Journal of Intelligent Financial Management

Journal of Intelligent Financial Management

Forecasting Volatility of the Tehran Stock Exchange Total Index Using Artificial Neural Networks and GARCH Models: A Comparative Study

Document Type : Original Article

Author
Sajjad Bayazi
PhD student in Economics, Allameh Tabatabaei University, Tehran, Iran
10.25843/JIFM.2025.8563.21287
Abstract
Price volatility in capital markets is a fundamental element of investment decision-making and risk management. Given the nonlinear characteristics and conditional heteroskedasticity inherent in financial time series, this study compares the forecasting performance of GARCH-family econometric models and the Multilayer Perceptron (MLP) artificial neural network in estimating the volatility of the Tehran Stock Exchange (TSE) Total Index. Daily data for the TSE Total Index spanning from March 2014 (Farvardin 1393) to March 2024 (Esfand 1402) were utilized. Following the calculation of daily log returns, stationarity and the presence of ARCH effects were assessed, and GARCH(1,1), EGARCH(1,1), and GJR-GARCH(1,1) models were fitted. Subsequently, an MLP neural network was designed using inputs comprising lagged returns, trading volume, and conditional volatility derived from the EGARCH model; the network was trained using the Levenberg-Marquardt algorithm. Out-of-sample forecasting performance was evaluated using RMSE, MAE, MAPE, and QLIKE metrics, alongside the Diebold-Mariano test. Statistical tests confirmed the stationarity of returns and the presence of ARCH effects. The leverage effect coefficient was significant at the 1% level in both asymmetric models, and the EGARCH model demonstrated the best fit among the GARCH-family models based on AIC and BIC criteria. In the out-of-sample forecast (490 observations), the ranking of models from most accurate to least accurate was ANN-MLP+EGARCH, ANN-MLP, EGARCH, GJR-GARCH, and GARCH(1,1); the Diebold-Mariano test indicated that the superiority of both neural network models over EGARCH was statistically significant. Neural networks—particularly when combined with EGARCH conditional volatility—significantly improved volatility forecasting accuracy compared to standalone GARCH models; thus, hybrid AI-GARCH tools can be beneficial for setting stop-loss limits and managing risk in the Tehran Stock Exchange, although the interpretation of this superiority depends on the definition of the volatility proxy.
Keywords
Volatility forecasting
Tehran Stock Exchange
GARCH family models
Artificial neural network
Financial econometrics
Subjects
 
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Journal of Intelligent Financial Management
Volume 2, Issue 2 - Serial Number 6
Summer 2026
Pages 295-307