Estimation of integrated quadratic covariation with endogenous sampling times

Yoann Potiron, Per A. Mykland

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

When estimating high-frequency covariance (quadratic covariation) of two arbitrary assets observed asynchronously, simple assumptions, such as independence, are usually imposed on the relationship between the prices process and the observation times. In this paper, we introduce a general endogenous two-dimensional nonparametric model. Because an observation is generated whenever an auxiliary process called observation time process hits one of the two boundary processes, it is called the hitting boundary process with time process (HBT) model. We establish a central limit theorem for the Hayashi–Yoshida (HY) estimator under HBT in the case where the price process and the observation price process follow a continuous Itô process. We obtain an asymptotic bias. We provide an estimator of the latter as well as a bias-corrected HY estimator of the high-frequency covariance. In addition, we give a consistent estimator of the associated standard error.

Original languageEnglish
Pages (from-to)20-41
Number of pages22
JournalJournal of Econometrics
Volume197
Issue number1
DOIs
Publication statusPublished - 2017 Mar 1

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Heterojunction bipolar transistors
Sampling
Estimator
Asymptotic Bias
Endogenous sampling
Integrated
Consistent Estimator
Nonparametric Model
Standard error
Hits
Central limit theorem
Process Model
Observation
Arbitrary

Keywords

  • Asymptotic bias
  • Asynchronous times
  • Endogenous model
  • Hayashi–Yoshida estimator
  • High-frequency data
  • Quadratic covariation
  • Time endogeneity

ASJC Scopus subject areas

  • Economics and Econometrics
  • Applied Mathematics
  • History and Philosophy of Science

Cite this

Estimation of integrated quadratic covariation with endogenous sampling times. / Potiron, Yoann; Mykland, Per A.

In: Journal of Econometrics, Vol. 197, No. 1, 01.03.2017, p. 20-41.

Research output: Contribution to journalArticle

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