Gold Price Research Articles

The ugly side of the Africa-UAE (United Arab Emirates) gold trade: Gold export misreporting and smuggling

United Arab Emirates (UAE) has become one of the major world's gold hubs and sources most of its gold from Africa. This paper examines the magnitude and patterns of gold export misreporting in the Africa-UAE gold trade. Africa-UAE gold trade is found to be characterized by overwhelming misreporting and smuggling. The most common practice is found to be gold export underreporting. To examine the drivers of gold export misreporting in the Africa-UAE gold trade, a dynamic panel data model was estimated. The estimation results indicate that export tax, the gold price, and institutional factors are the main drivers of gold export misreporting in the Africa-UAE gold trade. The drivers of pure gold smuggling are found to be the gold price and real exchange rate. The results indicate also that both gold export misreporting and smuggling exhibit some persistence behavior. The findings of this study point to improving African countries' institutions to reduce the extent of gold export misreporting in the Africa-UAE trade. Export tax rate can also be reduced to lower the incentives of gold export underreporting. African countries can also work towards harmonizing the regulatory frameworks concerning gold trade, to curb gold smuggling across African countries. UAE has a major role to play in deterring gold smuggling from Africa; export documents and proof of payment of export taxes from the exporting countries, should be required from those hand-carrying gold as they declare their gold to customs officers in Dubai.

Is the prediction of precious metal market volatility influenced by internet searches regarding uncertainty?

There are countless articles on the relationship between economic uncertainty and the precious metals market, but studies examining whether economic uncertainty related queries (EURQ) can provide valuable information for the prediction of precious metal price volatility are scarce. Therefore, this paper examines the predictive power of EURQ for price volatility in the precious metals market with the GARCH-MIDAS model structure. The parametric results indicate a significant positive effect of the EURQ indicator on precious metal market volatility and mixed results for its asymmetric variation. Additionally, we investigate the performance of forecasting models that incorporate long- and short-term asymmetric effects from an out-of-sample perspective. We discover that using information from negative changes in returns and positive and negative shocks to the EURQ is more beneficial for forecasting precious metal futures price volatility than forecasting models that consider only a single indicator. Our findings emphasize the importance of queries on uncertainty-related topics in forecasting gold, silver, and copper price volatility and provide valuable guidance for rationally planning investment ratios.

Unveiling the effects of mineral markets, fintech and governance on business performance: Evidence from China

The study intends to empirically estimate the effects of mineral markets, fintech, and governance on business performance in China. For this purpose, the study collects the data of 18 Chinese companies belonging to different sectors over the 2009 to 2020 period. To address the issue of cross-sectional dependence, “Panel Corrected Standard Error (PCSE) estimation is applied in the study. According to the study findings, there is a significant and negative impact of gold prices and coal prices and a positive impact of silver prices on business performance in China. No significant impact of oil prices on business performance is observed in the study. Governance is found to have a significant positive impact on business performance. However, no significant effect of fintech is found in the analysis. The study recommends that business firms should adopt appropriate measures to prevent bad effects of mineral market conditions on their profitability or performance. Strengthening the regulatory or institutional system and promoting innovations in the financial sector are also recommended by the present study.

Geopolitical Risk And Responses To Financial Markets

The Geopolitical risk (GPR) generated by the Russian invasion of Ukraine has triggered various impacts and responses in terms of financial markets and investors’ sentiments regarding sensible actions. The purpose of this study was to focus on GPR and responses to financial assets and commodities using a vector autoregressive (VAR) model. The results uncovered differences in the various responses of financial assets and commodities in different durations. It was found that under increasing GPR, gold is a safe haven for energy commodities, while bitcoin is safe haven for the capital market, and treasury bonds and US dollars are a safe haven for other various financial assets as well as commodities such as energy. Since gold’s behavior is determined by itself, investment in gold can help reduce risk in portfolios when there is an increase in geopolitical risk and at times of a bearish market.

