Modeling Gold Volatility: Realized GARCH Approach


Department of Economics, Semnan University, Semnan, Iran



orecasting 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, GIR-GARCH, and RGARCH with two realized volatility estimators using gold intraday data. We compared models, for in-sample fitting; by the log-likelihood 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.


Andersen, T. G., Bollerslev, T., Diebold, F. X., & Labys, P. (2003). Modeling and Forecasting Realized Volatility. Econometrica, 7(2), 579-625.
Badescu, A., Elliott, R. J., & Ortega, J. P. (2015). Non-Gaussian GARCH Option Pricing Models and Their Diffusion Limits. European Journal of Operational Research247(3), 820-830.
Barndorff-Nielsen, O. E. (2004). Power and Biopower Variation with Stochastic Volatility and Jumps. Journal of Financial Econometrics, 2(1), 1-37.
Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307-327.
Engle, R. (2002). New Frontiers for Arch Models. Journal of Applied Econometrics, 17(5), 425-446.
Engle, R. F. (1982). Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of United Kingdom Inflation. Econometrica, 50(4), 987-1007.
Engle, R. F., & Gallo, G. M. (2006). A Multiple Indicators Model for Volatility Using Intra-Daily Data. Journal of Econometrics, 131(1-2), 3-27.
Glosten, L. R., Jagannathan, R., & Runkle, D. E. (1993). On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks. Journal of Finance, 48(5), 1779-1801.
Hansen, P. R., & Lunde, A. (2005). A Forecast Comparison of Volatility Models: Does Anything Beat a GARCH (1,1)? Journal of Applied Econometrics, 20(7), 873-889.
Hansen, P. R., Huang, Z., & Shek, H. H. (2012). Realized GARCH: A Joint Model for Returns and Realized Measures of Volatility. Journal of Applied Econometrics, 27(6), 877-906.
Huang, Z., Wang, T., & Hansen, P. R. (2017). Option Pricing with the Realized GARCH Model: An Analytical Approximation Approach. Journal of Futures Markets37(4), 328-358.
---------- (2017). Option Pricing with the Realized GARCH Model: An Analytical Approximation Approach. Journal of Futures Markets37(4), 328-358.
Jiang, W., Ruan, Q., Li, J., & Li, Y. (2018). Modeling Returns Volatility: Realized GARCH Incorporating Realized Risk Measure. Physica A: Statistical Mechanics and its Applications500, 249-258.
Nelson, D. B. (1991). Conditional Heteroskedasticity in Asset Returns: A Nee Approach. Econometrica, 59(2), 347-370.
Patton, A. J. (2011). Volatility Forecast Comparison Using Imperfect Volatility Proxies. Journal of Econometrics, 160(1), 246-256.
Ranković, V., Drenovak, M., Urosevic, B., & Jelic, R. (2016). Mean-univariate GARCH VaR Portfolio Optimization: Actual Portfolio Approach. Computers & Operations Research72, 83-92.
Sahamkhadam, M., Stephan, A., & Östermark, R. (2018). Portfolio Optimization Based on GARCH-EVT-Copula Forecasting Models. International Journal of Forecasting34(3), 497-506.
Sharma, P. (2016). Forecasting Stock Market Volatility Using Realized GARCH Model: International Evidence. The Quarterly Review of Economics and Finance59, 222-230.
Shephard, N., & Sheppard, K. (2010). Realizing the Future: Forecasting with High-Frequency-Based Volatility (Heavy) Models. Journal of Applied Econometrics, 25(2), 197-231.
Wei, Z. Y., Luo, Y. F., Yu, D. Y., & Wang, A. F. (2017). The Measure of Risk for SSE 50 Index Based on Realized NGARCH Model. Journal of Chongqing University of Technology (Natural Science)5, Retrieved from