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Estimating integrated co-volatility with partially miss-ordered high frequency data
Liu Z.
2016-07-01
Source PublicationStatistical Inference for Stochastic Processes
ISSN15729311 13870874
Volume19Issue:2Pages:175-197
Abstract

The covariation of short-time period returns between securities plays an important role in many area of finance. Under the wide availability of high frequency financial data, realized covariation, as an ex-post measure of the covariation, can accurately estimate the quadratic covariation. However, the realized covariation fails to work when the multiple records appear. In this paper, we propose an estimator of integrated covariation, which is robust to the high frequency data containing multiple records. Consistency of the estimator and central limit theorem have been established. Moreover, several extensions which make the estimator available to different types of high frequency data are also considered. Simulation study confirms the performance of the estimator.

KeywordCentral Limit Theorem Diffusion Model High Frequency Data Multiple Transactions Stable Convergence
DOI10.1007/s11203-015-9124-y
URLView the original
Language英语
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Document TypeJournal article
CollectionDEPARTMENT OF MATHEMATICS
AffiliationUniversidade de Macau
Recommended Citation
GB/T 7714
Liu Z.. Estimating integrated co-volatility with partially miss-ordered high frequency data[J]. Statistical Inference for Stochastic Processes,2016,19(2):175-197.
APA Liu Z..(2016).Estimating integrated co-volatility with partially miss-ordered high frequency data.Statistical Inference for Stochastic Processes,19(2),175-197.
MLA Liu Z.."Estimating integrated co-volatility with partially miss-ordered high frequency data".Statistical Inference for Stochastic Processes 19.2(2016):175-197.
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