Gold Price Data Research Articles

COMPARATIVE ANALYSIS OF TIME SERIES FORECASTING MODELS USING ARIMA AND NEURAL NETWORK AUTOREGRESSION METHODS

Gold price fluctuations have a significant impact because gold is a haven asset. When financial markets are volatile, investors tend to turn to safer instruments such as gold, so gold price forecasting becomes important in economic uncertainty. The novelty of this research is the comparative analysis of time series forecasting models using ARIMA and the NNAR methods to predict gold price movements specifically applied to gold price data with non-stationary and non-linear characteristics. The aim is to identify the strengths and limitations of ARIMA and NNAR on such data. ARIMA can only be applied to time series data that are already stationary or have been converted to stationary form through differentiation. However, ARIMA may struggle to capture complex non-linear patterns in non-stationary data. Instead, NNAR can handle non-stationary data more effectively by modeling the complex non-linear relationships between input and output variables. In the NNAR model, the lag values of the time series are used as input variables for the neural network. The dataset used is the closing price of gold with 1449 periods from January 2, 2018, to October 5, 2023. The augmented Dickey-Fuller test dataset obtained a p-value = 0.6746, meaning the data is not stationary. The ARIMA(1, 1, 1) model was selected as the gold price forecasting model and outperformed other candidate ARIMA models based on parameter identification and model diagnosis tests. Model performance is evaluated based on the RMSE and MAE values. In this study, the ARIMA(1, 1, 1) model obtained RMSE = 16.20431 and MAE = 11.13958. The NNAR(1, 10) model produces RMSE = 16.10002 and MAE = 11.09360. Based on the RMSE and MAE values, the NNAR(1, 10) model produces better accuracy than the ARIMA(1, 1, 1) model.

Comparison of Fuzzy Grey Markov Model (1,1) and Fuzzy Grey Markov Model (2,1) in Forecasting Gold Prices in Indonesia

Currently, gold investment is considered promising despite the ever-changing price of gold. However, obtaining optimal profits is a challenge for investors. Therefore, a proper forecasting method is needed to forecast the gold price so investors can know the best transaction time. This study used two forecasting methods: the Fuzzy Grey Markov Model (1,1) and a new, never-before-used approach, the Fuzzy Grey Markov Model (2,1). The Fuzzy Grey Markov Model (2,1) approach is interesting because it can be considered for forecast data that shows varying increases and decreases, such as the gold price data used in this study. Both methods are combined models that utilize fuzzy logic to handle uncertainty in data; the Grey model forms a forecasting model, and the Markov chain determines the state transition probability matrix. Next, the error rates of the two methods are compared based on the Mean Absolute Percentage Error (MAPE) value to obtain the best forecasting method. As a result of this study, the Fuzzy Grey Markov Model (1,1) was chosen as the best forecasting method with a MAPE value of 0.28%.

Indonesia Gold Price Forecasting Using ARIMA Model (0,1,1) - GARCH (1,0)

A frequently employed time series model is the Autoregressive Integrated Moving Average (ARIMA) model. In highly volatile data, ARIMA models sometimes produce residual variances that are heteroscedasticity. One method that can overcome the problem of residual variance heteroscedasticity is the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) method. The purpose of this study is to obtain the ARIMA-GARCH model for daily gold price data in Indonesia for the period 1 January 2022 to 31 December 2022, and to obtain daily gold price forecasting results in Indonesia. The daily gold price forecasting model obtained for Indonesia is ARIMA (0,1,1) - GARCH (1,0) with a MAPE value of 0.5745% which shows that the model is very good because the MAPE value is less than 10%. The results of Indonesia's daily gold price forecast from January 1st, 2023 to January 3rd, 2023 remain stable.

