Gold Price Research Articles

Do economic uncertainty and political risk steer CDS dynamics? An analysis of the Türkiye CDS

Purpose This study aims to identify critical determinants of sovereign credit risk by examining the influence of oil prices, gold prices, geopolitical risk, market volatility, exchange rates, inflation and non-performing loans on Türkiye’s credit default swap (CDS) spreads. This analysis provides a comprehensive understanding of how economic uncertainty and political risk impact Türkiye’s financial stability, as reflected in its CDS market. This study investigates the importance of ex ante proxies in explaining changes in CDS spread by financial and economic indicators in Türkiye. Design/methodology/approach This research explores the connections between critical financial and economic indicators and the credit risk of Türkiye between 2009 and 2022 by using advanced econometric techniques such as ARDL bound tests, fully modified ordinary least squares (FMOLS), dynamic ordinary least squares (DOLS), Johansen co-integration tests and VECM Granger causality analyses. Findings ARDL bound test results reveal significant negative impacts of BIST and non-performing loans on CDS, and positive associations with inflation, VIX and geopolitical risk on CDSs. The short-term results show that BIST, INFL, NPL, USD, VIX and GPRT have negative coefficients. Johansen co-integration, FMOLS and DOLS results reinforce the ARDL findings. Moreover, BIST is a significant Granger cause of CDS. Originality/value This study is significant, as it jointly considers economic and political risk factors, thereby integrating multiple econometric models to provide more robust, meaningful and comparable results. By examining these factors together, the analysis offers a more comprehensive understanding of risk dynamics, yielding insights relevant to Türkiye. Although the findings are specific to Türkiye, they have broader implications, enriching the understanding of emerging economies. Türkiye’s status as a key representative of emerging markets strengthens the study’s value, as the results can serve as a reference point for other countries with similar economic structures. The importance of this study is also underscored by its potential to inform risk management strategies, guide policy decisions and offer insights to investors and financial analysts. By elucidating the intricate relationships among a broad spectrum of macroeconomic variables, this research contributes to a more comprehensive risk assessment framework. It equips stakeholders with a more informed perspective on the factors influencing credit risk in Türkiye’s economic landscape.

The Impact of Macroeconomic Factors on Gold Prices

This study analyzes the influence of significant macroeconomic variables, like interest rates, inflation, GDP growth rates, and currency rates, on gold prices. Analysis of a 20-year dataset from 2003 to 2023, employing descriptive statistics and regression techniques, demonstrates substantial connections between these variables and changes in gold prices. The results show that inflation and exchange rates have a positive relationship with gold prices, while GDP growth rate has a modest inverse relationship. Interest rates exhibit a weak inverse relationship, although it is not statistically significant. The model explains approximately 93% of the variation in gold prices, underscoring the complex interplay between macroeconomic factors and gold price movements. The results are significant for investors aiming to mitigate inflation or economic instability, as well as for policymakers evaluating the effects of monetary policy on gold demand. This research contributes to the understanding of gold's role in the global financial system and highlights areas for future investigation, including the incorporation of additional factors and advanced modelling techniques. Keywords: Gold prices, Macroeconomic factors, GDP growth rate, Interest rates, Inflation, Exchange rates, Regression analysis.

The forecasting and prospecting models of polychronous and polygenic gold deposits: Peculiarities of their development and representation

Forecasting and prospecting models of mineral deposits are traditionally compiled in tabular form accompanied with a graphic representation. The table considers the factors (criteria) of ore control (structural, lithological, metamorphic, etc.) and prospecting guides (geochemical, panned heavy-mineral concentrate mineralogical, geophysical, etc.) as attributive elements. Such tables usually “summarize” all the ore control factors, regardless of the duration of gold accumulation and the stage-by-stage nature of ore formation. This often leads to “mixing” of elements of different ages (including mutually exclusive ones), which hampers understanding the regularities of gold mineralization and complicates justification of an effective forecasting and prospecting complex. Therefore, the compilation of forecasting and prospecting models of polychronous and polygenic gold ore deposits should take into account characteristic features of each stage of the ore formation and should identify ore control factors for each of them.

