Document Type : Research Article

Authors

1 Department of management, Faculty of Social Sciences and Economics, Alzahra university, Tehran, IRAN.

2 Department of Management, Faculty of Social Science & Economics, Alzahra University, Tehran, Iran.

Abstract

A large number of investors have been attracted to the Iran Mercantile Exchange as a result of launching Bahar Azadi Coin future contracts, also known as gold coin future contracts, since 2007. The nature of gold price as a physical-commodity and financial asset, as well as other contributing factors to the gold futures market, extremely complicates the analysis of the relationship between the underlying variables.
One of the methods to forecast the price volatility is the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model. However, the high percentage of errors in such prediction has forced researchers to apply a variety of techniques in the hope of more accurate projections. Similarly, in this study, a hybrid model of the GARCH and Artificial Neural Network model (ANN) was used to predict the volatility of gold coin spot and future prices in the Iran Mercantile Exchange.
In this study, variables such as global gold price, spot or future gold coin price (depending on which one is analyzed), US Dollar/IR Rial, world price of OPEC crude oil, and Tehran Stock Exchange Index were considered as factors affecting the price of gold coin. The results of the study indicate that the ANN-GARCH model provides a better prediction model compared to the Autoregressive models. Moreover, the ANN-GARCH model was utilized to compare the predictive power of spot and future gold coin prices, and it revealed that gold coin future price fluctuations predicted spot price of gold coin more accurately.

Keywords

[1] Bildirici, M., Ersin,Ö.Ö, 2009, Improving forecasts of GARCH family models with the arti cial neural networks: An application to the daily returns in Istanbul Stock Exchange, Expert Systems with Applications,36(4), 7355-7362, 
[2] Bollerslev, T., 1986, Generalized autoregressive conditional heteroscedasticity, Journal of econometrics,31(3), 307-327, 
[3] Engle, R.F., 1982, Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom in ation, Econometrica,50(4), 987-1007,
[4] Fang, L., Yu, H., Xiao, W., 2018, Forecasting gold futures market volatility using macroeconomic variables in the United States, Economic Modelling,72, 249-259,
[5] Goodarzi, M., Amiri, B., 2013, Presenting a Model for Identifying Affecting variables on the Future Price of Gold Coins by Arti cial Neural Network Method and Comparing It with Regression Models, Journal of Financial Engineering and
Portfolio Management,4(15), 17-33.
[6] Keshavarz Hadad, GH., Heirani, M., 2015, Estimation of Value at Risk in the Presence of Dependence Structure in Financial Returns: A Copula Based Approach, Journal of Economic Research,49(4), 869-902,
[7] Kirkulak-Uludag, B., Lkhamazhapov, Z., 2016, The volatility dynamics of spot and futures gold prices: Evidence from Russia, Research in International Business and Finance,38, 474-484,
[8] Kristjanpoller, W., Fadic, A., Minutolo, M., 2014, Volatility forecast using hybrid Neural Network models, Expert Systems with Applications,41(5), 24372442,
[9] Kristjanpoller, W., Minutolo, M., 2015, Gold price volatility: A forecasting approach using the Arti cial Neural NetworkGARCH model, Expert Systems with Applications,42(20), 72457251,
[10] Kroner, K. F., Kneafsey, K. P., Claessens, S., 1995, Forecasting volatility in commodity markets, Journal of Forecasting,14(2), 7795.
[11] Le, T., Chang, Y., 2012, Oil price shocks and gold returns, International Economics,131, 71-103, 
[12] Lin, F., Chen, Y., Yang, Sh., 2016, Does the value of US dollar matter with the price of oil and gold? A dynamic analysis from timefrequency space, International Review of Economics and Finance,43, 59-71, 
[13] Miyazaki, T., Hamori, SH., 2012, Testing for causality between the gold return and stock market performance: evidence for gold investment in case of emergency, Applied Financial Economics,23(1), 27-40, 
[14] Monfared, S. A., Enke, D., 2014, Volatility forecasting using a hybrid GJR-GARCH neural network model, Procedia Computer Science,36, 246253,
[15] Nicolau, M., Palomba, G., 2015, Dynamic relationships between spot and futures prices. The case of energy and gold commodities, Resources Policy,45, 130-143,
[16] Pradhana, R., Hall, J., Toit, E., 2020, The leadlag relationship between spot and futures prices: Empirical evidence from the Indian commodity market, Resources Policy, 27-40,
[17] Roh, T., 2007, Forecasting the volatility of stock price index, Expert Systems with Applications,33(4), 916922, 
[18] Saeidi, A., Alimohammadi, Sh., 2014, studying effective factors on gold coinfutures contracts price, Financial engineering and portfolio management,5(20),41-56.
[19] Saeidi, H., Mohammadi, Sh., 2011, Forecasting the volatility of Tehran Stock Exchange Index return by using hybrid model of ANN-GARCH model, Journal of Securities Exchange,4(16), 153-174.
[20] Shams, Sh., Naji Zavareh, M., 2016, Comparison Between The Hybrid Model of Genetic Fuzzy and Self - Organizing Systems and Linear Model to Predict The Price of Gold Coin Futures Contracts, Journal of Financial Research,17(2),
239-258.
[21] Truck, S., Liang, K., 2012, Modelling and forecasting volatility in the gold market, International Journal of Banking and Finance,9(1), 48-80,
[22] Tully, E., Lucey, B. M., 2007, A power GARCH examination of the gold market, Research in International Business and Finance,21(2), 316325,