An Enhanced Study on Gold Price Prognosis using Machine Learning

Machine learning has emerged as a prominent research area for predicting gold prices, utilizing historical data and algorithms. The field aims to uncover patterns, trends, and connections among various factors that influence gold prices, including economic indicators, geopolitical events, and supply and demand dynamics. By employing machine learning algorithms, predictive models can be constructed to provide valuable insights into potential patterns in gold price movements. This enables traders, investors, and other stakeholders to make informed decisions when it comes to gold investments. In our study, we delve into the realm of data science and machine learning techniques to forecast gold prices. We meticulously analyze historical gold price data, develop sophisticated forecasting models, and rigorously evaluate their performance. Through this process, we are able to identify meaningful patterns and correlations that significantly contribute to the prediction of future gold prices. One of the key aspects of our study is the assessment of the reliability and accuracy of various machine learning models specifically designed for gold price prediction. We examine different algorithms and approaches, comparing their effectiveness in capturing the underlying patterns in gold price movements. This evaluation provides us with important findings and insights, enabling us to determine the most suitable models for accurate gold price forecasting. However, it is crucial to acknowledge the limitations inherent in our study. The forecasting of gold prices is a complex task influenced by a multitude of factors, some of which may be unpredictable or subject to sudden changes. Therefore, our models may not capture all the nuances and intricacies of gold price dynamics. To address these limitations, we propose recommendations for future research, such as exploring novel data sources, incorporating additional variables, or improving the models' adaptability to changing market conditions. Machine learning plays a pivotal role in the field of gold price prediction. By leveraging historical data and employing sophisticated algorithms, we can uncover valuable insights and patterns that assist in forecasting future gold prices. Our study aims to contribute to this growing body of research by developing reliable models and providing important insights for traders, investors, and other stakeholders in the gold market

Artificial Intelligence-Driven Model for Gold Price Prediction

This study introduces an innovative approach to forecasting gold prices by employing Artificial Intelligence (AI)--driven models. Utilizing advanced machine learning techniques, including Logistic Regression, Random Forest, Decision Tree, and Support Vector Machine (SVM), the research evaluates the predictive capabilities of these models through comprehensive assessments based on key performance metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R2 Error. A particular focus is placed on ensemble learning, exemplified by the Random Forest model, which demonstrates superior accuracy in capturing intricate patterns within gold price data. These findings contribute valuable insights to the field of financial forecasting, emphasizing the potential of AI-driven models to inform stakeholders in gold investment and financial markets. The study concludes by advocating for ongoing research and continuous model refinement to adapt to dynamic market conditions and enhance the precision of gold price predictions. Keywords: gold price prediction artificial intelligence, MSE.

The Importance of Gold’s Effect on Investment and Predicting the World Gold Price Using the ARIMA and ARIMA-GARCH Model

This paper studies the importance of gold's effect on investment and the fact that gold is often seen as a safe-haven asset during economic uncertainty. When inflation rates rise, investors may turn to gold to preserve their wealth; the government will reserve gold to reduce the exchange rate risk. To provide a comprehensive analysis, the study incorporates forecasting the price of gold using both the Autoregressive Integrated Moving Average (ARIMA) and ARIMA-Generalized Autoregressive Conditional Heteroskedasticity (ARIMA-GARCH) models. The gold price data is daily from 1/01/2021 to 3/01/2024. We perform model comparisons that the ARIMA (2,1,3) and the ARIMA (2,1,3)-GARCH (1,1), which model gives lower mean absolute error (MAE) and root mean squared error (RMSE) values. The results show that the MAE and RMSE predictions of the ARIMA (2,1,3)-GARCH (1,1) model are 80.1371 and 96.8299, better than those of the other model. Therefore, the ARIMA (2,1,3)-GARCH (1,1) model forecast results are better precise. It gives a forecast value for gold prices in the world market at the end of 2024 of 1942.094 USD per troy ounce. Hence, the recommendation for investors and policymakers is that if the price is higher than 1942.094 USD per troy ounce in 2024, investors and policymakers should slow down to buy and wait for it to adjust first, or investors and policymakers with gold should gradually sell to make some profit. Moreover, good portfolio management will reduce the exchange rate risk by including an optimized amount of gold in currency portfolios. However, holding gold is risky; its prices may fluctuate due to factors beyond our control, such as war, uncertainty about world economic growth, and inflation. Therefore, investors and policymakers should consider the abovementioned factors and be careful when hedging in gold.

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.

