Spot Gold Price Research Articles

A novel approach to Predict WTI crude spot oil price: LSTM-based feature extraction with Xgboost Regressor

This paper presents a novel model based on LSTM to predict future prices of WTI crude oil. The WTI price forecasting utilizes data on spot gold price, US 10-year bond yield, global economic activity, and US dollar index from January 1986 to May 2023. The model's performance is assessed using measures such as MAE, MSE, RMSE, MAPE, and R2 metrics. The results generated by the proposed new model are compared to those of the existing machine and deep learning methods, and it is observed that the new model performs better than the existing models in all statistical tests. The study further examined the decision-making processes of the model using SHAP analysis and assessed the individual contribution of each feature to the model's predictions. The correlation between the US Dollar Index and Gold prices and WTI crude oil prices is evident. The SHAP research has demonstrated that the model effectively captures complicated economic linkages and enhances the accuracy of forecasts. The results of this study enhance the development of models that are capable of predicting results, even in times of significant instability, such as economic crises. Using sophisticated data analytics and AI methods would improve the efficiency of energy market oversight.

Hybrid DE-Optimized GPR and NARX/SVR Models for Forecasting Gold Spot Prices: A Case Study of the Global Commodities Market

In this work, we highlight three different techniques for automatically constructing the dataset for a time-series study: the direct multi-step, the recursive multi-step, and the direct–recursive hybrid scheme. The nonlinear autoregressive with exogenous variable support vector regression (NARX SVR) and the Gaussian process regression (GPR), combined with the differential evolution (DE) for parameter tuning, are the two novel hybrid methods used in this study. The hyper-parameter settings used in the GPR and SVR training processes as part of this optimization technique DE significantly affect how accurate the regression is. The accuracy in the prediction of DE/GPR and DE/SVR, with or without NARX, is examined in this article using data on spot gold prices from the New York Commodities Exchange (COMEX) that have been made publicly available. According to RMSE statistics, the numerical results obtained demonstrate that NARX DE/SVR achieved the best results.

Unveiling the Future: Forecasting the US Debt Ceiling and Financial Market Trends

The US debt limit has important implications for economic and financial stability. By analyzing historical data, this article finds that there is a significant correlation between the debt limit and the US financial market. The article also constructs an exponential smoothing mathematical model to successfully predict the debt limit in the future and test the accuracy of the model using historical data. The results show that the debt limit has a significant positive linear correlation with the S&P 500 Index and the Spot Gold Price Index. Although there is no linear correlation between the debt limit and the dollar index, this paper finds a hidden pattern by comparing the data of the Democratic and Republican administrations. Based on the results of the projections of the debt limit for the next five years and the correlation with the three major indices, the article makes a reasonable prediction of the future trend of the US financial market and provide insights. These results suggest that the situation in the United States may be more optimistic than expected.

Prediksi Spot Price Komoditas Emas Berjangka dengan Pendekatan Vector Error Correction Model

Time series data usually exhibit non-stationary behavior and involve interrelated variables. Thus, we need a model that can obtain good forecasting results from non-stationary time series data with multivariate variables. The Vector Error Correction Model (VECM) is a multivariate time series model which is a vector form of Vector Autoregressive Regression (VAR) for time series data that are non-stationary and have a cointegration relationship. This research was conducted to model the cointegration relationship in providing clarity on the long-term relationship of the influence of future prices and the Covid-19 pandemic on price movements of gold futures commodities and to predict spot price prediction modeling for gold futures commodities. The results of the research using the VECM (2) model, which is the best model, show that the future price of the gold commodity is quite dominant in influencing the value of the spot price of gold. The Covid-19 variable does not have a significant effect on the spot gold price variable.

Integration Learning of Neural Network Training with Swarm Intelligence and Meta-heuristic Algorithms for Spot Gold Price Forecast

ABSTRACT This research attempts to enhance the learning performance of radial basis function neural network (RBFNuNet) via swarm intelligence (SI) and meta-heuristic algorithms (MHAs). Further, the genetic algorithm (GA) and ant colony optimization (ACO) algorithms are applied for RBFNuNet to learn. The proposed integration of GA and ACO approaches-based (IGACO) algorithm combines the complementarity of exploitation and exploration capabilities to achieve optimization resolve. The feature of population diversification has higher opportunity to pursue the global optimal substitute being constrained to local optimal exceeding in five continuous test functions. The experimental results have illustrated that GA and ACO approaches can be incorporated intelligently and propose an integrated algorithm, which intents for obtaining the optimal accuracy training performance among relevant algorithms in this study. Additionally, method assessment results for five benchmark problems and a practical spot gold price forecast exercise show that the proposed IGACO algorithm outperforms other algorithms and the Box-Jenkins models in terms of forecasting preciseness and execution time.

