When Moving‐Average Models Meet High‐Frequency Data : Uniform Inference on Volatility
We conduct inference on volatility with noisy high‐frequency data. We assume the observed transaction price follows a continuous‐time Itô‐semimartingale, contaminated by a discrete‐time moving‐average noise process associated with the arrival of trades. We estimate volatility, defined as the quadratic variation of the semimartingale, by maximizing the likelihood of a misspecified moving‐average model, with its order selected based on an information criterion. Our inference is uniformly valid over a large class of noise processes whose magnitude and dependence structure vary with sample size. We show that the convergence rate of our estimator dominates
n 1/4 as noise vanishes, and is determined by the selected order of noise dependence when noise is sufficiently small. Our implementation guarantees positive estimates in finite samples.
Year of publication: |
2021
|
---|---|
Authors: | Da, Rui ; Xiu, Dacheng |
Published in: |
Econometrica. - The Econometric Society, ISSN 0012-9682, ZDB-ID 1477253-X. - Vol. 89.2021, 6, p. 2787-2825
|
Publisher: |
The Econometric Society |
Saved in:
Online Resource
Saved in favorites
Similar items by person
-
When Moving-Average Models Meet High-Frequency Data : Uniform Inference on Volatility
Da, Rui, (2019)
-
Disentangling Autocorrelated Intraday Returns
Da, Rui, (2021)
-
Taming the Factor Zoo : A Test of New Factors
FENG, GUANHAO, (2020)
- More ...