Publication Type
Journal Article
Version
acceptedVersion
Publication Date
7-2016
Abstract
We propose a semiparametric two‐step inference procedure for a finite‐dimensional parameter based on moment conditions constructed from high‐frequency data. The population moment conditions take the form of temporally integrated functionals of state‐variable processes that include the latent stochastic volatility process of an asset. In the first step, we nonparametrically recover the volatility path from high‐frequency asset returns. The nonparametric volatility estimator is then used to form sample moment functions in the second‐step GMM estimation, which requires the correction of a high‐order nonlinearity bias from the first step. We show that the proposed estimator is consistent and asymptotically mixed Gaussian and propose a consistent estimator for the conditional asymptotic variance. We also construct a Bierens‐type consistent specification test. These infill asymptotic results are based on a novel empirical‐process‐type theory for general integrated functionals of noisy semimartingale processes.
Keywords
high frequency data, semimartingale, spot volatility, nonlinearity bias, GMM
Discipline
Econometrics
Research Areas
Econometrics
Publication
Econometrica
Volume
84
Issue
4
First Page
1613
Last Page
1633
ISSN
0012-9682
Identifier
10.3982/ECTA12306
Publisher
Econometric Society
Citation
LI, Jia and XIU, Dacheng.
Generalized method of integrated moments for high-frequency data. (2016). Econometrica. 84, (4), 1613-1633.
Available at: https://ink.library.smu.edu.sg/soe_research/2526
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/10.3982/ECTA12306