Symmetries or Asymmetries: How MSCI Index Advanced European Markets’ Exchange Rates Respond to Macro-Economic Fundamentals

The purpose of this study is to find symmetries and asymmetries in the exchange rate and macroeconomic fundamentals of advanced European markets, namely Denmark, the Euro Area, and United Kingdom, for the period of 2011 to 2022 via application of the NARDL technique. The findings reveal that interest rate affects DKK exchange rate asymmetrically in the long and short run, whereas money supply affects it in the short run. Foreign reserves are found to be helpful for all three currencies in stabilizing the exchange rate. A decline in gold price weakens GBP, DKK, and EUR in the long run. Previous studies suggest that the existence of asymmetrical relationships justifies the selection of NARDL for empirical analysis. This study makes a contribution to the existing literature, as it proves that forecasting via NARDL is also robust for analysis. The findings have significant policy implications for financial applications.

Exploring co-explosive dynamics: Bitcoin price, attractiveness, and sentiment variables

This study applies a co-explosive VAR model based on the methodologies of Nielsen (2010) and Engsted and Nielsen (2012). to estimate the determinants of Bitcoin price in the long term such as the gold price, S&P500, Google searches for Bitcoin, and the financial stress index. We explore the co-explosive dynamics between Bitcoin’s price and these factors. Using weekly data spanning from October 4, 2013, to September 24, 2021, the research uncovers a four-explosive-root relationship with Bitcoin’s price. Our primary emphasis is on the interaction between Bitcoin’s price and Google search interest. Enhanced Google search activity is correlated with an increase in Bitcoin’s price in the long term. The Bitcoin price and Google search volumes adjust to long-term deviations from their established relationship, classifying them as explosive variables. This behavior of Bitcoin’s price is consistent with existing literature that highlights its inherently explosive nature.

Assessing the Environmental Sustainability Corridor: Carbon Emissions in Relation to Gold Price, Economic Growth, Foreign Direct Investment, and Renewable Energy Consumption

The growing concerns about global warming and its perceived influence on economic sustainability require a reassessment of the environmental consequences of gold mining, with a special focus on BRICS countries: Brazil, Russia, India, China, and South Africa. This paper examines the environmental sustainability corridor, carbon emission, gold price, economic growth, foreign direct investment (FDI), and renewable energy use between 1989 and 2020. The long-run association among the variables is checked by us through the PMG technique. Our findings indicate that while the gold price, FDI, and renewable energy use decrease carbon emission, economic growth adds to the increase in carbon emissions in the long run. These findings bring out the dual challenge of promoting economic growth while managing environmental impact. The study underlines how policymakers need to provide regulatory frameworks which will encourage renewable energy and responsible foreign investment, as a means of trying to mitigate the environmental impacts of gold mining and achieve sustainable development. Our research adds to the continuing debate about how economic expansion can be balanced with environmental preservation for resource-rich countries.

Total reserves as a factor of the economic security of the state

The purpose of the article is to study the most important problems related to the dynamics of total reserves, key factors affecting their volume and structure, the content of state measures aimed at their accumulation and rational use in the context of ensuring the economic security of the state, justification of the most urgent measures, aimed at overcoming existing problems in this area. General scientific and special methods are used: description, comparison, analysis, grouping, synthesis, theoretical generalization, abstract-logical. The volume of total reserves of Ukraine as of April 1, 2024 guarantees financing for 5.8 months of future imports, which characterizes the value of this indicator as “optimal”. The NBU’s net currency sales (funds spent on maintaining the relative exchange rate stability of the hryvnia during the war) during January 2022 – March 2024 exceeded USD 59 billion, or about USD 2.2 billion per month on average, which is two-thirds revenues in favor of the government in the form of loans and grants received during this period. The main factors that led to the unprecedented spending of reserves to support the hryvnia exchange rate were a multiple increase in the deficit of foreign trade in goods and services, an increase in balances on foreign currency accounts of non-financial corporations (the unwillingness of enterprises to sell currency at the current exchange rate), a radical increase in the expenses of refugees abroad, which are accounted for in balance of payments as “travel” or “import of services”. The dynamics of the growth of funds in the foreign currency accounts of legal entities indicates the expediency of considering the possibility of returning to the practice of mandatory sale of part of the foreign currency earnings of exporting enterprises. The parameters of this regulation should become the subject of a professional discussion between the government and the National Bank. Analysis of the long-term dynamics of gold prices indicates the need for a gradual increase in the share of this banking metal in the structure of the country’s total reserves, taking into account the 8-fold increase in its value since 2000. The structure of the country’s total reserves in terms of currencies shows the absolute dominance of the US dollar in it (92 %), which in the medium term will require taking measures aimed at harmonizing the structure of reserves due to the increase in the weight of other currencies that are actively used in settlements with partner countries, primarily euros and Chinese yuan.

