Abstract
Volatility clustering and leverage are two of the most prominent stylized features of the dynamics of asset prices. In order to incorporate these features as well as the typical fat-tails of the log return distributions, several types of exponential Lévy models driven by random clocks have been proposed in the literature. These models constitute a viable alternative to the classical stochastic volatility approach based on SDEs driven by Wiener processes. This paper has two main objectives. First, using threshold type estimators based on high-frequency discrete observations of the process, we consider the recovery problem of the underlying random clock of the process. We show consistency of our estimator in the mean-square sense, extending former results in the literature for more general Lévy processes and for irregular sampling schemes. Secondly, we illustrate empirically the estimation of the random clock, the Blumenthal-Geetor index of jump activity, and the spectral Lévy measure of the process using real intraday high-frequency data.
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Figueroa-López, J.E. Statistical estimation of Lévy-type stochastic volatility models. Ann Finance 8, 309–335 (2012). https://doi.org/10.1007/s10436-010-0150-x
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DOI: https://doi.org/10.1007/s10436-010-0150-x
Keywords
- Time-changed Lévy model
- Stochastic volatility
- Random clock
- Non-parametric estimation
- Parameter estimation based on high-frequency data