Publication Type
Journal Article
Version
acceptedVersion
Publication Date
8-2021
Abstract
We provide an asymptotic theory for the estimation of a general class of smooth nonlinear integrated volatility functionals. Such functionals are broadly useful for measuring financial risk and estimating economic models using high-frequency transaction data. The theory is valid under general volatility dynamics, which accommodates both Itô semimartingales (e.g., jump-diffusions) and long-memory processes (e.g., fractional Brownian motions). We establish the semiparametric efficiency bound under a nonstandard nonergodic setting with infill asymptotics, and show that the proposed estimator attains this efficiency bound. These results on efficient estimation are further extended to a setting with irregularly sampled data.
Keywords
Brownian motion, Economic model, Transaction data, Nonlinear system, Estimator, Applied mathematics, Mathematics, Financial risk, Volatility (finance)
Discipline
Econometrics
Research Areas
Econometrics
Publication
Econometric Theory
Volume
37
Issue
4
First Page
664
Last Page
707
ISSN
0266-4666
Identifier
10.1017/S0266466620000274
Publisher
Cambridge University Press
Citation
LI, Jia and Liu, Yunxiao.
Efficient estimation of integrated volatility functionals under general volatility dynamics. (2021). Econometric Theory. 37, (4), 664-707.
Available at: https://ink.library.smu.edu.sg/soe_research/2561
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/S0266466620000274