statistical properties of DMC in detail. More specifically, we first proved that 0≤DMC≤1, and then deduced that the DMC is approximately χ2 distributed for overlapping windows with different window size. In addition, the range of DMC was determined numerically by statistical test, where the multiple correlations can be regarded as statistically significant. Furthermore, we discussed the influence of the cross-correlation among the target variable and independent variables on DMC. Finally, by utilizing this statistical test on financial system and environment data, we found that the US dollar index affects international gold spot prices and West Texas Intermediate oil price greatly, and PM2.5 dominates in Beijing’s AQI system.

The role of gold futures in mitigating the impact of economic uncertainty on spot prices: Evidence from China

This paper studies the co-integration relationship and volatility spillover effect between China's gold futures and spot prices through the VECM-BEKK-GARCH model. Then, MSGARCH and DCCE-GARCH are applied to study the relationship among China's gold futures market, spot market price volatility and the stabilization effect in uncertain economic environments. This paper enriches the current research, providing gold market participants with hints to address economic uncertainty. The empirical results show that China's gold futures market has a weak stabilization effect on spot price volatility. In scenarios with uncertain economic information and uncertain macroeconomic changes, the correlation between gold futures and spot price volatility is reduced in China, and the role of gold futures in stabilizing the spot price weakens. Furthermore, with economic uncertainty, the fluctuation range of the gold futures price is greater than that of the spot price, with a tendency of more frequent fluctuations. This also means that the effectiveness of the futures market in regulating the spot price will be reduced, and gold market regulators need to stabilize the market through alternative methods to futures.

Half Century of Gold Price: Regime-Switching and Forecasting Framework

This paper studies the history of gold price in the international context using Markov-switching models. The literature surrounding the Markov-switching model is reviewed from the earliest iterations of Hamilton to recent developments. We show applicability of Markov stochastic process in forecasting commodity prices; in particular, the gold spot price. The research imposes the features of Markov regime-switching models, considering gold as a financial asset to offer a comprehensive methodology for forecasting commodity price. The paper discovers that applying Markov regime-switching could significantly improve the forecast abilities in commodity prices. Analysis of the model outcome indicates that the abnormal increases of gold price in history always resulted from special economic conditions. This study makes a novel contribution to the field by demonstrating that the impact of CPI change to gold price is subject to the regimes, which is more sophisticated than what has been commonly accepted in economics literature to date.

A study of cointegration of gold market of the emerging and developed economies

Gold, which is considered to be the most precious metal (Bilal, Talib, Haq, Khan, & Naveed, 2013), from ancient times has been considered as a very conservative investment. Studies examining the utility of gold have found evidence in favour of gold as a source of the hedge (Narayan, Narayan, & Zheng, 2010; Bampinas & Panagiotidis, 2015), diversification (Ibrahim, 2012; Hoang, Lean, & Wong, 2015), as well as a safe haven in times of adverse market movements (Ciner, Gurdgiev, & Lucey, 2013; Bredin, Conlon, & Potì, 2015). This paper attempts to study how global gold price trends impact domestic gold prices and domestic gold price trends contemplate in international gold markets. The study has been based on 3157 observations of daily data recorded over a period of 13 years – from March 2005 to December 2018 – to show the relationship between the USA and India’s gold market. This paper fills the need for empirical evidence for the short and long-term interrelation between India and USA gold markets and the results show no evidence of a long-term association between Indian and COMEX gold spot prices.

Estimating copper concentrates benchmark prices under dynamic market conditions

Copper concentrates are the primary product sold by copper mines to traders or smelters. Their price is set in private agreements following unofficial market practices which, along with the fact of the wide variety of concentrates layouts traded worldwide, make problematic for any market participant to have a clear price reference for a specific concentrate. This paper presents a copper concentrates benchmark price model which provides suitable short-term price estimations based on metals and discounts forecasts, as well as on copper, gold and silver spot and future prices data from the LME and COMEX. The model, which redeems considerably low forecast error values for the short-term concentrate prices, constitutes a useful and applicable tool for miners, traders and smelters to set a benchmark price level for their copper concentrate transactions, also helping them optimize their operations, as well as estimate their immediate liquidity needs or their actual necessity to hedge the price risks associated to their concentrate trading. In addition, different concentrates layouts have been analyzed to test the model's behavior with the most common specification and blends of copper concentrates demanded by the market, as well as to portray the model's forecasting capacity and its ability to convey information on the future relevance of the different components of pricing in the most frequent timeframe in which copper concentrate trading takes place.

A Note on the Asymptotic Normality Theory of the Least Squares Estimates in Multivariate HAR-RV Models

In this work, multivariate heterogeneous autoregressive-realized volatility (HAR-RV) models are discussed with their least squares estimations. We consider multivariate HAR models of order p with q multiple assets to explore the relationships between two or more assets’ volatility. The strictly stationary solution of the HAR(p,q) model is investigated as well as the asymptotic normality theories of the least squares estimates are established in the cases of i.i.d. and correlated errors. In addition, an exponentially weighted multivariate HAR model with a common decay rate on the coefficients is discussed together with the common rate estimation. A Monte Carlo simulation is conducted to validate the estimations: sample mean and standard error of the estimates as well as empirical coverage and average length of confidence intervals are calculated. Lastly, real data of volatility of Gold spot price and S&P index are applied to the model and it is shown that the bivariate HAR model fitted by selected optimal lags and estimated coefficients is well matched with the volatility of the financial data.

