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Prediction of Gold and Silver Prices in an Emerging Economy: Comparative Analysis of Linear, Nonlinear, Hybrid, and Ensemble Models

Saurabh Kumar


This study compares the accuracy of different forecasting techniques for gold and silver returns in a leading emerging economy. The study employs four forecasting models: autoregressive integrated moving average (ARIMA), artificial neural network (ANN), hybrid, and ensemble models. The study takes data of more than 7 years and forecasting is carried out for different forecast horizons varying from 1- to 20-steps ahead. The results reveal that ARIMA model is the best model to predict the gold returns, whereas, the ANN model along with the ensemble model are the best to predict the silver returns. The results also indicate that there exists nonlinear patterns in the time-series data of gold and silver returns. The study has significant implications for investors, academia, and policymakers.


ARIMA; Artificial Neural Network; Hybrid Models; Ensemble; Forecasting; Gold; Silver; Market Efficiency

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