Modeling Gold Volatility: Realized GARCH Approach

Forecasting the volatility of a financial asset has wide implications in finance. Conditional variance extracted from the GARCH framework could be a suitable proxy of financial asset volatility. Option pricing, portfolio optimization, and risk management are examples of implications of conditional variance forecasting. One of the most recent methods of volatility forecasting is Realized GARCH (RGARCH) that considers a simultaneous model for both realized volatility and conditional variance at the same time. In this article, we estimate conditional variance with GARCH, EGARCH, GIRGARCH, and RGARCH with two realized volatility estimators using gold intraday data. We compared models, for insample fitting; by the loglikelihood value and used MSE and QLIKE lose functions to evaluate predicting accuracy. The results show that the RGARCH method for GOLD outperforms the other methods in both ways. So, using the RGARCH model in practical situations, like pricing and risk management would tend to better results.