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dc.contributor.authorVerousis, Thanos
dc.contributor.authorAp Gwilym, Owain
dc.date.accessioned2024-07-03T10:17:57Z
dc.date.available2024-07-03T10:17:57Z
dc.date.issued2010en_US
dc.identifier.urihttp://hdl.handle.net/20.500.12127/7516
dc.description.abstractWe develop a multiple-stage algorithm for detecting outliers in Ultra High-Frequency financial market data. We show that an efficient data filter needs to address four effects: the minimum tick size, the price level, the volatility of prices and the distribution of returns. We argue that previous studies tend to address only the distribution of returns, and may tend to ‘overscrub’ a data set. In this study, we address these issues in the market microstructure element of the algorithm. In the statistical element, we implement the robust median absolute deviation method to take into account the statistical properties of financial time series. The data filter is then tested against previous data-cleaning techniques and validated using a rich individual equity options transactions data set from the London International Financial Futures and Options Exchange. The paper has many relevant insights for any practitioner who uses high frequency derivatives data, for example, for market analysis or for developing trading strategies.en_US
dc.language.isoenen_US
dc.publisherPalgrave Macmillan Chamen_US
dc.subjectUltra High Frequencyen_US
dc.subjectData Mining and Cleaningen_US
dc.subjectEquity Optionsen_US
dc.subjectLIFFEen_US
dc.titleAn improved algorithm for cleaning ultrahigh frequency dataen_US
dc.identifier.journalJournal of Derivatives & Hedge Fundsen_US
dc.source.volume15en_US
dc.source.beginpage323en_US
dc.source.endpage340en_US
dc.contributor.departmentSchool of Business & Economics, Swansea University, Singleton Park, SA2 8PP, Swansea, UKen_US
vlerick.knowledgedomainAccounting & Financeen_US
vlerick.typearticleJournal articleen_US
vlerick.vlerickdepartmentAFen_US
dc.identifier.vperid311477en_US


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