Efficient asymptotic variance reduction when estimating volatility in high frequency data

Simon Clinet, Yoann Potiron

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

This paper shows how to carry out efficient asymptotic variance reduction when estimating volatility in the presence of stochastic volatility and microstructure noise with the realized kernels (RK) from Barndorff-Nielsen etal. (2008) and the quasi-maximum likelihood estimator (QMLE) studied in Xiu (2010). To obtain such a reduction, we chop the data into B blocks, compute the RK (or QMLE) on each block, and aggregate the block estimates. The ratio of asymptotic variance over the bound of asymptotic efficiency converges as B increases to the ratio in the parametric version of the problem, i.e. 1.0025 in the case of the fastest RK Tukey-Hanning 16 and 1 for the QMLE. The impact of stochastic sampling times and jump in the price process is examined carefully. The finite sample performance of both estimators is investigated in simulations, while empirical work illustrates the gain in practice.

Original languageEnglish
JournalJournal of Econometrics
DOIs
Publication statusAccepted/In press - 2018 Jan 1

Fingerprint

Quasi-maximum Likelihood
High-frequency Data
Variance Reduction
Asymptotic Variance
Volatility
Maximum Likelihood Estimator
Maximum likelihood
kernel
Stochastic Volatility
Asymptotic Efficiency
Microstructure
Jump
Sampling
Converge
Estimator
Estimate
Kernel
Quasi-maximum likelihood estimator
Variance reduction
High-frequency data

Keywords

  • High frequency data
  • Integrated volatility
  • Jumps
  • Market microstructure noise
  • Quasi-maximum likelihood estimator
  • Realized kernels
  • Stochastic sampling times

ASJC Scopus subject areas

  • Economics and Econometrics
  • Applied Mathematics

Cite this

Efficient asymptotic variance reduction when estimating volatility in high frequency data. / Clinet, Simon; Potiron, Yoann.

In: Journal of Econometrics, 01.01.2018.

Research output: Contribution to journalArticle

@article{f8777b379a0b476bb3c5c9d9544b8a71,
title = "Efficient asymptotic variance reduction when estimating volatility in high frequency data",
abstract = "This paper shows how to carry out efficient asymptotic variance reduction when estimating volatility in the presence of stochastic volatility and microstructure noise with the realized kernels (RK) from Barndorff-Nielsen etal. (2008) and the quasi-maximum likelihood estimator (QMLE) studied in Xiu (2010). To obtain such a reduction, we chop the data into B blocks, compute the RK (or QMLE) on each block, and aggregate the block estimates. The ratio of asymptotic variance over the bound of asymptotic efficiency converges as B increases to the ratio in the parametric version of the problem, i.e. 1.0025 in the case of the fastest RK Tukey-Hanning 16 and 1 for the QMLE. The impact of stochastic sampling times and jump in the price process is examined carefully. The finite sample performance of both estimators is investigated in simulations, while empirical work illustrates the gain in practice.",
keywords = "High frequency data, Integrated volatility, Jumps, Market microstructure noise, Quasi-maximum likelihood estimator, Realized kernels, Stochastic sampling times",
author = "Simon Clinet and Yoann Potiron",
year = "2018",
month = "1",
day = "1",
doi = "10.1016/j.jeconom.2018.05.002",
language = "English",
journal = "Journal of Econometrics",
issn = "0304-4076",
publisher = "Elsevier BV",

}

TY - JOUR

T1 - Efficient asymptotic variance reduction when estimating volatility in high frequency data

AU - Clinet, Simon

AU - Potiron, Yoann

PY - 2018/1/1

Y1 - 2018/1/1

N2 - This paper shows how to carry out efficient asymptotic variance reduction when estimating volatility in the presence of stochastic volatility and microstructure noise with the realized kernels (RK) from Barndorff-Nielsen etal. (2008) and the quasi-maximum likelihood estimator (QMLE) studied in Xiu (2010). To obtain such a reduction, we chop the data into B blocks, compute the RK (or QMLE) on each block, and aggregate the block estimates. The ratio of asymptotic variance over the bound of asymptotic efficiency converges as B increases to the ratio in the parametric version of the problem, i.e. 1.0025 in the case of the fastest RK Tukey-Hanning 16 and 1 for the QMLE. The impact of stochastic sampling times and jump in the price process is examined carefully. The finite sample performance of both estimators is investigated in simulations, while empirical work illustrates the gain in practice.

AB - This paper shows how to carry out efficient asymptotic variance reduction when estimating volatility in the presence of stochastic volatility and microstructure noise with the realized kernels (RK) from Barndorff-Nielsen etal. (2008) and the quasi-maximum likelihood estimator (QMLE) studied in Xiu (2010). To obtain such a reduction, we chop the data into B blocks, compute the RK (or QMLE) on each block, and aggregate the block estimates. The ratio of asymptotic variance over the bound of asymptotic efficiency converges as B increases to the ratio in the parametric version of the problem, i.e. 1.0025 in the case of the fastest RK Tukey-Hanning 16 and 1 for the QMLE. The impact of stochastic sampling times and jump in the price process is examined carefully. The finite sample performance of both estimators is investigated in simulations, while empirical work illustrates the gain in practice.

KW - High frequency data

KW - Integrated volatility

KW - Jumps

KW - Market microstructure noise

KW - Quasi-maximum likelihood estimator

KW - Realized kernels

KW - Stochastic sampling times

UR - http://www.scopus.com/inward/record.url?scp=85048806404&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85048806404&partnerID=8YFLogxK

U2 - 10.1016/j.jeconom.2018.05.002

DO - 10.1016/j.jeconom.2018.05.002

M3 - Article

AN - SCOPUS:85048806404

JO - Journal of Econometrics

JF - Journal of Econometrics

SN - 0304-4076

ER -