Modelling and forecasting gold price return and its volatility

Abstract

In the financial industry, forecasting gold price return and its volatility are crucial. Even tiny improvements in prediction performance can add up to a significant sum of money. One strategy for anticipating gold price volatility is the GARCH family of models. The GARCH process provides a more realistic background for estimating financial instrument values and rates than other models. The aim of this research is to model the volatility and price of gold via GARCH(1,1), EGARCH(1,1) and TGARCH(1,1) models. The data was employed from 4th January 2016 until 29th October 2021 retrieved from Yahoo Finance website. The data was converted into return price to make it stationary. The performance of the estimated models was compared by using information criterion. The best model is the one that has the lowest values of information criterion. TGARCH(1,1) is outperformed other proposed models where it has an ability to capture the bad and good news that exist in the data series. The best model is used to forecast the volatility and return of gold price. The return is expected to be constant with high risk for the next 5 days from the point origin in this study.