Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/59492 
Year of Publication: 
2011
Series/Report no.: 
Working Paper No. 2011-09
Publisher: 
Rutgers University, Department of Economics, New Brunswick, NJ
Abstract: 
In recent years, numerous volatility-based derivative products have been engineered. This has led to interest in constructing conditional predictive densities and confidence intervals for integrated volatility. In this paper, we propose nonparametric estimators of the aforementioned quantities, based on model free volatility estimators. We establish consistency and asymptotic normality for the feasible estimators and study their finite sample properties through a Monte Carlo experiment. Finally, using data from the New York Stock Exchange, we provide an empirical application to volatility directional predictability.
Subjects: 
diffusions
realized volatility measures
kernels
microstructure noise
jumps
prediction
Document Type: 
Working Paper

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