The Impact of COVID-19 on Gold Price Volatility

Purpose: This article investigates the implications of the spread of COVID-19 on gold spot prices. Design/Methodology/Approach: We use GARCH and GJR-GARCH models based on daily gold returns over the period 2012-2020 to analyze the impact of the coronavirus on the volatility of gold returns. Findings: We find a positive correlation between the increasing number of global coronavirus cases and increases in gold price. Using GARCH and GJR-GARCH models, we find a significant positive impact of COVID-19 on the conditional variance equation, indicating that the coronavirus may indeed increase the volatility of gold returns. This relates to the fact that the spread of the virus increases uncertainty with regard to the future of economic and financial markets, causing the demand for gold to increase and in turn pushing prices upwards, a trend which may be likely to continue until a vaccine or other treatments begin to stabilize the global economic outlook. Practical Implications: The issue of volatility is of significant concern to both investors and policymakers who base decisions on the relative stability of both individual financial markets and the world economy. Furthermore, volatility estimation is an essential factor in many models and has broad application to the market risk management practices of firms. Finally, understanding the volatility of the gold market is crucial for any analysis of current and future expectations regarding the risks associated with coronavirus which apply to global markets. Originality/Value: The lockdown restrictions which have been widely implemented across the globe to curb the spread of the virus have included travel prohibitions and border closures, stay-at-home and work-from-home orders, and extensive business closures, all causing immense fallout for the global economy. In the current study, we analyze for the first time the impact of the coronavirus on gold spot prices by examining their correlation with the number of cumulative global cases and daily new cases.

A causal study on gold, SENSEX, and gold exchange traded funds

Gold exchange traded funds help investors to generate the returns based on the movements of underlying commodity. In current scenario, gold ETFs are emerging as one of the best ETFs for the investments. The present study intended to determine the causal relationship between spot prices of gold, SENSEX, and ten selected BSE listed Gold ETFs. The closing prices gold ETFs, SENSEX and spot prices of gold was collected during 2015–2018. The study applied ADF root test, co-integration test, and Granger causality test to study the causal relationship. The results of the study revealed that out of ten, six gold ETFs has significant causal relationship with gold price and only two schemes has significant relationship with BSE SENSEX during 2015–2018. The study concluded that gold ETFs are largely affected by the spot price movements of gold. Therefore, an investor must understand the pricing dynamics of the underlying asset of ETF.

Forecasting Short Run Gold Spot Price in India Using Mixed Frequency Models

Consumers, as well as investors, rely on bullion price forecasts for making sound investment decisions in the spot as well as futures markets. Enhanced accuracy of forecasts bodes well for the efficiency of markets as it facilitates rational expectations by agents in the economy which in turn may improve policy transmission. The usual time series forecasting methods require the dependent variables and independent variables to be of the same frequency e.g. to forecast monthly gold price, we require independent variables to be available at monthly frequency even if they are available at a higher frequency(daily or hourly). This is accomplished using aggregation of data which leads to loss of information. Such high-frequency data contains critical signals on the evolution of the dependent variable which can be leveraged to improve the forecast accuracy. In this work, I used a mixed frequency forecasting model for forecasting gold price in India using crude oil prices, stock index and USD-INR exchange rate as high-frequency predictors. I further demonstrate that the forecast accuracy achieved using the Mixed Data Sampling(MIDAS) regression outperforms the forecasts achieved using ARIMA and ARIMAX models.

Say anything you want about me if you spell my name right: the effect of Internet searches on financial market

In this paper, we focus on the influence of attention from investors on the dynamics of different financial assets. Investor attention is measured by the number of Google searches for three types of assets: stocks, gold and cryptocurrencies. We analyse the daily price of: the Standard & Poor’s 500 (S&P 500), the Russell 2000 Index (RUT), Bitcoin and the gold spot price in USD. The study covers a period of 5 years: 2013–2018. According to the results, the prices of different assets react differently to changes in the level of investor attention (and vice versa), and this relationship changes over time. Bitcoin seems to be the most sensitive to the changes in investor attention and changes in its dynamics appear to influence investor attention the most. Changes in the volatility and trade volume of stocks occur simultaneously with changes in investor interest. However, during periods of increased stock volatility, we can observe increased investor interest in gold over any other asset. The price of gold itself has proven itself to be most immune to changes in investor attention.

Analyzing the Reaction of Mining Stocks to the Development of Copper Prices

ABSTRACT Copper is considered one of the most important minerals in the world; however, most of the finance literature focus on determining the relationship between changes in gold spot prices and mining stock returns. To fill this literature gap, we analyze the impact of changes in copper spot and futures prices on the stock returns of copper mining firms. Considering a sample of high market-cap firms, we find evidence of a positive but inelastic relationship between copper stock returns and changes in copper prices. Additionally, we determine that the 2008–2009 global crisis influenced investors’ decisions thus generating a negative impact on copper stock returns. Finally, we provide evidence to reject the hypothesis of integrated markets; indeed, changes in copper prices have a larger impact on stock returns of copper mining firms traded in more developed markets (New York, Toronto, and London) compared with stocks traded in a less developed one (Lima).

GOLD: HEDGE, SAFE HAVEN OR DIVERSIFIER FOR INDONESIAN CAPITAL MARKET

This study scrunitize whether gold can act as hedge, diversifier and safe haven in Indonesian Capital Market. Data employed in this study are monthly Jakarta Composite Index and monthly international gold spot price during January 1999 to September 2013. Using Generalized Autocorrelation Conditional Heteroscedasticity (GARCH), the results show that gold can not act as hedge and diversifier in Indonesian Capital Market because both instruments have comovement. Furthermore, gold can not act as robust safe haven in Indonesian Capital Market. These findings suggest that it is better to investors whose already hold Indonesian stocks as their portfolio to exclude gold as their part of portfolio and divert their portfolio to other instrument with risk free characteristic such as Indonesia sovereign debt and Indonesia sovereign Sukuk during financial market crash.