Assessing the Effectiveness of Machine Learning Techniques for Silver Price Prediction: A Comparative Study

Silver is considered an important asset in terms of economic indicators and a valuable investment asset in terms of the markets. Therefore, determining silver prices is critically important for both national economies and investors. However, the non-stationary and non-linear nature of silver prices makes predicting price movements challenging. The methods used for predicting silver prices must be suitable for capturing these volatile and complex behavioral characteristics. The silver market can be influenced by other commodities and investment assets. Factors affecting silver prices, such as gold prices, Brent crude oil prices, the US Dollar index, the VIX index, and the S&P 500 index, can play a significant role. In this context, these variables have been used as inputs for predicting silver prices in the study. Three different models have been developed to predict the prices one, two, and three days ahead. These models have been predicted using four different machine learning methods: linear regression, support vector regression (SMOReg), k-nearest neighbors (k-NN), and random forest (RF). The results show that the random forest and k-NN methods exhibit the highest performance. The random forest achieves the highest accuracy in the first two models, while k-NN excels in the third model. Linear regression and SMOReg methods are less successful compared to the others. Consequently, it can be concluded that random forest and k-NN methods can be preferred for long-term predictions, and that these results may provide valuable insights, especially for investors and decision-makers.

Guiding onshore exchange rates with ‘free-market’ insights: discovery of a new predictor

ABSTRACT Under the managed floating exchange rate regime, onshore exchange rates often do not reflect the ‘free-market’ level due to regulation and intervention. In this paper, we derive the implied ‘free-market’ exchange rates from gold prices and quantify the strength of market force guiding the onshore exchange rates as a new predictor. Using machine learning methods to make forecasts, we find that the inclusion of this new predictor improves the forecasting performance of one-day-ahead onshore exchange rate returns across all models.

Impact of oil and gold prices on Bitcoin price during Russia-Ukraine and Israel-Gaza wars

Political tensions between countries and geopolitical events can create significant uncertainty in financial markets, impacting the prices of traditional assets such as oil and gold, as well as the emerging asset, Bitcoin. This study investigates the effect of gold and oil prices, including their price shocks, on Bitcoin price during two specific conflicts: the Russia-Ukraine war and the Israel-Gaza conflict. To achieve this, we employed the Vector Auto-Regression (VAR) method and wavelet coherence analysis. The data used spans daily observations from January 2022 to April 2024.The findings indicate that gold prices have a positive impact on Bitcoin prices. Specifically, there is a predominantly short-term relationship between gold prices and Bitcoin prices throughout the entire period under review, with this influence gradually decreasing over the medium and long term. Conversely, the relationship between Brent oil prices and Bitcoin prices during the same period shows that crude oil prices generally have a negative and insignificant effect on Bitcoin prices in the short term.

Predicted Increase in Gold Price Every Year with Impact on Economic Factors

The purpose of this study is to develop a predictive model to predict the trend of the emas price over time and to investigate how this affects important economic variables. As a popular commodity that is regarded as a safe refuge for investments, changes in the price of emas can have a significant impact on a number of economic indicators, such as inflation, consumer spending, and investment decisions. Compiling historical data on emas prices, macroeconomic variables, and other related topics is a component of research methodology. Regression analysis and ARIMA modeling are two methods of deret waktu analysis that can be used to create an andal model of problems. The model's predictive accuracy is then determined by using appropriate statistical metrics. This study's findings provide new and important information about the factors influencing changes in the price of emas and how they affect the economy as a whole. The model that is being used can be used by investors, financial institutions, and policymakers to predict gold price movements and make informed decisions to mitigate risks and capitalize on opportunities. The implications of this research extend all the way to the emas market since it increases our understanding of the close relationship between commodity prices and economic dynamics. The knowledge gained can help create more comprehensive investment and economic policies, which will eventually affect the stability of the economy and economic growth.

Comparative Analysis of Mutual Funds and Gold ETFs: A Study on Investment Performance

This project analysis the returns of specific large-cap equity mutual funds and Gold Exchange Traded Funds (ETFs) to determine the best investment options for investors looking to balance growth and stability. To create a well-rounded portfolio, the study assesses the performance metrics and risk factors of these investment options. Large-cap equity funds are examined for their ability to offer growth in investment value, while gold ETFs are evaluated for their function as protection against market fluctuations and rising prices. A Decision matrix framework is utilized to methodically assess every fund and ETF based on factors including past performance and level of risk. This method based on data allows for a comprehensive analysis, pointing out the advantages and drawbacks of equity and gold investments. The research provides information on strategic asset allocation, highlighting diversification to maximize profits and handle risk efficiently. The results are meant to assist investors in making knowledgeable, eco-friendly investment choices in a changing financial environment.

