Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/59500
Authors: 
Corradi, Valentina
Distaso, Walter
Swanson, Norman R.
Year of Publication: 
2011
Series/Report no.: 
Working Papers, Department of Economics, Rutgers, the State University of New Jersey 2011,08
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 kernel estimators of the aforementioned quantities. The kernel functions used in our analysis are based on different realized volatility measures, which are constructed using the ex post variation of asset prices. A set of sufficient conditions under which the estimators are asymptotically equivalent to their unfeasible counterparts, based on the unobservable volatility process, is provided. Asymptotic normality is also established. The efficacy of the estimators is examined via Monte Carlo experimentation, and an empirical illustration based upon data from the New York Stock Exchange is provided.
Subjects: 
diffusions
integrated volatility
realized volatility measures
kernels
microstructure noise
conditional confidence intervals
jumps
prediction
JEL: 
C22
C53
C14
Document Type: 
Working Paper

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