Estimating the Integrated Volatility with Tick Observations

55 Pages Posted: 14 Sep 2015 Last revised: 28 Aug 2017

See all articles by Jean Jacod

Jean Jacod

Université Paris VI Pierre et Marie Curie

Yingying Li

Hong Kong University of Science & Technology (HKUST), Dept of ISOM and Dept of Finance; Hong Kong University of Science & Technology (HKUST) - Department of Information Systems, Business Statistics and Operations Management; Hong Kong University of Science & Technology (HKUST) - Department of Finance

Xinghua Zheng

Hong Kong University of Science & Technology (HKUST) - Department of Information Systems, Business Statistics and Operations Management

Date Written: September 12, 2015

Abstract

We develop a volatility estimator that can be directly applied to tick-by-tick data. More specifically, we consider a model that allows for (i) irregular observation times that can be endogenous, (ii) dependent noise that can have diurnal features and be dependent on the latent price process, and (iii) jumps in the latent price process. We show that our estimator yields consistent estimates and enjoys the optimal rate of convergence. Simulation as well as empirical studies demonstrate favorable properties of our proposed estimator.

Keywords: High frequency data, integrated volatility, market microstructure noise, dependent noise, endogenous time

JEL Classification: C14, C13, D40

Suggested Citation

Jacod, Jean and Li, Yingying and Zheng, Xinghua, Estimating the Integrated Volatility with Tick Observations (September 12, 2015). Available at SSRN: https://ssrn.com/abstract=2659615 or http://dx.doi.org/10.2139/ssrn.2659615

Jean Jacod

Université Paris VI Pierre et Marie Curie ( email )

4, Place Jussieu, B.P. 169
Laboratoire de Probabilites
F-75252-Paris Cedex 05
France
01 44 27 53 21 (Phone)

Yingying Li

Hong Kong University of Science & Technology (HKUST), Dept of ISOM and Dept of Finance ( email )

Clear Water Bay, Kowloon
Hong Kong

Hong Kong University of Science & Technology (HKUST) - Department of Information Systems, Business Statistics and Operations Management ( email )

Clear Water Bay
Kowloon
Hong Kong

Hong Kong University of Science & Technology (HKUST) - Department of Finance ( email )

Clear Water Bay, Kowloon
Hong Kong

Xinghua Zheng (Contact Author)

Hong Kong University of Science & Technology (HKUST) - Department of Information Systems, Business Statistics and Operations Management ( email )

Clear Water Bay
Kowloon
Hong Kong

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