Exploring the effectiveness of a multilayer neural network model for gold price prediction

Exploring the effectiveness of a multilayer neural network model for gold price prediction

Gold Smuggling in India and Its Effect on the Bullion Industry

This study strives to examine when and where most of the gold smuggling takes place in India. It further analyses the causal relationship between smuggled gold and other macroeconomic variables. Finally, it analyses how the smuggled gold affects the Indian bullion industry. The data related to gold smuggling has been sourced from the website of the Directorate Revenue Intelligence and analysed using graphs and the Granger causality test. The variables used in the study are the quantity of smuggled gold, exchange rates, the major stock indices in the world, the number of auspicious days in a month, domestic and international gold prices, India’s jewellery export, the GDP, customs duty, and the domestic gold supply. The results revealed that most of the gold smuggling takes place on Fridays and mostly occurs in the months of October, November, and December. The states of West Bengal, Delhi, Maharashtra, and Tamil Nadu account for most of the gold smuggling in India. A positive correlation is observed between the smuggled gold, India’s gold demand, the number of auspicious days in the month, India’s jewellery export, India’s GDP, India’s domestic gold supply, and stock indices such as SENSEX, FTSE100, DFMGI. Gold smuggling in India is caused by India’s gold demand, the level of jewellery export, the GDP, domestic and international gold prices, and India’s customs duty.

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.

Association between Select Financial Variables and the Indian Stock Market

The study was carried out to examine the association between select financial variables and the stock market in India using the time-series data from July 2011 to June 2023. Correlation analysis demonstrated that BSE-Sensex was positively associated with CPI, exchange rate, and gold price but negatively associated with crude oil price. Economic regression analysis illustrated that BSE-Sensex was significantly influenced by its past value but had no relationship between select financial variables. The governments may intervene in currency markets to manage exchange rate fluctuations; impose price controls to prevent excessive price increases, encourage domestic oil production through exploration and technological advancements that can reduce reliance on imports, and implement regulations on gold imports, exports, and trading can help manage price fluctuations and subsequently impact stock market performance.

Forward and Reverse Gematria are Very Different Beasts

Abstract Though a relatively quiet minority, some commentators since the eighth-century Bede have suggested that the notorious “666” of Revelation 13:18 alludes to the measurement of King Solomon’s excessive gold accumulation which immediately precedes his idolatrous downfall in the Biblical narrative. We now bolster that hypothesis with the observation that the letters of Solomon’s (New Testament Greek) name add up to 1,260, thereby linking this passage with the repeated references to “1,260 days” and related time durations in the preceding sections of the Apocalypse. We go on to adduce a diverse array of reasons to confirm Solomon as the intended solution over against competing candidates such as Nero Caesar and against other types of interpretations such as the purely symbolic. This approach to John’s “calculate the number” riddle – intertextuality feeding into forward gematria feeding into further intertextuality – has hermeneutical, exegetical, spiritual, canonical, geometrical, logical, and aesthetic advantages over popular reverse gematria attempts to search for a (generally extra-Biblical) name producing the value 666.

The Co-volatility of Uncertainty Shock on Chinese Exchange Rate: Based on Time-frequency Domain and Time-varying Method

In recent years, with the global epidemic, the Russia-Ukraine conflict, the great power game, and so on events, the international political and economic environment has become more and more unstable, the USDCNY exchange rate fluctuates significantly, and the external uncertainty risk shocks negatively hit the foreign exchange market. Based on this, this article expands global uncertainty risk shocks into four dimensions: global geopolitical risk, U.S. economic policy uncertainty, Chinese economic policy uncertainty, and VIX index, in addition to adding international WTI crude oil and gold price as additional variables, which are significantly correlated with both, to study the linkage among uncertainty shocks, commodity volatility and USDCNY exchange rate from the time-frequency domain. Furthermore, we analyze the time-varying spillover effects among variables at different frequency scales by decomposing the data into multiple scales using the Maximum Overlap Discrete Wavelet Transform (MODWT). Findings reveal that uncertainty factors and commodity prices exert distinct influence mechanisms on the USDCNY exchange rate under different cycles of frequency fluctuations. At various time-frequency domain volatility scales, VIX and WTI generate significant risk spillovers over the USDCNY exchange rate. Moreover, when global major events occur, time-varying spillover effects of variables are also heterogeneous. The research findings presented in this paper can provide valuable insights for policymakers and investors regarding the impact of international uncertainty shocks on the Chinese exchange rate across various short, medium, and long frequencies. This understanding can aid in managing uncertainty risks in decision-making processes, ultimately facilitating the efficient functioning of the foreign exchange market.

