Publication Type
Journal Article
Version
acceptedVersion
Publication Date
9-2017
Abstract
We derive the asymptotic efficiency bound for regular estimates of the slope coefficient in a linear continuous-time regression model for the continuous martingale parts of two Itô semimartingales observed on a fixed time interval with asymptotically shrinking mesh of the observation grid. We further construct an estimator from high-frequency data that achieves this efficiency bound and, indeed, is adaptive to the presence of infinite-dimensional nuisance components. The estimator is formed by taking optimal weighted average of local nonparametric volatility estimates that are constructed over blocks of high-frequency observations. The asymptotic efficiency bound is derived under a Markov assumption for the bivariate process while the high-frequency estimator and its asymptotic properties are derived in a general Itô semimartingale setting. To study the asymptotic behavior of the proposed estimator, we introduce a general spatial localization procedure which extends known results on the estimation of integrated volatility functionals to more general classes of functions of volatility. Empirically relevant numerical examples illustrate that the proposed efficient estimator provides nontrivial improvement over alternatives in the extant literature.
Keywords
Adaptive estimation, Beta, Stochastic volatility, Spot variance, Semiparametric efficiency, High-frequency data
Discipline
Econometrics
Research Areas
Econometrics
Publication
Journal of Econometrics
Volume
200
Issue
1
First Page
36
Last Page
47
ISSN
0304-4076
Identifier
10.1016/j.jeconom.2017.01.010
Publisher
Elsevier
Citation
LI, Jia; TODOROV, Viktor; and TAUCHEN, George.
Adaptive estimation of continuous-time regression models using high-frequency data. (2017). Journal of Econometrics. 200, (1), 36-47.
Available at: https://ink.library.smu.edu.sg/soe_research/2582
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/10.1016/j.jeconom.2017.01.010