Equity Portfolio Optimization With Gold

Design/methodology/approach: The investment portfolio optimization procedure in the Markovitz environment is made. The study is devoted to investments made by Polish investors, that is stocks are reflected by the WIG index from the Warsaw Stock Exchange. The analysis is conducted in different periods of time in 2006–2019 covering bull and bear markets of gold. Findings: It is shown that gold can be used as a hedge asset; however, exact conclusions depend on the market situation. Including gold in the stock portfolio increases the portfolio rate of return and at the same time decreases risk measured with variance, however only during the bull market of gold. During the bear market, gold helps minimize risk but at the same time it lowers the portfolio rate of return compared to stocks-only portfolio. Besides, it turns out that during the bull market of gold, both skewness and kurtosis of the portfolio are more advantageous for an optimal portfolio covering both stocks and gold than for the stock one. Research limitations/implications: An important assumption of the study is in what way investments in gold are made. Here, they are made by the use of spot gold prices and they are recalculated into Polish zlotys in order to check the Polish investor perspective. In reality, it is not easy to invest directly in big amounts of gold, although in the literature spot gold prices are used for such research. Originality/value: The research contributes to the existing international literature on using gold in stock portfolio management by analyzing the problem from the point of view of a Polish investor.

Interconnectedness between Commodity Futures and Spot Prices: A Comparative Analysis between Ordinary Least Square (OLS) and Quantile Regression (QR)

The study sought to contribute to the extant literature on the interconnectedness between commodity spot prices and futures prices by covering daily data from 2001-2019. Employing the OLS and the QR, different dynamics of the relationship between commodity spot and futures prices emerged from the study. For oil and gold prices, OLS estimator revealed that neither spot nor futures prices of the commodities had a significant effect on the other. Quantile regression estimators however suggested otherwise. For oil prices, futures prices were found to have a significant positive effect on spot oil prices at the 60th and 75th percentile whereas spot oil prices were found to have a significant positive effect on the futures oil prices at the lower tail (0.1, 0.2, and 0.25 quantiles). For gold prices, futures gold prices had a significant positive effect on spot gold prices at the 75th percentile (3rd quantile) marked as the upper tail of the distribution whereas a significant negative effect was revealed at the middle quantile (50th percentile). For cocoa prices, both the OLS and the QR estimators were significant in either direction. A significant positive effect of futures (spot) cocoa price on spot (futures) cocoa price was observed across all quantiles in both directions. The results suggest that speculators and arbitrageurs in the commodity market must be concerned about the causality moving from one direction to another and take appropriate investment positions that protect their interests.

Modelling the asymmetric linkages between spot gold prices and African stocks

We model the asymmetric linkages between returns of spot gold prices and African stock markets using wavelets and quantile regression techniques. The maximal overlap discrete wavelet transform technique was employed to decompose the returns into short-, medium-, and long-term series and the quantile regression was employed to explore the nexus by matching their conditional distributions along 0.05 quantile intervals. We find that the relationship between gold and African stocks is frequency-dependent and asymmetric in nature across the various timescales and quantiles. We find a mixture of negative and positive connections across the various quantiles in the short- and medium-terms. In the long-term, whereas the effect of gold is positive for Ghana, Mauritius, and Nigeria; it is negative for Egypt, Morocco, South Africa, and Tunisia. The results possess important implications for risk management as dependencies are not only studied over the entire conditional distribution at once but based on quantiles and further at different frequencies. Investors can make well-informed decisions to mitigate trade risks as they closely match the time heterogeneity in the markets.

Does Information Spillover and Leverage Effect Exist in World Gold Markets?

Financial markets are highly sensitive to the flow of information. The nature and arrival of information impact the price of gold. Gold, an international commodity, is traded across the world. Hence, the size and sign of information in any one country may be expected to affect the international gold price fixation. The objective of the study is to analyse the information spillover and the leverage effect transmitted from major gold consuming countries to the fixation of London Bullion Market Association (LBMA) AM fix and PM fix prices and how significantly the information of Indian market contributes to the price fixation. The multivariate exponential generalized autoregressive conditional heteroscedasticity (EGARCH) model proposed by Koutmos and Booth ( 1995 , Journal of International Money and Finance, 14(6), 747–762) is used to analyse the daily spot gold price data of major gold consuming countries like India, the USA, Europe and Japan. The results concluded that price and volatility of major gold consuming countries spill over to the LBMA AM fix and PM fix prices. LBMA AM fix and PM fix prices are sensitive to the new information and their price movements are significantly influenced by the information from India and the USA. The leverage effect shows that positive information causes more volatility in the LBMA fix prices than negative information. The study suggests that the investors, bankers, wholesalers and retail customers who depend on LBMA fix prices as a benchmark price can predict them by utilizing the information received from the Indian and US markets. They can specifically look for positive news than the negative news, as they have more impact on the spillover effect.

Non-Probabilistic Inverse Fuzzy Model in Time Series Forecasting

Many models and techniques have been proposed by researchers to improve forecasting accuracy using fuzzy time series. However, very few studies have tackled problems that involve inverse fuzzy function into fuzzy time series forecasting. In this paper, we modify inverse fuzzy function by considering new factor value in establishing the forecasting model without any probabilistic approaches. The proposed model was evaluated by comparing its performance with inverse and noninverse fuzzy time series models in forecasting the yearly enrollment data of several universities, such as Alabama University, Universiti Teknologi Malaysia (UTM), and QiongZhou University; the yearly car accidents in Belgium; and the monthly Turkish spot gold price. The results suggest that the proposed model has potential to improve the forecasting accuracy compared to the existing inverse and non-inverse fuzzy time series models. This paper contributes to providing the better future forecast values using the systematic rules.

