Jump-robust volatility estimation using nearest neighbor truncation

Torben G. Andersen*, Dobrislav Dobrev, Ernst Schaumburg

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

211 Scopus citations

Abstract

We propose two new jump-robust estimators of integrated variance that allow for an asymptotic limit theory in the presence of jumps. Specifically, our MedRV estimator has better efficiency properties than the tripower variation measure and displays better finite-sample robustness to jumps and small ("zero") returns. We stress the benefits of local volatility measures using short return blocks, as this greatly alleviates the downward biases stemming from rapid fluctuations in volatility, including diurnal (intraday) U-shape patterns. An empirical investigation of the Dow Jones 30 stocks and extensive simulations corroborate the robustness and efficiency properties of our nearest neighbor truncation estimators.

Original languageEnglish (US)
Pages (from-to)75-93
Number of pages19
JournalJournal of Econometrics
Volume169
Issue number1
DOIs
StatePublished - Jul 2012

Keywords

  • Finite activity jumps
  • High-frequency data
  • Integrated variance
  • Intraday U-shape patterns
  • Jump robustness
  • Nearest neighbor truncation
  • Realized volatility

ASJC Scopus subject areas

  • Economics and Econometrics

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