Ultra high frequency volatility estimation with dependent microstructure noise

Yacine At-Sahalia; Per A. Mykland; Lan Zhang

doi:10.1016/j.jeconom.2010.03.028

Ultra high frequency volatility estimation with dependent microstructure noise

Yacine At-Sahalia, Per A. Mykland, Lan Zhang

Research output: Contribution to journal › Article › peer-review

174 Scopus citations

Abstract

We analyze the impact of time series dependence in market microstructure noise on the properties of estimators of the integrated volatility of an asset price based on data sampled at frequencies high enough for that noise to be a dominant consideration. We show that combining two time scales for that purpose will work even when the noise exhibits time series dependence, analyze in that context a refinement of this approach is based on multiple time scales, and compare empirically our different estimators to the standard realized volatility.

Original language	English (US)
Pages (from-to)	160-175
Number of pages	16
Journal	Journal of Econometrics
Volume	160
Issue number	1
DOIs	https://doi.org/10.1016/j.jeconom.2010.03.028
State	Published - Jan 2011

All Science Journal Classification (ASJC) codes

Economics and Econometrics

Keywords

High frequency data
Market microstructure
Multiple scales realized volatility
Realized volatility
Serial dependence
Subsampling
Two scales realized volatility

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10.1016/j.jeconom.2010.03.028

Cite this

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title = "Ultra high frequency volatility estimation with dependent microstructure noise",

abstract = "We analyze the impact of time series dependence in market microstructure noise on the properties of estimators of the integrated volatility of an asset price based on data sampled at frequencies high enough for that noise to be a dominant consideration. We show that combining two time scales for that purpose will work even when the noise exhibits time series dependence, analyze in that context a refinement of this approach is based on multiple time scales, and compare empirically our different estimators to the standard realized volatility.",

keywords = "High frequency data, Market microstructure, Multiple scales realized volatility, Realized volatility, Serial dependence, Subsampling, Two scales realized volatility",

author = "Yacine At-Sahalia and Mykland, {Per A.} and Lan Zhang",

note = "Funding Information: We are grateful to Joel Hasbrouck for comments and help with the sequencing of trades. Financial support from the NSF under grants SBR-0350772 (A{\"i}t-Sahalia), DMS-0204639 , DMS 06-04758 , and SES 06-31605 (Mykland and Zhang) and the NIH under grant RO1 AG023141-01 (Zhang) is also gratefully acknowledged. ",

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doi = "10.1016/j.jeconom.2010.03.028",

language = "English (US)",

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journal = "Journal of Econometrics",

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T1 - Ultra high frequency volatility estimation with dependent microstructure noise

AU - At-Sahalia, Yacine

AU - Mykland, Per A.

AU - Zhang, Lan

N1 - Funding Information: We are grateful to Joel Hasbrouck for comments and help with the sequencing of trades. Financial support from the NSF under grants SBR-0350772 (Aït-Sahalia), DMS-0204639 , DMS 06-04758 , and SES 06-31605 (Mykland and Zhang) and the NIH under grant RO1 AG023141-01 (Zhang) is also gratefully acknowledged.

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N2 - We analyze the impact of time series dependence in market microstructure noise on the properties of estimators of the integrated volatility of an asset price based on data sampled at frequencies high enough for that noise to be a dominant consideration. We show that combining two time scales for that purpose will work even when the noise exhibits time series dependence, analyze in that context a refinement of this approach is based on multiple time scales, and compare empirically our different estimators to the standard realized volatility.

AB - We analyze the impact of time series dependence in market microstructure noise on the properties of estimators of the integrated volatility of an asset price based on data sampled at frequencies high enough for that noise to be a dominant consideration. We show that combining two time scales for that purpose will work even when the noise exhibits time series dependence, analyze in that context a refinement of this approach is based on multiple time scales, and compare empirically our different estimators to the standard realized volatility.

KW - High frequency data

KW - Market microstructure

KW - Multiple scales realized volatility

KW - Realized volatility

KW - Serial dependence

KW - Subsampling

KW - Two scales realized volatility

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JO - Journal of Econometrics

JF - Journal of Econometrics

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Ultra high frequency volatility estimation with dependent microstructure noise

Abstract

All Science Journal Classification (ASJC) codes

Keywords

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