Enhancing Gold Price Forecasting: A Study On Optimal Model Selection

Abstract

The study of gold price predictions has garnered significant attention due to the metal's historical and economic importance. Accurate forecasting of gold prices is crucial for investors, policymakers, and economists to make informed decisions. Previous research has utilized various time series techniques to predict gold prices, with mixed results in terms of accuracy and reliability. Despite extensive studies, there is still a lack of consensus on the most effective model for long-term gold price forecasting. By identifying the best fit model for gold price predictions, this research aims to contribute to more precise and reliable forecasting methods.

The research aims to identify the most effective model for long-term gold price forecasting, addressing the current lack of consensus in this area. It then reviews previous studies that have employed various time series techniques for gold price forecasting, noting their mixed results in terms of accuracy and reliability.

The findings of this research are expected to contribute to the development of more precise and reliable gold price forecasting methods. It is hypothesized that the ARIMA (0, 2, 1) model will provide the lowest RMSE and AIC values, making it the best fit for forecasting gold prices. By identifying the best fit model, the study aims to provide valuable insights for financial decision-making and economic planning. The results may have significant implications for investment strategies, policy formulation, and economic forecasting related to gold prices.