Fluctuation and forecasting of gold prices in Saudi Arabia’s market

PurposeThe purpose of this study is to analyze the fluctuations in gold prices within the Saudi Arabian market and to develop a reliable forecasting model to aid market participants and policymakers in making informed decisions.Design/methodology/approachIn this study, we employ a rigorous time series analysis methodology, including the ARIMA (Auto Regressive Integrated Moving Average) model, to analyze historical gold price data in the Saudi Arabian market. The approach involves identifying optimal model parameters and assessing forecast accuracy to provide actionable insights for market participants.FindingsThe study showcases that the autoregressive properties of past gold prices play a pivotal role in capturing the inherent serial correlation within the market, enabling the ARIMA model to effectively forecast future gold price movements with accuracy.Research limitations/implicationsOur study primarily focuses on quantitative analysis, whereas few qualitative parameters are not included. Future studies may benefit from incorporating qualitative factors and expert opinions to enhance the robustness of gold price predictions and capture the full spectrum of market dynamics.Social implicationsParticipants and policymakers may find this study helpful in navigating the complicated Saudi Arabian gold market. By understanding financial stability and investment decisions more thoroughly, individuals and institutions may be able to manage their portfolios more effectively.Originality/valueBy combining historical insights with advanced ARIMA modeling techniques, this research provides valuable insight into gold price dynamics in the Saudi Arabian market.

Multivariate time series analysis of COVID-19 Pandemic and gold price by using Error Corrections Model

Coronavirus (COVID-19) severe acute respiratory syndrome is an infectious disease that has a direct influence on the world’s population, as is obvious from the prevalence of COVID-19. However, as the COVID-19 virus continues to proliferate, its impact is becoming more and more significant and extensive. Accordingly, the study’s goal is to use the Error Corrections Model (ECM) to examine the relationships between the COVID-19 pandemic and gold prices. An Error Corrections Model was used by the researchers in this investigation. Error Correction Models and long-term equilibrium can be found for non-stationary time series that have been cointegrated together. Two-step estimation can be used to estimate the ECM since a proportion of the imbalance from one period is adjusted in the succeeding period, the system returns to equilibrium. The calculated cointegrating relations are used to create the error correction terms, and the estimation of VAR is done by a first differencing process, and then appears in the model as an explanatory variable, with the term of error correction. The COVID-19 pandemic and gold price data were made available to the general public in a sample data. For the year 2020, it includes data on daily observations for the relevant variables. R-language results show that the COVID-19 pandemic and gold prices have been demonstrated to have a long-term causal relationship. During the most recent period of variance decomposition, 99.76 percent of the variance in covid-19 explains only 0.24 percent of the variance in gold price. This explains that there is no short-term causal relationship between covid-19 and gold price, as indicated by the Wald test results, the granger causality results have shown that the gold price is influenced not only by itself but also by the Covid-19 pandemic. Finally, there is a positive relationship between the Covid-19 pandemic and the price of gold, and this has a negative impact on human life during 2020.

Aplikasi Model ARIMA dalam Peramalan Data Harga Emas Dunia Tahun 2010-2022

Gold investment is one of the favorite investments during the Covid-19 pandemic because the price of gold is relatively volatile but shows an increasing trend. Savvy investors investing in gold need to be able to predict future opportunities. Therefore, price estimation is needed to develop a buying and selling strategy to maximize profits. The Autoregressive Integrated Moving Average (ARIMA) model is a suitable method for predicting time series data, so the best ARIMA model will be applied for forecasting world gold prices. The best ARIMA model is selected based on the Akaike Information Criterion (AIC) and Mean Absolute Percentage Error (MAPE) criteria. Monthly world gold price data for 146 periods are applied in this study and will be used to predict gold prices for the following six periods. ARIMA(0,1,1) is the best model obtained from the analysis results, with AIC and MAPE values ​​of 1264.731 and 11.972%, respectively. Forecasting results show that world gold prices will increase for the next periods.

New horizon in fuzzy distributions: statistical distributions in continuous domains generated by Choquet integral.

In this paper, some statistical properties of the Choquet integral are discussed. As an interesting application of Choquet integral and fuzzy measures, we introduce a new class of exponential-like distributions related to monotone set functions, called Choquet exponential distributions, by combining the properties of Choquet integral with the exponential distribution. We show some famous statistical distributions such as gamma, logistic, exponential, Rayleigh and other distributions are a special class of Choquet distributions. Then, we show that this new proposed Choquet exponential distribution is better on daily gold price data analysis. Also, a real dataset of the daily number of new infected people to coronavirus in the USA in the period of 2020/02/29 to 2020/10/19 is analyzed. The method presented in this article opens a new horizon for future research.