ANALISIS PERBANDINGAN METODE ARIMA DAN LEAST SQUARE UNTUK PREDIKSI HARGA EMAS : PENDEKATAN PROBABILISTIK DAN STATISTIK

Gold, a precious metal renowned for its value in various sectors, including investment and jewelry, is often considered a secure asset within investment portfolios. However, its prices exhibit high volatility influenced by economic, geopolitical, and global financial factors. Previous research has focused on predictive methods to anticipate gold price movements. In recent years, heightened complexity and uncertainty, exacerbated by global factors such as economic shifts and the Covid-19 pandemic, emphasizes the urgency of accurate gold price predictions. This study comprehensively analyzes and compares the performance of Autoregressive Integrated Moving Average (ARIMA) and Ordinary Least Squares (OLS) in forecasting gold prices, utilizing statistical and probabilistic approaches. ARIMA excels in handling time series data, identifying complex patterns, and forecasting price changes based on historical trends. Conversely, OLS, a probabilistic method, stands out in adjusting linear models to gold price data, providing detailed insights into influencing factors. The research employs a 5-year gold price dataset (2018-2023) and evaluates the models' performance using Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE). Results indicate OLS outperforms ARIMA, with lower MSE (45.79 vs. 284.83) and MAPE (0.0026 vs. 0.0066). This study contributes nuanced insights for market participants, investors, and researchers to comprehend commodity market behaviour, particularly in gold, emphasizing the importance of accurate prediction methods in strategic decision-making.

Impact of Oil and Gold Prices on Southeast Asian Stock Markets: Empirical Evidence from Quantile Regression Analysis

According to Chang and Li (2022), the COVID-19 pandemic may have had an impact on the European and American capital markets’ dependence on crude oil. However, no studies have assessed the returns and impacts of crude oil and gold prices on Southeast Asian stock markets in conjunction with the COVID-19 pandemic. To address this gap, a quantile regression model was used to analyze data of Southeast Asia stock prices from 2016 to 2023, alongside the daily closing prices of Dubai crude oil and world gold. The findings suggest that crude oil has a large trickle-down impact on the Southeast Asian market returns. This highlights the importance of dynamic linkages over time by reporting dynamic spillover to be statistically significant in Southeast Asian stock returns. Most stock returns show that volatility shocks are enduring. In Singapore and Thailand, the gold returns significantly and favorably affect the stock returns at all quantiles. At various quantiles, the impact of gold returns is notably favorable in the remaining scenarios.

BP neural network predicts the exchange rate of US dollar against gold

Gold and the US dollar are important currencies, and their price changes reflect changes in the world and North American economies. The gold market has become a financial investment market of equal importance to markets such as stocks, foreign exchange, and bonds. In such a situation, the price change of gold has become an important predictive research object. However, due to the changing world landscape, the randomness of gold price changes, and the existence of exchange rate and other influencing factors, it is very difficult to predict. It is crucial to establish a prediction model for exchange rate changes over time. This topic establishes a neural network model for exchange rate changes over time using the newly developed Back Propagation Neural Network in recent years. Finally, after several experiments, the parameters are adjusted to the optimal state to obtain the minimum error result. Through this study, we can determine that the prediction function of the BP artificial neural network can be applied in certain fields of life, but it also has significant limitations, to make it a complete tool for our daily life or work, it is necessary to further improve this technology.

