Predictive density estimators for daily volatility based on the use of realized measures

Arbeitspapier

The main objective of this paper is to propose a feasible, model free estimator of the predictive density of integrated volatility. In this sense, we extend recent papers by Andersen, Bollerslev, Diebold and Labys (2003), and by Andersen, Bollerslev and Meddahi (2004, 2005), who address the issue of pointwise prediction of volatility via ARMA models, based on the use of realized volatility. Our approach is to use a realized volatility measure to construct a non parametric (kernel) estimator of the predictive density of daily volatility. We show that, by choosing an appropriate realized measure, one can achieve consistent estimation, even in the presence of jumps and microstructure noise in prices. More precisely, we establish that four well known realized measures, i.e. realized volatility, bipower variation, and two measures robust to microstructure noise, satisfy the conditions required for the uniform consistency of our estimator. Furthermore, we outline an alternative simulation based approach to predictive density construction. Finally, we carry out a simulation experiment in order to assess the accuracy of our estimators, and provide an empirical illustration that underscores the importance of using microstructure robust measures when using high frequency data.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Working Paper ; No. 2006-20

Classification and Topics

• Classification: Wirtschaft ; Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes ; Forecasting Models; Simulation Methods ; Semiparametric and Nonparametric Methods: General
• Subject: Diffusions ; integrated volatility ; kernels ; microstructure noise ; realized volatility measures

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Corradi, Valentina ; Distaso, Walter ; Swanson, Norman R.
• Event: Veröffentlichung
• (who): Rutgers University, Department of Economics
• (where): New Brunswick, NJ
• (when): 2006

Further information

• Handle: http://hdl.handle.net/10419/31280
• Last update: 10.03.2025, 11:44 AM CET