RETRACTED: An Evolutionary Technique for Building Neural Network Models for Predicting Metal Prices

In this research, a neural network (NN) model for metal price forecasting based on an evolutionary approach is proposed. Both the neural network model’s network parameters and network architecture are selected automatically. The time series metal price data set is used to construct a novel fitness function that takes into account both error minimizations and the reproduction of the auto-correlation function. Calculating the average entropy values allowed the selection of the input parameter count for the neural network model. Gold price forecasting was performed using the proposed methodology. The optimal hidden node number, learning rate, and momentum are 9, 0.026, and 0.76, respectively, according to the evolutionary-based NN model. The proposed strategy is shown to reduce estimation error while also reproducing the auto-correlation function of the time series data set by the validation results with gold price data. The performance of the proposed method is better than other current methods, according to a comparison study.

A Neural Learning Approach for a Data-Driven Nonlinear Error Correction Model.

A nonlinear error correction model (ECM) is developed to fit nonlinear relationships between the nonstationary time series in a cointegration relationship. Different from the previous parametric methods, this paper constructs a hybrid neural network to learn the nonlinear error correction model by combining a linear recurrent neural network with a multilayer BP network. The network learning algorithm is given by using the gradient descent method and error back propagation. Based on the principle of data-driven, all network parameters can be obtained through the network learning and training. The daily data of gold price and the US dollar index in 2021 were used to verify this proposed nonlinear ECM neural learning method and the results were compared by the likelihood ratio Chi-square test. Simulation results show that the proposed data-driven nonlinear error correction neural learning method can improve goodness of fit statistical significantly of complex nonlinear relationship between time series.

Testing Black Scholes and Garch Model Options on Gold Price Index With Long Strangle Strategy Using 1985-2020 Data

Objective - This research examines the implementation of options contracts on gold prices using GARCH and Black Scholes models accompanied by a long strangle strategy. Methodology – This research used 36 years of secondary data for daily gold prices from www.gold.org obtained from the year 1985 to 2020. The implementation of options contracts on gold prices using GARCH and Black Scholes models with long strangle strategy through three types of maturity dates (1-month, 2-months, and 3-months). These results were tested by comparing the average percentage value of the actual options premium price and the options calculated using the AMSE (Average Mean Square Error) methodology, where the smallest percentage value is a more precise reflection of the model. Findings – The research indicates that the results of the error percentage for 1-month, 2-months, and 3-months maturities increased linearly along with the size of the maturity period of the option contract also profit percentage of long strangle strategy for gold price option contract in average 17-27% or below 30%. Novelty – Based on the research result, the long strangle strategy is not the best strategy regarding the gold price option contract, and this study can contribute to current practices for the investor. Also, this research is unique because no research used gold price data for 36 years (daily basis) for the options contracts. Type of Paper - Empirical Keywords: Gold Price; Options Contract; Black Scholes; GARCH; Long Strangle; AMSE JEL Classification: G11, G13. Reference to this paper should be made as follows: Hendrawan, R; Maharsasi, D.E. (2022). Testing Black Scholes and Garch Model Options on Gold Price Index With Long Strangle Strategy Using 1985-2020 Data, J. Fin. Bank. Review, 7(3), 160 – 174. https://doi.org/10.35609/jfbr.2022.7.3(3)

IMPLEMENTASI METODE FUZZY TIME SERIES PADA PERAMALAN HARGA EMAS DI INDONESIA

Changes in gold prices in Indonesia continue to occur from time to time with uncertain changes and can be said to be volatile. With the condition of the volatile data on changes in gold prices, predictions are needed that are appropriate or close to the existing data. Chen's fuzzy time series method can be used as a solution to this problem because it has a low error rate. The purpose of this study was to predict the price of gold in Indonesia in the coming period by implementing the Chen fuzzy time series method, these steps can be solved using R software. The data used in this study were daily gold price data in Indonesia for the period January 1, 2022, to June 30, 2022. The results obtained are gold price forecasts for the period July 1, 2022, amounting to Rp. 988,313 and the MAPE value of 0.60515%. The MAPE value indicates that Chen's fuzzy time series method was very well used to forecast Indonesias gold price data.

Gold Price Prediction Based on Gold.org Data Using the Long Short Term Memory Method

Gold is one form of precious metal with priceless value in this day and age. Therefore, many people are starting to invest in gold. Someone who wants to invest in gold must pay attention to changes in gold's buying and selling price. One site that can be used as a reference to see changes in the price of buying and selling gold is gold.org. Several factors influence gold price changes, namely changes in the value of the US Dollar exchange rate, the amount of world gold production, and the increase in demand for gold itself. This means that the price of gold tends to be unstable because of frequent changes. The LSTM or Long Short Term Memory method can be implemented to predict gold prices based on previous gold prices. The prediction model built in this study predicts gold price in the future based on 60 last gold price data. Based on the results of accuracy measure, an accuracy of 87.84% was obtained with the value of the difference between the original price and the prediction of 5 and the number of epochs being 100.