Underlying factors for gold price prediction based on ARIMA models

Abstract. The purpose of this study is to explore the application of ARIMA model and deep learning technology in time series diagram prediction and the comparison of their effects. First, this paper analyzes the time series data in detail and models them using the ARIMA model. The ARIMA model effectively captures the seasonal and trending characteristics of the data through autoregressive, moving average, and differential steps. At the same time, the method of introducing deep learning models based on recurrent neural networks (RNNs), especially long short-term memory networks (LSTMs), to deal with the nonlinear characteristics and long-term dependency of time series data. By comparing and analyzing the performance of the ARIMA model and the deep learning model in terms of prediction accuracy, computational efficiency and model generalization ability, finding that the deep learning model has higher prediction accuracy when processing complex time series data. In addition, this study also uses time series diagrams to visually display the prediction results, which further verifies the advantages of deep learning models in capturing dynamic changes in data. This study provides a new perspective and method for time series data analysis and prediction, and has important practical significance for finance, meteorology, energy and other fields.

Gold’s Value as an Investment

Abstract This paper presents an approach for pricing gold from investors’ perspective. The model is based on no-arbitrage principles with minimal structural assumptions. There is no need to specify investor preferences. When fitted to match 10-year real U.S. Treasury rates, the model can replicate the salient fluctuations in the time series of gold prices since 2007. The model is also able to capture key patterns of CME Comex gold futures prices. The model implies that the majority of the value of gold is due to its role as an investment asset. (JEL G12, G13)

DATA MODELING OF GOLD PRICES USING SUPPORT VECTOR REGRESSION WITH LINEAR, POLYNOMIAL, AND RADIAL BASIS FUNCTION KERNELS

Investment is a commitment of funds or other resources made with the goal of obtaining profits in the future. Gold is one of the commodities favored by investors because of its relatively stable value. Although relatively stable, gold prices experience fluctuations, which introduce risks associated with gold investments. The pattern of gold prices can be mathematically modeled using Support Vector Regression (SVR). SVR finds a function as a hyperplane (separating line) in the form of a regression function that fits all input data with an error and minimizes it as much as possible. SVR enables a balance between data fitting and overfitting through the use of kernel functions. The pattern of gold prices and SVR is the main focus of this research, aiming to model gold prices effectively. Furthermore, SVR seeks to obtain a hyperplane (regression function) that fits all input data by minimizing errors. The gold price modeling results using SVR indicated that the best model was obtained with a linear kernel, showing an error of 0.73% with parameters C = 10^(-4) and ? = 10^(-4). This implies the model's strong ability to follow data patterns accurately, resulting in highly reliable forecasting.

Interconnectedness of financial markets in crises in the case of the enlarged BRICS

The article presents a study of the connectivity and integration of sovereign bond and stock markets in 10 BRICS+ countries in the context of crisis instabilities in 2019−2024. Financial markets are becoming more integrated, and an increasing share of public investments are carried out across borders, which increases not only the opportunities for participants, but also the risks of a new crisis. The work used data on central bank rates of the considered countries, yield indices of 10-year government bonds, gold and Brent oil prices. The methods include the analysis of exchange rate dynamics, connectivity estimates based on the multivariate concordance coefficient and two-factor Friedman rank variance analysis, VAR models, Granger predictability and cointegration. The objective of this study is to analyze the interrelationship and cointegration between the sovereign bond and equity markets of selected BRICS+ countries during crisis periods. Our findings indicate that market interrelationship intensifies during crises, which in turn amplifies volatility. Additionally, we observed that none of the economies within the BRICS+ group can be classified as fully integrated or entirely isolated markets. The disruption of the interrelationship in the sovereign bond markets of the group is primarily reflected in the inconsistency of dynamic changes between Russia, China, and India. During the global shock of 2019–2020, the crisis spread from China, followed by Indonesia, and later to the other countries of the group. The financial and debt markets of the sampled countries were able to quickly cope with the severe shocks of the COVID-2019 period. The 2022–2024 crisis, which lasted significantly longer, began in Russia before spreading to countries across Asia and Africa. By 2024, Russia’s sovereign bond yields showed a marked decline. The increased market volatility following 2022 disrupted the integration and interrelationship of the stock and debt markets within the BRICS+ countries.

