This study examines the dynamic interplay between the US Dollar Index(USDI)and gold prices(GP)to assess the sustainability of gold price trends.Employing a rolling window bootstrapping causality test methodology acros...This study examines the dynamic interplay between the US Dollar Index(USDI)and gold prices(GP)to assess the sustainability of gold price trends.Employing a rolling window bootstrapping causality test methodology across full and sub-samples,the findings of this study challenge the conventional assumption of a stable long-term inverse correlation between USDI and GP,thereby validating the hypothesis that their relationship is nonlinear and time-dependent.During periods of heightened geopolitical and economic volatility,both the US dollar and gold function as safe-haven assets,with USDI fluctuations exerting a positive influence on GP.Conversely,under stable market conditions,the US dollar serves as the currency in which gold is denominated,resulting in a negative impact of USDI on GP.Notably,GP also demonstrates bidirectional causality,exhibiting both positive and negative effects on USDI.The analysis reveals that while a general inverse correlation persists between gold and the US dollar,this relationship transitions to positive during surges in global political and economic instability.In light of contemporary developments—including escalating geopolitical rivalries,tepid post-pandemic economic recovery,and elevated US interest rates driven by inflationary pressures—this study posit that the upward trajectory of gold prices retains a robust empirical foundation.展开更多
金融时间序列预测对经济决策和投资意义重大,但金融市场的复杂性给预测模型构建带来挑战,而黄金价格走势备受关注,准确预测至关重要。本文针对现有组合模型不足,提出创新的非线性ARIMA-LSTM组合模型用于黄金价格预测。实证分析发现,ARIM...金融时间序列预测对经济决策和投资意义重大,但金融市场的复杂性给预测模型构建带来挑战,而黄金价格走势备受关注,准确预测至关重要。本文针对现有组合模型不足,提出创新的非线性ARIMA-LSTM组合模型用于黄金价格预测。实证分析发现,ARIMA(3,1,5)模型、LSTM模型及GRU模型虽能捕捉时间序列特征但预测存在偏差,结果表明组合模型ARIMA-LSTM预测效果优于其他三种模型。通过MAE和RMSE评估,验证了ARIMA-LSTM模型在黄金价格预测中的优势,为金融决策提供新思路。Financial time series forecasting is of great significance to economic decision-making and investment, but the complexity of financial markets brings challenges to the construction of forecasting models, and the trend of gold price has attracted much attention, so accurate forecasting is crucial. This paper aims at the shortcomings of existing combination models, an innovative nonlinear ARIMA-LSTM combined model is proposed for gold price prediction. The empirical analysis shows that although ARIMA(3,1,5) model, LSTM model and GRU model can capture the features of time series, the prediction bias exists. The results show that the combined model ARIMA-LSTM has better prediction effect than the other three models. Through MAE and RMSE evaluation, the advantages of ARIMA-LSTM model in gold price prediction are verified, which provides new ideas for financial decision-making.展开更多
Using statistical data of the United States,the authors establish the model affecting gold price, consider that stock prices, inflation rate, exchange rate, interest rate are the main factors affecting gold price.
基金Project of National Social Science Fund of China(Project No.:23BGJ010)。
文摘This study examines the dynamic interplay between the US Dollar Index(USDI)and gold prices(GP)to assess the sustainability of gold price trends.Employing a rolling window bootstrapping causality test methodology across full and sub-samples,the findings of this study challenge the conventional assumption of a stable long-term inverse correlation between USDI and GP,thereby validating the hypothesis that their relationship is nonlinear and time-dependent.During periods of heightened geopolitical and economic volatility,both the US dollar and gold function as safe-haven assets,with USDI fluctuations exerting a positive influence on GP.Conversely,under stable market conditions,the US dollar serves as the currency in which gold is denominated,resulting in a negative impact of USDI on GP.Notably,GP also demonstrates bidirectional causality,exhibiting both positive and negative effects on USDI.The analysis reveals that while a general inverse correlation persists between gold and the US dollar,this relationship transitions to positive during surges in global political and economic instability.In light of contemporary developments—including escalating geopolitical rivalries,tepid post-pandemic economic recovery,and elevated US interest rates driven by inflationary pressures—this study posit that the upward trajectory of gold prices retains a robust empirical foundation.
文摘金融时间序列预测对经济决策和投资意义重大,但金融市场的复杂性给预测模型构建带来挑战,而黄金价格走势备受关注,准确预测至关重要。本文针对现有组合模型不足,提出创新的非线性ARIMA-LSTM组合模型用于黄金价格预测。实证分析发现,ARIMA(3,1,5)模型、LSTM模型及GRU模型虽能捕捉时间序列特征但预测存在偏差,结果表明组合模型ARIMA-LSTM预测效果优于其他三种模型。通过MAE和RMSE评估,验证了ARIMA-LSTM模型在黄金价格预测中的优势,为金融决策提供新思路。Financial time series forecasting is of great significance to economic decision-making and investment, but the complexity of financial markets brings challenges to the construction of forecasting models, and the trend of gold price has attracted much attention, so accurate forecasting is crucial. This paper aims at the shortcomings of existing combination models, an innovative nonlinear ARIMA-LSTM combined model is proposed for gold price prediction. The empirical analysis shows that although ARIMA(3,1,5) model, LSTM model and GRU model can capture the features of time series, the prediction bias exists. The results show that the combined model ARIMA-LSTM has better prediction effect than the other three models. Through MAE and RMSE evaluation, the advantages of ARIMA-LSTM model in gold price prediction are verified, which provides new ideas for financial decision-making.
文摘Using statistical data of the United States,the authors establish the model affecting gold price, consider that stock prices, inflation rate, exchange rate, interest rate are the main factors affecting gold price.
