Publication Type
Journal Article
Version
acceptedVersion
Publication Date
10-2016
Abstract
We propose a consistent functional estimator for the occupation time of the spot variance of an asset price observed at discrete times on a finite interval with the mesh of the observation grid shrinking to zero. The asset price is modeled nonparametrically as a continuous-time Itô semimartingale with nonvanishing diffusion coefficient. The estimation procedure contains two steps. In the first step we estimate the Laplace transform of the volatility occupation time and, in the second step, we conduct a regularized Laplace inversion. Monte Carlo evidence suggests that the proposed estimator has good small-sample performance and in particular it is far better at estimating lower volatility quantiles and the volatility median than a direct estimator formed from the empirical cumulative distribution function of local spot volatility estimates. An empirical application shows the use of the developed techniques for nonparametric analysis of variation of volatility.
Keywords
semimartingale, long memory, stochastic volatility, semiparametric efficiency, local asymptotic mixed normality, irregular sampling.
Discipline
Econometrics
Research Areas
Econometrics
Publication
Econometric Theory
Volume
32
Issue
5
First Page
1253
Last Page
1288
ISSN
0266-4666
Identifier
10.1017/S0266466615000171
Publisher
Cambridge University Press
Citation
LI, Jia; TODOROV, Viktor; and TAUCHEN.
Estimating the volatility occupation time via regularized Laplace inversion. (2016). Econometric Theory. 32, (5), 1253-1288.
Available at: https://ink.library.smu.edu.sg/soe_research/2581
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1017/S0266466615000171