Abstract
This study proposes a liquidity–adjusted range–based volatility estimator by integrating market microstructure noise into the Rogers and Satchell (1991) framework using bid–ask spreads. Employing daily bid–ask spread information, our specification captures liquidity–induced volatility components and improves the precision of range–based estimation. Analyzing a comprehensive dataset from 2020 to 2025, we find that the proposed specification significantly improves benchmark volatility tracking for BTC, GLD, and USO, whereas it shows no incremental performance gain for the highly liquid equity ETF, SPY. This divergence suggests that bid–ask spread information is particularly valuable when liquidity–related microstructure frictions are economically significant. Our proposed model optimizes volatility estimation for continuous trading under high–frequency coverage constraints.