Analisis Harga Emas, Ukuran Perusahaan dan Profitabilitas Terhadap Pembiayaan Gadai Emas: Studi Empiris pada PT Bank Syariah Indonesia Tbk KCP Sukabumi Cicurug Setia Budi Periode 2021-2023

The purpose of this study was to determine how much influence the Gold Price, Company Size, and Profitability have on Gold Pawn Financing at PT Bank Syariah Indonesia Tbk, KCP Sukabumi Cicurug Setia Budi for the period 2021-2023. The objects of research are Gold Price (X1), Company Size (X2) Profitability (X3) and Gold Pawn Financing (Y). The research uses descriptive and associative methods with a quantitative approach. The population in the study was the financial statements of PT Bank Syariah Indonesia Tbk, KCP Sukabumi Cicurug Setia Budi. Data analysis techniques used are classical assumption test, multiple linear regression analysis, multiple correlation coefficient, coefficient of determination, partial test (t test) and simultaneous test (f test). The results showed a relationship between Gold Price, Company Size, Profitability on Gold Pawn Financing of 0.766 the level of relationship including strong relationship criteria. Then the results of the partial test show that the price of gold has a positive and significant effect on gold pawn financing, company size has a negative and significant effect on gold pawn financing, profitability has a negative and significant effect on gold pawn financing.

Research on the Correlation between the Movement of the Dollar and the Price of Gold

Abstract. Gold acts as a hedge to protect investors' assets. The U.S. dollar is a global currency and has an important place in international trade. Oil, on the other hand, is a non-renewable resource and an important international resource with unstable prices. This study used price data for the U.S. dollar, gold and oil from 2000 to 2023 to analyze the movement of gold, U.S. dollar and oil prices. The experiment uses a regression model to determine the effect of the dollar on the price of oil and gold. The study found a significant negative correlation between the U.S. dollar and the price of gold and an insignificant negative correlation with oil between 2000 and 2014. Between 2015 and 2023, there is a change in U.S. monetary policy, which leads to a weakening of the negative price correlation between gold and the U.S. dollar, but U.S. dollar still with the negative correlation with oil remaining weak. These results suggest that buying gold is the best way to protect your assets in times of financial crisis or market instability, but the US dollar and oil can change in price due to macroeconomic and political factors. To some extent these data provide a reference value for investment in the coming year.

Evaluating the advancement of sustainable development objectives in recently industrialized nations by tying gold prices, fossil fuel prices, and energy use

Rapid industrialization in recently industrialized countries (NICs) has exacerbated environmental degradation, raising urgent concerns about how energy consumption, gold prices, and fossil fuel prices contribute to this growing issue. This study aims to evaluate the asymmetric relationships between these factors and environmental degradation across 10 NICs, including Brazil, India, South Korea, Mexico, Indonesia, Turkey, Thailand, South Africa, Malaysia, and the Philippines, from 1995 to 2023. Using the CS-ARDL model, our study demonstrates that while energy costs and renewable energy consumption reduce environmental degradation, gold prices and fossil fuel consumption significantly increase ecological harm. Additionally, the study employs FGLS, CS-DL, AMG, and Driscoll-Kray to verify the robustness of these findings. Notably, the Environmental Kuznets Curve (EKC) hypothesis holds true in these economies. Key results show that a 1 % increase in renewable energy use decreases emissions by 0.7 %, while a 1 % rise in gold prices correlates with a 0.5 % increase in pollution levels. These findings underscore the need for NICs to implement green investments, promote renewable energy, and initiate structural reforms to ensure sustainable development.

Perceived Risk And Self Efficacy Effect On Digital Gold Applications Usage

Gold prices in the last quarter of 2022 to March 2023 have increased significantly. This was caused by several reasons, such as war and two large banks' bankruptcies, namely Silicon Valley Bank and Signature Bank; many investors have switched to gold investment. Indonesians are used to gold investment; various Fintech applications have provided digital gold investment, and the government also has regulations for digital gold investments, but Indonesians still hesitate to use digital gold for investment. This study aims to analyse the effect of some factors on digital gold applications using the Technology Acceptance Model (TAM) and adding external variables such as perceived risk and self-efficacy. The results showed that all TAM variables had a positive effect, and self-efficacy and perceived risk variables also had a positive effect. This means digital gold application users in Indonesia are still affected by perceived ease of use, perceived usefulness, perceived risk of applications and self-efficacy in using digital gold applications.