Abstract
Numerous volatility-based derivative products have been engineered in recent years. This has led to interest in constructing conditional predictive densities and confidence intervals for integrated volatility. In this article 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.
Original language | English (US) |
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Pages (from-to) | 1496-1512 |
Number of pages | 17 |
Journal | Journal of the American Statistical Association |
Volume | 106 |
Issue number | 496 |
DOIs | |
State | Published - Dec 2011 |
Keywords
- Diffusion
- Jump
- Kernel
- Microstructure noise
- Prediction
- Realized volatility measure
ASJC Scopus subject areas
- Statistics and Probability
- Statistics, Probability and Uncertainty