The modeling and forecasting of yield volatility in financial markets have become increasingly critical due to their pivotal role in key applications such as Value at Risk (VaR) assessment, optimal resource allocation in investment portfolios, effective investment management, and accurate pricing of derivatives. Among the key drivers of yield volatility, information asymmetry between market participants has emerged as a significant factor influencing market dynamics. Despite its importance, the empirical exploration of this phenomenon in emerging markets, particularly the Tehran Stock Exchange (TSE), remains scarce. This study examines the impact of information asymmetry on the volatility of the TSE's total index, employing the advanced Fractionally Integrated Generalized Autoregressive Conditional Heteroskedasticity (FIGARCH) framework. Intraday trading data spanning 2023 and 2024, collected at five-minute intervals, form the basis of this analysis. Information asymmetry was quantified via the transaction volume-weighted price impact metric. The Akaike Information Criterion (AIC) and Schwartz Bayesian Criterion (SBC) determined the optimal model specification, leading to the application of FIGARCH, with exogenous information asymmetry incorporated into the GARCH (1,1) baseline model. Empirical results underscore a positive and statistically significant relationship between information asymmetry and return volatility in the TSE. These findings have profound implications for enhancing market efficiency, risk management, and derivative pricing strategies in emerging financial markets.