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On estimating the integrated co-volatility using noisy high-frequency data with jumps
Jing B.-Y.1; Li C.-X.1; Liu Z.3
2013-11-02
Source PublicationCommunications in Statistics - Theory and Methods
ISSN03610926 1532415X
Volume42Issue:21Pages:3889-3901
Abstract

In this article, we consider the estimation of covariation of two asset prices which contain jumps and microstructure noise, based on high-frequency data. We propose a realized covariance estimator, which combines pre-averaging method to remove the microstructure noise and the threshold method to reduce the jumps effect. The asymptotic properties, such as consistency and asymptotic normality, are investigated. The estimator allows very general structure of jumps, for example, infinity activity or even infinity variation. Simulation is also included to illustrate the performance of the proposed procedure. © 2013 Copyright Taylor and Francis Group, LLC.

KeywordCentral Limit Theorem Co-volatility High-frequency Data Ito Semi-martingale Jumps Microstructure Noise
DOI10.1080/03610926.2011.639974
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaMathematics
WOS SubjectStatistics & Probability
WOS IDWOS:000325197900006
Scopus ID2-s2.0-84885615667
Fulltext Access
Citation statistics
Document TypeJournal article
CollectionDEPARTMENT OF MATHEMATICS
Affiliation1.Lanzhou University
2.Hong Kong University of Science and Technology
3.Universidade de Macau
Recommended Citation
GB/T 7714
Jing B.-Y.,Li C.-X.,Liu Z.. On estimating the integrated co-volatility using noisy high-frequency data with jumps[J]. Communications in Statistics - Theory and Methods, 2013, 42(21), 3889-3901.
APA Jing B.-Y.., Li C.-X.., & Liu Z. (2013). On estimating the integrated co-volatility using noisy high-frequency data with jumps. Communications in Statistics - Theory and Methods, 42(21), 3889-3901.
MLA Jing B.-Y.,et al."On estimating the integrated co-volatility using noisy high-frequency data with jumps".Communications in Statistics - Theory and Methods 42.21(2013):3889-3901.
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