Pre-averaging based estimation of quadratic variation in the presence of noise and jumps: Theory, implementation, and empirical evidence

Arbeitspapier

This paper provides theory as well as empirical results for pre-averaging estimators of the daily quadratic variation of asset prices. We derive jump robust inference for pre-averaging estimators, corresponding feasible central limit theorems and an explicit test on serial dependence in microstructure noise. Using transaction data of different stocks traded at the NYSE, we analyze the estimators' sensitivity to the choice of the pre-averaging bandwidth and suggest an optimal interval length. Moreover, we investigate the dependence of pre-averaging based inference on the sampling scheme, the sampling frequency, microstructure noise properties as well as the occurrence of jumps. As a result of a detailed empirical study we provide guidance for optimal implementation of pre-averaging estimators and discuss potential pitfalls in practice.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: SFB 649 Discussion Paper ; No. 2010-038

Classification and Topics

• Classification: Wirtschaft ; Semiparametric and Nonparametric Methods: General ; Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes ; General Financial Markets: General (includes Measurement and Data)
• Subject: quadratic variation ; market microstructure noise ; pre-averaging ; sampling schemes ; jumps ; Börsenkurs ; Zeitreihenanalyse ; Mikrostrukturanalyse ; Noise Trading ; Theorie ; Schätzung ; USA

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Hautsch, Nikolaus ; Podolskij, Mark
• Event: Veröffentlichung
• (who): Humboldt University of Berlin, Collaborative Research Center 649 - Economic Risk
• (where): Berlin
• (when): 2010

Further information

• Handle: http://hdl.handle.net/10419/56650
• Last update: 10.03.2025, 11:42 AM CET