Developing A Conceptual Framework For Addressing The Accuracy Of Forecasting Models In Predicting Platinum Group Metals Prices.

Abstract

The volatility and complexity inherent in Platinum Group Metals (PGMSs) markets necessitate robust forecasting models for accurate price prediction, crucial for informed decision-making and risk management. This study proposes a comprehensive conceptual framework aimed at addressing the accuracy of forecasting models in predicting PGMSs prices, leveraging the Autoregressive Integrated Moving Average (ARIMA), Generalized Autoregressive Conditional Heteroskedasticity (GARCH), and Artificial Neural Network (ANN) methodologies.

The framework integrates key stages of model selection, evaluation, and refinement tailored to the unique characteristics of PGMSs price data. It begins with meticulous data collection, incorporating historical price series, macroeconomic indicators, supply-demand dynamics, and geopolitical factors influencing PGMSs markets. Model selection involves the exploration and comparison of ARIMA, GARCH, and ANN models, each offering distinct capabilities in capturing different aspects of price behavior, such as trend, seasonality, and volatility clustering.

Central to the framework is the rigorous evaluation of forecasting models using appropriate statistical metrics, including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and forecast error distributions, to assess accuracy and robustness across various market scenarios. Additionally, diagnostic tests and sensitivity analyses are employed to validate model assumptions and identify potential sources of forecasting uncertainty. Furthermore, the framework emphasizes the iterative nature of model refinement, enabling the incorporation of new information and adaptive adjustments to forecasting methodologies in response to changing market dynamics. Expert judgment and qualitative insights are integrated to complement quantitative analysis, enhancing the reliability and interpretability of forecast outcomes.

By offering a structured approach to forecasting model development and evaluation, this conceptual framework provides valuable guidance for stakeholders involved in PGMSs markets analysis, including traders, investors, and policymakers. Future research directions may explore the integration of hybrid models and advanced machine learning techniques to further enhance forecasting accuracy and resilience to market fluctuations.