Prediction of Gold Price Based on US Dollar Index Using STSR Approach

Maylita Hasyim; Any Tsalasatul Fitriyah; Vita Fibriyani

doi:10.31764/jtam.v10i4.40387

Prediction of Gold Price Based on US Dollar Index Using STSR Approach

Maylita Hasyim, Any Tsalasatul Fitriyah, Vita Fibriyani

Abstract

Gold is widely recognized as a safe-haven asset, and its price dynamics play a crucial role in global financial markets. However, gold prices exhibit highly volatile and nonlinear behavior, making accurate prediction a challenging task. In addition to the influence of historical gold prices through autoregressive effects, movements in the US Dollar Index (DXY) are known to affect gold price fluctuations through potentially nonlinear relationships. Conventional linear time series models may not adequately capture both the temporal dependence of gold prices and the nonlinear influence of external economic indicators. Therefore, the Semiparametric Time Series Regression (STSR) approach is employed because it combines a parametric autoregressive component with a nonparametric spline component, enabling the model to simultaneously represent linear temporal dependence and nonlinear relationships. This study aims to model and predict gold prices using the STSR approach and evaluate its forecasting performance. The data used in this study consist of daily observations of the GCJ6 gold futures contract and the DXY obtained from Investing.com, covering the period from June 2, 2025 to February 3, 2026, with a total of 140 observations. In the proposed model, the GCJ6 price at the previous period (GCJ6t-1) is incorporated as the predictor in the parametric component, while the DXY serves as the predictor in the nonparametric component. The nonparametric function is estimated using a spline estimator with optimal knot selection based on the Generalized Cross Validation (GCV) criterion. Forecasting performance is evaluated using the Mean Absolute Percentage Error (MAPE), Symmetric Mean Absolute Percentage Error (sMAPE), and Mean Absolute Scaled Error (MASE). The proposed STSR model achieved an overall MAPE of 1.478%, an sMAPE of 0.335, and a MASE of 0.618. The overall MAPE below 2% indicates excellent forecasting accuracy, while the low sMAPE and MASE values further confirm the robustness of the proposed model. These findings indicate that the proposed STSR model effectively captures both temporal dependence and nonlinear effects, providing accurate and reliable predictions of gold prices.

Keywords

Gold Price; US Dollar Index; Semiparametric Regression; Time Series.

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References

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DOI: https://doi.org/10.31764/jtam.v10i4.40387