PERAMALAN HARGA EMAS DUNIA DENGAN MODEL GLOSTEN-JAGANNATHAN-RUNCLE GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY

Gold investment is considered safer and has less risk than other types of investment. One of the important knowledge in investing in gold is predicting the price of gold in the future through modeling the price of gold in the past. The purpose of this study is to model the gold price in the past so that it can be used to predict gold prices in the future. The world gold price data is a time series data that has heteroscedasticity properties, so the time series model used to solve the heteroscedasticity problem is GARCH. This study has an asymmetric effect, so the asymmetric GARCH model is used, namely the Glosten-Jagannathan-Runkle GARCH (GJR-GARCH) model to model the world gold price data. The data is divided into in-sample data from January 3, 2012 to December 31, 2018 to create a world gold price model and out-sample data from January 1, 2019 to December 31, 2020, which is used to evaluate model performance based on MAPE values. The best model is the ARIMA(1,1,0) GJR-GARCH(1,1) model with a MAPE data out sample value of 18,93% which shows that the performance of the model has good forecasting abilities.

Forecasting gold price using a novel hybrid model with ICEEMDAN and LSTM-CNN-CBAM

Gold price has always played an important role in the world economy and finance. In order to predict the gold price more accurately, this paper proposes a novel decomposition-ensemble model. Firstly, the original gold prices are decomposed into sublayers with different frequencies by the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN). Secondly, the long short-term memory, convolutional neural networks, and convolutional block attention module (LSTM-CNN-CBAM) joint forecasting all sublayers. Finally, the prediction of the sublayers with different models is reconstructed as the final predicted results with the summation method. Among them, the proposed model could capture the essence of sequence effectively through ICEEMDAN algorithm, extract the long-term effect of the gold price by LSTM, mining the deep features of gold price data with CNN, and improving the feature extraction ability of the network through CBAM. It is proved by experiment that the cooperation among LSTM, CNN and CBAM can strengthen the modeling ability and improve the prediction accuracy. Moreover, the decomposition algorithm ICEEMDAN can further increase the forecast precision, and the prediction effect is better than other decomposition methods. Overall, the novel model ICEEMDAN-LSTM-CNN-CBAM (ILCC) could enhance the prediction accuracy and outperform other related comparative models.

GSTARIMA Model with Missing Value for Forecasting Gold Price

Gold is one of the investments that be a great demand. Selecting and applying the best GSTARIMA model for gold price forecasting was the aim of this study. However, the gold price data that has been obtained missing values. Missing value data has been imputed by the last data before the missing value and moving average techniques. The GSTAR (1) and GSTARI (1, 1) models have been combined with an imputation technique solved this problem. Based on the smallest RMSE value, the GSTARI (1, 1) model which has been combined with the imputation technique that used the last value was the best method because it produced the smallest RMSE when compared to other methods. Forecasting results shown that gold prices in the United States, United Kingdom, and Indonesia increased but gold prices in Turkey actually decreased. Forecasting gold prices in each of these countries become one of the references in investing in gold. Based on the results of gold price forecasting, gold prices changed but not significantly.

Testing the role of gold in crisis: a global perspective

During the period of extreme financial turmoil, investors will include hedge and safehaven assets in their portfolios. The present study looked at the dynamic link between gold return and the world stock return during crises. The study examined the conditional correlation between world stock and gold return. It investigated the usefulness of gold as a safe haven for stocks in the bear markets from January 2001 to June 2021 using daily MSCI stock index and gold price data. The crisis period is defined as the bear stock market in the present study, and bear markets were identified using the algorithm of Pagan and Sossounov (2003). Seven bear markets were identified for the purpose of the study. A combination of the DCC-GARCH model and OLS regression were employed to determine the relationship between gold and the MSCI world stock index. The study found that the dynamic conditional correlations are primarily negative in the bear markets under investigation. Gold performs the role of a safe haven for all bear markets except for the bear market from 2004-02-27 to 2004-06-30. It was concluded that gold acts as a robust, safe haven during turmoil and safe haven property depends on the severity of stock price changes. The findings have implications for policymakers and investors.