Abstract
We consider specification and inference for the stochastic scale of discretely-observed pure-jump semimartingales with locally stable Lévy densities in the setting where both the time span of the data set increases, and the mesh of the observation grid decreases. The estimation is based on constructing a nonparametric estimate for the empirical Laplace transform of the stochastic scale over a given interval of time by aggregating high-frequency increments of the observed process on that time interval into a statistic we call realized Laplace transform. The realized Laplace transform depends on the activity of the driving pure-jump martingale, and we consider both cases when the latter is known or has to be inferred from the data.
Original language | English (US) |
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Pages (from-to) | 1233-1262 |
Number of pages | 30 |
Journal | Annals of Statistics |
Volume | 40 |
Issue number | 2 |
DOIs | |
State | Published - Apr 2012 |
Keywords
- High-frequency data
- Inference
- Jumps
- Laplace transform
- Time-varying scale
ASJC Scopus subject areas
- Statistics and Probability
- Statistics, Probability and Uncertainty