The purpose of this study is to put forward the impact of asymmetric information on stock return volatility under structural breaks by using ARFIMA-FIGARCH dual long memory and Markov Switching Regression models. Accordingly, dollar-denominated daily prices, and daily bid-ask spread and turnover of the BIST 100 index for the period 04.01.2010-31.07.2019 have been considered in the study. In the study, possible breaks in variance of return series have determined by Sanso et al. (2004) 's break in variance test, but it could not been determined any breaks. Therefore, the optimal volatility model for the stock return volatility series have been investigated by using ARFIMA-FIGARCH model without taking structural breaks into consideration. It has been determined that the most suitable model is ARFIMA(3ξ,d,1) according to the skewed student t distribution. Finally in the study, the impact of asymmetric information on stock return volatility has been investigated by using Markov Switching Regression model. It has been found that the bid-ask spread has an additive effect on stock return volatility in both regimes. Furthermore, it has been concluded that turnover has an additive effect on stock return volatility in Regime 1; but turnover has an detractive effect on stock return volatility in Regime 2.