Empirical Modeling for the Spot Price of Gold Based on Influencing Factors

Wang, Weihe; Xia, Weixuan

doi:10.11114/aef.v4i3.2295

Cited by 3 publications

(4 citation statements)

References 4 publications

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“…Te time-series methods forecast the future gold price based on the past time series, including ARIMA (auto-regressive integrated moving average), the ARCH (auto-regressive Conditional Heteroscedasticity) class models, and the GARCH (Generalized Auto-Regressive Conditional Heteroscedasticity) class models among others. For example, Wang and Xia [6] analyzed the marginal and joint impact of economic variables on the gold price, and provided a short-term forecast of the gold price by multiple regression, achieving a satisfactory result. Yang [7] used the ARIMA model, and the results showed that the model can refect the true gold price trend to a certain extent.…”

Section: Literature Reviewmentioning

confidence: 99%

International Gold Price Forecast Based on CEEMDAN and Support Vector Regression with Grey Wolf Algorithm

Qiu

Shi

et al. 2022

Complexity

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Considering the complexity pattern of the gold price, this paper adopts the decomposition-reconstruction-forecast-mergence scheme to perform the international gold price forecast. The original gold price data are decomposed into 12 intrinsic mode functions and a residual by the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method, and then the 13 sequences are reconstructed into a high-frequency subsequence (IMFH), a low-frequency subsequence (IMFL), and the residual (Res). According to the different characteristics of the subsequences, the IMFL and Res are forecasted by the support vector regression (SVR) model. Besides, in order to further improve the prediction accuracy of IMFH, we have developed a novel hybrid method based on the support vector regression (SVR) model and the grey wolf optimizer (GWO) algorithm with SVR for predicting the IMFH of gold prices, i.e., the CEEMDAN-GWO-SVR model. This hybrid model combines the methodology of complex systems with machine learning techniques, making it more appropriate for analyzing relationships such as high-frequency dependences and solving complex nonlinear problems. Finally, the final result is obtained by combining the forecasting results of the three subsequences. The empirical results show that the proposed model demonstrates the highest prediction ability among all of the investigated models in a comparison of prediction errors with other individual models.

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Section: Literature Reviewmentioning

confidence: 99%

International Gold Price Forecast Based on CEEMDAN and Support Vector Regression with Grey Wolf Algorithm

Qiu

Shi

et al. 2022

Complexity

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“…Gold prices are positively linked to inflation rates [1]. Thus, the major macroeconomic variables that affect the spot price of gold in the short and long terms are usually reviewed [2]. Consequently, the Federal Reserve System (FED), the West Texas Intermediate (WTI), the Inflation Expectations (INF), the Dow Jones Industrial Average (DJI), and the US Dollar Index (DXY) are all widely used to forecast the gold price [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

“…Thus, the major macroeconomic variables that affect the spot price of gold in the short and long terms are usually reviewed [2]. Consequently, the Federal Reserve System (FED), the West Texas Intermediate (WTI), the Inflation Expectations (INF), the Dow Jones Industrial Average (DJI), and the US Dollar Index (DXY) are all widely used to forecast the gold price [1][2][3]. The FED is the central bank of the United States central bank that ensures the smooth operation of the US economy and long-term public interest rates [4].…”

Section: Introductionmentioning

confidence: 99%

Gold-Price Forecasting Method Using Long Short-Term Memory and the Association Rule

Boongasame

Viriyaphol²,

Tassanavipas³

et al. 2022

JMM

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Since gold prices influence international economic and monetary systems, numerous studies have been conducted to forecast gold prices. Nonetheless, studies employing the linear relationship method usually fail to explain the change in the pattern of the gold price. This study introduces a new paradigm that incorporates association rules and long short-term memory (LSTM) as a nonlinear-based method. For simulation, the proposed method was analyzed with data from Yahoo Finance from January 2010 to December 2020. The association rule was used to choose features relevant to the gold spot (GS) in the US Dollar Index (DXY). The LSTM forecast the gold price with a range of hyperparameter settings. The simulation results showed that the proposed method—the LSTM with GS and DXY, or LSTM-GS-DXY—resulted in low mean absolute percentage error (MAPE) metrics. In addition, the proposed LSTM-GS-DXY system outperformed the simple moving average (SMA), weight moving average (WMA), exponential moving average (EMA), and auto-regressive integrated moving average (ARIMA).

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“…Based on the state space model, Que and Ma (2013) find that trade integration is the primary catalyst to facilitate the RMB internationalization. Some scholars (He & Chen, 2019;Wang & Xia, 2016) conclude that the rapid growth of China's economic strength and comprehensive national strength was the fundamental factor in the early development of RMB internationalization. China's economic strength is the major factor affecting the degree of RMB internationalization, followed by the degree of capital account openness.…”

Section: Introductionmentioning

confidence: 99%

Currency internationalization and openness: A paradigm from renminbi

Jia

2022

Rev International Economics

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This article intended to empirically test the interactive effects among renminbi (RMB) internationalization, trade and financial openness. Based on the monthly data from the first month of 2011 to the sixth month of 2018, we first build a vector error correction model includes RMB internationalization index, financial openness, trade openness, inflation rate, exchange rate and economic growth rate. Second, both linear and nonlinear Granger causality tests are conducted to analyze the causality of those variables. The results reveal that financial and trade openness have the greatest effects on the degree to which RMB is internationalized. In the short run, the trade openness will promote the development of RMB internationalization, while the financial openness will restrain such an effect. Long-term conclusions are the opposite. Meanwhile, financial openness can influence RMB internationalization nonlinearly while RMB internationalization cannot influence financial openness either linearly or nonlinearly. In addition, a strong two-way nonlinear Granger causality exists between RMB internationalization and exchange rate, and RMB internationalization and trade openness, respectively. Moreover, we find more nonlinear Granger relationships compared to linear Granger relationships, which implied that the relationships between RMB internationalization and other essential elements need to be 956

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Empirical Modeling for the Spot Price of Gold Based on Influencing Factors

Cited by 3 publications

References 4 publications

International Gold Price Forecast Based on CEEMDAN and Support Vector Regression with Grey Wolf Algorithm

International Gold Price Forecast Based on CEEMDAN and Support Vector Regression with Grey Wolf Algorithm

Gold-Price Forecasting Method Using Long Short-Term Memory and the Association Rule

Currency internationalization and openness: A paradigm from renminbi

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