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

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 
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. Quadratic Variation , MarketMicrostructure Noise , Pre-averaging , Sampling Schemes , Jumps 
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Metadaten
Author:Nikolaus Hautsch, Mark Podolskij
URN:urn:nbn:de:hebis:30-75630
Parent Title (German):Center for Financial Studies (Frankfurt am Main): CFS working paper series ; No. 2010,17
Series (Serial Number):CFS working paper series (2010, 17)
Document Type:Working Paper
Language:English
Year of Completion:2010
Year of first Publication:2010
Publishing Institution:Univ.-Bibliothek Frankfurt am Main
Release Date:2010/09/04
Tag:Jumps; MarketMicrostructure Noise; Pre-averaging; Quadratic Variation; Sampling Schemes
Issue:July 2010
Pagenumber:57
HeBIS PPN:226768503
Institutes:Wirtschaftswissenschaften
Dewey Decimal Classification:330 Wirtschaft
JEL-Classification:C14 Semiparametric and Nonparametric Methods
C22 Time-Series Models; Dynamic Quantile Regressions (Updated!)
G10 General
Sammlungen:Universitätspublikationen
Licence (German):License Logo Veröffentlichungsvertrag für Publikationen

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