Modeling and forecasting realized volatility with the fractional Ornstein-Uhlenbeck process

• Main Authors: WANG, Xiaohu, XIAO, Weilin, Jun YU
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2023
• Subjects: Rough volatility; Fractional Ornstein-Uhlenbeck process; Hurst parameter; Long memory; Anti-persistent errors; Out-of-sample forecasting; ARFIMA; Econometrics
• Online Access: https://ink.library.smu.edu.sg/soe_research/2592; https://ink.library.smu.edu.sg/context/soe_research/article/3591/viewcontent/ModelingRealizedVolatility_sv.pdf
• Institution: Singapore Management University

This paper proposes to model and forecast realized volatility (RV) using the fractional Ornstein-Uhlenbeck (fO-U) process with a general Hurst parameter, H. A two-stage method is introduced for estimating parameters in the fO-U process based on discrete-sampled observations. In the first stage, H is...