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 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 preaveraging 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.
Year of publication: |
2010
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Authors: | Hautsch, Nikolaus ; Podolskij, Mark |
Publisher: |
Frankfurt a. M. : Goethe University Frankfurt, Center for Financial Studies (CFS) |
Subject: | Quadratic Variation | MarketMicrostructure Noise | Pre-averaging | Sampling Schemes | Jumps |
Saved in:
freely available
Series: | CFS Working Paper ; 2010/17 |
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Type of publication: | Book / Working Paper |
Type of publication (narrower categories): | Working Paper |
Language: | English |
Other identifiers: | 639511031 [GVK] hdl:10419/43201 [Handle] RePEc:zbw:cfswop:201017 [RePEc] |
Classification: | C14 - Semiparametric and Nonparametric Methods ; C22 - Time-Series Models ; G10 - General Financial Markets. General |
Source: |
Persistent link: https://www.econbiz.de/10010303682