1 null 13009-44613009-4461 1 https://doi.org/10.22105/tqfb.v3i1.79 Research Article Data envelopment analysis, Generalized Autoregressive Conditional Heteroskedasticity models, Stock returns, Temporal efficiency, Tehran stock exchange, Hybrid model Assessment of Dynamic Efficiency and Forecasting Stock Return Volatility in the Iranian Stock Exchange Based on a Hybrid DEA–GARCH ModelAssessment of Dynamic Efficiency and Forecasting Stock Return Volatility in the Iranian Stock Exchange Based on a Hybrid DEA–GARCH Model Sattari Khadem Nastaran Department of Financial Engineering, Islamic Azad University, Science and Research Branch, Tehran, Iran. Rostami Mohsen Department of Mathematics and Computer Science, Islamic Azad University, Science and Research Branch, Tehran, Iran. 01 2025 30 01 2025 3 1 © 2025 Rea Press 2025

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 Assessment of Dynamic Efficiency and Forecasting Stock Return Volatility in the Iranian Stock Exchange Based on a Hybrid DEA–GARCH Model

This study aims to develop and implement a hybrid Data Envelopment Analysis-Generalized Autoregressive Conditional Heteroskedasticity (DEA–GARCH) model to simultaneously analyze firm efficiency and stock return volatility in the Tehran Stock Exchange (TSE). The research is applied and quantitative, incorporating dynamic efficiency analysis using window-DEA and volatility modeling through Generalized Autoregressive Conditional Heteroskedasticity (GARCH) family models. Data Envelopment Analysis (DEA) efficiency scores were incorporated as explanatory variables in the GARCH model to examine the impact of firm efficiency on return volatility. The findings indicate that more efficient firms (DEA Score > 0.9) exhibit more stable and lower-risk returns, while less efficient firms (DEA Score < 0.7) are prone to higher volatility. Performance indicators of the hybrid model improved compared to the standard GARCH model, with Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) reported as 0.009 and 6.7%, respectively. The results further reveal that industries with higher average efficiency experience lower volatility, showing a negative correlation between average efficiency and return volatility (ρ = −0.63). The DEA–GARCH model provides the capability to forecast volatility during crisis periods and offers practical guidance for investors and regulatory authorities.

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