Designing a Model for Forecasting the Gold Price Returns (Emphasizing on Combined convolutional neural network Models and GARCH Family Models)

Finding the best way to optimize the portfolio is one of the concerns of activists in the investment management industry. In recent years, the introduction of economic and mathematical models in the prediction of Gold indice has helped many investors to optimize portfolios. Therefore, in this study, we introduce models of GARCH family composition and convoultional neural network to predict the daily yield of Gold index will be paid during the period of 1390-1398. In this study, the Gold index is examined using GARCH and EGARCH short-term memory models. Of the two variables, the price of crude oil and the dollar index as factors that their shocks and fluctuations have a major impact on Gold indices are used as control variables. In addition to using convolutional model, considering the better performance of combined models (compared to individual models ) In anticipation In this study, all models of the GARCH family (both short and long run) with the convoultional neural network were combined and using the combined models, the efficiency of the main stock index and the five selected indicators for the next 10 days were predicted step by step and its accuracy Based on the evaluation criteria.