This study aims to develop and implement a hybrid quantitative DEA–GARCH model to simultaneously analyze corporate efficiency and stock return volatility in the Tehran Stock Exchange. The research is applied in nature and adopts a descriptive–analytical approach based on real capital market data. In the first stage, the relative efficiency of firms was estimated using Data Envelopment Analysis (DEA), employing a dynamic window DEA framework over consecutive time intervals to capture temporal variations in performance. The selected input variables included operating expenses, fixed assets, and the level of investment, while the output variables comprised operating profit and market value. The selection of these variables was grounded in the theoretical literature on firm efficiency and prior empirical studies. In the second stage, stock return volatility was modeled and forecast using the GARCH family of models. The efficiency scores derived from the DEA model were incorporated into the conditional variance equation as an exogenous explanatory variable in order to examine the effect of firm efficiency on the magnitude and persistence of return volatility. The statistical population consisted of all firms listed on the Tehran Stock Exchange. The final sample was determined through purposive screening based on criteria such as the availability of complete financial and price data, absence of prolonged trading suspensions, and the reliability of published financial statements. The findings indicate that more efficient firms exhibit more stable and lower-risk returns, whereas less efficient firms are more prone to pronounced volatility. The hybrid model demonstrated improved performance relative to the standard GARCH specification, with RMSE and MAPE reported at 0.009 and 6.7%, respectively. The results further reveal that industries with higher average efficiency experience lower volatility, and a negative correlation (ρ=−0.63) was observed between mean efficiency and return volatility. The DEA–GARCH framework provides robust predictive capability during turbulent periods and offers practical implications for investors and policymakers.