Examining the Halal Perspective of Pre-Order Transactions in Gold Buying and Selling: A Qualitative Descriptive Analysis

Abstract This research will discuss buying and selling, especially buying and selling gold. Gold today is not only favored as a woman's jewelry but as a means of investment. The existence of gold buying and selling transactions by pre order raises questions that develop in the community regarding the halal and haram laws. This research is a type of qualitative descriptive research, by examining various literatures and criticizing the problems that occur in the field. By sharing opinions, from the community, religious leaders and scholars that buying and selling gold by pre order is halal. Because basically dp (downpayment) is only used as a guarantee for the buyer's certainty to buy the gold. Not as a contract. Because the correct contract is when it is paid in full and the gold has been received at the price according to the day the gold is received. Gold here is categorized as jewelry according to the illatnya "gold formed in various models is categorized as jewelry so that it is said to be goods or clothing". Keywords: buying and selling gold, buying and selling gold on pre-order, buying and selling gold on forging. Abstrak Penelitian ini membahas pembelian dan penjualan, khususnya pembelian dan penjualan emas. Saat ini, emas tidak hanya diminati sebagai perhiasan wanita tetapi juga sebagai sarana investasi. Keberadaan transaksi pembelian dan penjualan emas melalui pre order menimbulkan pertanyaan yang berkembang di masyarakat mengenai hukum halal dan haram. Penelitian ini merupakan jenis penelitian deskriptif kualitatif, dengan menelaah berbagai literatur dan mengkritisi masalah-masalah yang terjadi di lapangan. Dengan berbagi pendapat dari masyarakat, pemimpin agama, dan ulama bahwa pembelian dan penjualan emas melalui pre order adalah halal, karena pada dasarnya uang muka hanya digunakan sebagai jaminan kepastian pembeli untuk membeli emas, bukan sebagai kontrak. Kontrak yang benar adalah saat pembayaran lunas dan emas telah diterima dengan harga sesuai dengan hari emas diterima. Emas di sini dikategorikan sebagai perhiasan berdasarkan illatnya "emas yang dibentuk dalam berbagai model dikategorikan sebagai perhiasan sehingga dianggap sebagai barang atau pakaian". Kata kunci: pembelian dan penjualan emas, pembelian dan penjualan emas pre-order, pembelian dan penjualan emas secara palsu.

Predictive Machine Learning models to estimate the price of gold [Modelos predictivos de Machine Learning para estimar el precio del oro

The purpose of this study was to determine the optimal algorithm to estimate the price of gold and identify the variables most incident to its variation. An exploratory level methodology, quantitative approach and non-experimental design was used. The results obtained when performing EDA show that the variables with the highest correlation with respect to the price of gold are the cost of production with 44% and the S&P_500 with 30%. When validating the models, the result was that the Gradient boosting algorithm has an optimal R2 of 99.4%, this value justifies the importance of the model in order to estimate the price of gold. Likewise, without leaving aside the Random Forest algorithm, it also shows an R2 of 99.3%. Likewise, it was identified that the variables with the highest incidence are Cost_prod with 51.5% and USD_X with 30.4%. Finally, it is concluded that the use of these algorithms such as Gradient boosting and Random Forest can estimate the price of gold taking into account the variables that affect its variation.

Literasi Keuangan dan Efikasi Keuangan terhadap Minat Investasi Emas Antam Masyarakat Desa Labanjaya Kecamatana Pedes Kabupaten Karawang

The aim of this research is to explain Financial Literacy and Financial Efficacy of the Antam Gold Investment Interest of the Labanjaya Village Community. This research uses Descriptive Verification. The sample in this research was determined using the Lemeshow formula of 96 community respondents. Data was obtained using a Likert scale questionnaire distributed using Google Form. The data analysis method uses Multiple Linear Regression Analysis using SPSS version 26 tools. The results of this research show that the Financial Literacy Variable has a positive and significant effect on Investment Interest, and the Financial Efficacy Variable has a significant positive effect on Investment Interest, and Financial Literacy and Financial Efficacy have a positive effect and significant to Investment Interest. Keywords: Financial Efficacy, Financial Literacy, Investment Interest.