Are gold markets weak form efficient? Evidence from China, India and Russia

The main purpose of this study is to determine the weak form efficiency of the emerging gold markets such as China, India and Russia with the special focus on testing random walks (RWS) and martingale difference sequence (MDS) hypotheses during different periods of time. This study uses biased free statistical techniques such as runs test, parametric variance ratio tests and recent modified non-parametric variance ratio tests based on ranks and signs by using daily spot gold prices from January 12, 1993 to October 28, 2016. Findings of the study suggest that Russian gold market is weak form efficient throughout the period whereas other two markets are found weak form efficient during second sub period only that is, January 2000 to December 2005.

Price Discovery Role and Causal Relationship Between Malaysian Gold Futures Prices and Spot Gold Prices

Price Discovery Role and Causal Relationship Between Malaysian Gold Futures Prices and Spot Gold Prices

On the efficiency of the global gold markets

This paper examines the weak-form efficiency of the global gold markets with specific focus on the random walks (RWS) and martingale difference sequence (MDS) hypotheses, and consequently, investigates the extent to which predictability or non-predictability of global daily spot gold price return series behaviour can be explained by volatilities in macroeconomic fundamentals. We apply traditional parametric variance-ratio tests and their recent non-parametric modifications based on ranks and signs to one of the largest datasets on world gold markets to-date, consisting of daily spot price series of 28 emerging and developed gold markets from January 1968 to August 2014. First, our results show that gold markets in Egypt, Indonesia, Mexico, Nepal, Pakistan, Russia, Saudi Arabia, UAE and Vietnam are not weak-form efficient neither from the perspective of the strict RWS nor in the relaxed MDS sense. By contrast, RWS and MDS hypotheses cannot be rejected for gold markets in Hong Kong, Japan, Switzerland, UK and US at the conventional rejection levels. Results for gold markets in Australia, Bahrain, Brazil, Canada, China, Germany, India, Malaysia, Singapore, South Africa, South Korea, Taiwan, Thailand and Turkey are, however, mixed. Second, our findings show that greater changes in economic fundamentals are associated with lower levels of rejecting the RWS and MDS hypotheses. Third, our evidence shows that the probability of rejecting the weak-form efficiency is higher in emerging gold markets than developed ones. Fourth, our results show that the RWS hypothesis is rejected more frequently than its MDS alternative, and thereby justifying our decision to conduct an explicit test of the RWS and MDS hypotheses. Our results are robust to estimating subsamples, overlapping rolling windows and endogeneity corrected models, as well as controlling for a number of country-specific institutional and trading factors. Our findings have crucial implications for global portfolio managers, investors, poly-makers and regulatory authorities.

An Empirical Study on Performance of Gold ETFs in India - Post Crash Period

India being gold-centric, investors and corporates moved from buying and saving concept of gold to Investment and returns concept, a paradigm shift that brought forth many investment options in gold too. With booming capital market mitigating risk and maximizing returns became greatest challenge for any investment option. Mutual funds couldn’t live up to this challenge, thus new option, exchange traded funds (ETF) evolved, as underlying asset was Index, instead of single asset. As ETFs fared well at bourses Gold ETFs were also launched by large firms and were listed on stock exchange of India. Against this backdrop, the current study aims to study the performance of gold ETFs for the last five years (post-crash period) and also tries to evaluate the relationship of gold ETFs to spot Gold prices and gold ETF's to Nifty by using regression and correlation techniques. Out of the five gold ETFs selected in the study, GOLDSHARE is found to be more correlated to the spot gold price followed by GOLDBEES. The study also finds that the relationship of gold ETF’s to Nifty is inverse, meaning that as Nifty decreases gold ETF’s perform better, which is a unique phenomenon observed mostly in India.

A new Prediction method of Gold price: EMD-PSO-SVM

The current gold market shows a high degree of nonlinearity and uncertainty. In order to predict the gold price, Empirical Mode Decomposition (EMD) was introduced into Support vector machine (SVM). Firstly, we used the EMD method to decompose the original gold price series into a finite number of independent intrinsic mode functions (IMFs), and then grouped the IMFs according to different frequencies. Secondly, SVM was used to predict each IMF in which particle swarm optimization (PSO) was applied to optimize the parameters of SVM. Finally, the sum of each IMF's forecasting result will be the final prediction. In order to validate the accuracy of the proposed combination model, the London spot gold price and the Shanghai Futures gold price series were employed. Empirical studies indicated that the EMD-PSO-SVM model outperformed the WT-PSO-SVM model, and was feasible and effective in gold price prediction. We can promote the EMD-PSO-SVM to other related financial areas.