A Reduced Form Framework for Modeling Volatility of Speculative Prices Based on Realized Variation Measures

237 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Do Jumps Matter for Volatility Forecasting? Evidence from Energy Markets (2016, background): “Our paper relates to the important works of Andersen et al. (2007) and Andersen et al. (2011), who study the importance of jumps for volatility forecasting in the equity, fixed-income and foreign exchange markets.”
Measuring and Forecasting Volatility in Chinese Stock Market Using HAR-CJ-M Model (2013, methodology): “[14] also showed that the overnight return variance r2 t,n in SP and US markets made up 16.”
Continuous-Time Models, Realized Volatilities, and Testable Distributional Implications for Daily Stock Returns (2010, methodology): “Compared to Andersen et al. (2007b) we accommodate the issue of microstructure noise more directly through the generalized signature plots and rely on the new sequential jump detection technique.”
Roughing up Beta: Continuous vs. Discontinuous Betas, and the Cross-Section of Expected Stock Returns (2014, unknown): “22This characterization of the overnight returns as discontinuous movements occurring at deterministic times mirrors the high-frequency modeling approach recently advocated by Andersen, Bollerslev, and Huang (2011). these different estimators.”
Nonparametric Estimation and Forecasting for Time-Varying Coefficient Realized Volatility Models (2018, methodology): “(2011), as there is a dynamic structure involved in model (4) and the commonly-assumed α-mixing condition may not be satisfied in our setting.”