Modeling and forecasting volatility of gold price using arima – ann hybrid model and gold market behavior during world crises

Abstract

Gold is one of the most valuable metals in the world with numerous applications, including jewelry and electronic devices. Countries often utilize gold as an economic health indicator, and financial institutes use gold as a hedge against loans. Additionally, gold is a popular investment asset for diversifying portfolios. Thus, gold price predictions are crucial to making proper future decisions. Understanding the reasons why the price of gold fluctuates is one of the challenging tasks. In earlier research, gold price forecasting had been done using statistical methods. But with the recent developments in machine learning methods, it is now possible to combine conventional statistical models with machine learning to produce a hybrid model that makes better predictions. In the first part of the work, a novel hybrid model was proposed, by using Autoregressive Moving Average (ARIMA), Long-short Term Memory (LSTM), and Prophet. It is also essential to develop models that can predict gold prices during a crisis because, during these times, models will deviate from their typical historical patterns. Hence, another attempt has been made to examine the influence of crude oil, and silver, on gold prices during the 2008 financial crisis and the COVID-19 period, and predict gold prices using regression Analysis, co-integration, Vector Error Correction Model (VECM). It was found that there are short-term causalities between gold and the previous month's crude oil and silver. Therefore, having a joint impact on the current gold price during the crisis periods. Using the models proposed in this paper, better gold price predictions can be made in the future, even during financial crises. Better forecasting leads to better risk management, investment decisions, hedging, economic analysis, and strategic trading giving the opportunity to earn profits.