Local Parametric Estimation in High Frequency Data

Yoann Potiron, Per Mykland

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

We give a general time-varying parameter model, where the multidimensional parameter possibly includes jumps. The quantity of interest is defined as the integrated value over time of the parameter process (Formula presented.). We provide a local parametric estimator (LPE) of Θ and conditions under which we can show the central limit theorem. Roughly speaking those conditions correspond to some uniform limit theory in the parametric version of the problem. The framework is restricted to the specific convergence rate n 1∕2 . Several examples of LPE are studied: estimation of volatility, powers of volatility, volatility when incorporating trading information and time-varying MA(1).

Original languageEnglish
JournalJournal of Business and Economic Statistics
DOIs
Publication statusPublished - 2019 Jan 1

Fingerprint

Parametric Estimation
High-frequency Data
Volatility
Estimator
Time-varying Parameters
Process Parameters
Central limit theorem
Convergence Rate
Time-varying
Jump
time
High-frequency data
Values
Model

Keywords

  • Integrated volatility
  • Market microstructure noise
  • Powers of volatility
  • Quasi-maximum likelihood estimator

ASJC Scopus subject areas

  • Statistics and Probability
  • Social Sciences (miscellaneous)
  • Economics and Econometrics
  • Statistics, Probability and Uncertainty

Cite this

Local Parametric Estimation in High Frequency Data. / Potiron, Yoann; Mykland, Per.

In: Journal of Business and Economic Statistics, 01.01.2019.

Research output: Contribution to journalArticle

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