Gold price prediction by a CNN-Bi-LSTM model along with automatic parameter tuning.

Banking and stock markets consider gold to be an important component of their economic and financial status. There are various factors that influence the gold price trend and its fluctuations. Accurate and reliable prediction of the gold price is an essential part of financial and portfolio management. Moreover, it could provide insights about potential buy and sell points in order to prevent financial damages and reduce the risk of investment. In this paper, different architectures of deep neural network (DNN) have been proposed based on long short-term memory (LSTM) and convolutional-based neural networks (CNN) as a hybrid model, along with automatic parameter tuning to increase the accuracy, coefficient of determination, of the forecasting results. An illustrative dataset from the closing gold prices for 44 years, from 1978 to 2021, is provided to demonstrate the effectiveness and feasibility of this method. The grid search technique finds the optimal set of DNNs' parameters. Furthermore, to assess the efficiency of DNN models, three statistical indices of RMSE, RMAE, and coefficient of determination (R2), were calculated for the test set. Results indicate that the proposed hybrid model (CNN-Bi-LSTM) outperforms other models in total bias, capturing extreme values and obtaining promising results. In this model, CNN is used to extract features of input dataset. Furthermore, Bi-LSTM uses CNN's outputs to predict the daily closing gold price.

The Determining Factors Influences Student Gold Investing Interest

The purpose of this research is to determine the phenomena of student interest in gold investing following the COVID-19 pandemic. This study determines market targets, product development, and financial services. This quantitative research method collects data through a questionnaire. A total of 181 students were sampled using a random sampling technique. The study's findings indicate that financial literacy and investment motivation favor and significantly affect interest in gold investing. Meanwhile, income has a small but beneficial effect on interest in gold investment. If income affects students' interest in investing in gold at Brawijaya University, the income variable cannot be established realistically. According to observations, because the revenue obtained is sufficient for daily necessities, the amount of income is still supported by parents, and it is not easy to continuously save gold because funds are prioritized for other purposes or investments.

A short-term price prediction-based trading strategy.

Quantitative investment theory has emerged as a prominent and widely researched domain within the financial markets, where investors predominantly focus on discerning the intricate influences of market dynamics. In this paper, we proposed a short-term prediction-based trading strategy, which can equiponderate between return and risk, considerations while accounting for investor risk preferences. This strategy employs GM(1,1) to capture nuanced features of price dynamics in short-term intervals and update the GM(1,1) model with the latest data. Subsequently, a multi-objective planning equation is formulated to optimize asset allocations by determining the optimal holding that strikes between specific returns and risk mitigation. In the end, this work conducts a case study and sensitivity analysis using five years of gold and bitcoin price data spanning from 2016 to 2021. This empirical examination serves to affirm the efficacy and resilience of the proposed trading strategy. The case study reveals that proficient short-term price forecasting serves as a potent means to proactively mitigate risk, facilitating, judicious and objective trading practices. Moreover, it underscores the strategy's tangible utility as a guide for real-world investment decisions.

Investor Behavior in Gold, US Dollars and Cryptocurrency during Global Pandemics

COVID-19 and SARS are epidemics which have influenced the largest global economic crisis in recent years. This research reveals that both SARS and COVID-19 have led to fluctuations in the prices of gold and the US dollar index; however, there is no direct causal relationship be-tween COVID-19 and the price of bitcoin. The USD index saw a significant increase during the SARS outbreak, while gold prices surged during the COVID-19 pandemic. The notion that cryptocurrency will surpass the value of gold or traditional currencies seems improbable, given the lack of evidence linking bitcoin prices to COVID-19. Gold is expected to maintain its value in the long term, offering lower risk compared to other currencies.