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An improved algorithm for cleaning Ultra High-Frequency data

Verousis, Thanos and ap Gwilym, Owain (2010) 'An improved algorithm for cleaning Ultra High-Frequency data.' Journal of Derivatives and Hedge Funds, 15 (4). pp. 323-340. ISSN 1357-0927

An improved algorithm for cleaning Ultra High Frequency data.pdf - Accepted Version

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We 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.

Item Type: Article
Uncontrolled Keywords: ultra high frequency, data mining and cleaning, equity options, LIFFE
Subjects: H Social Sciences > HG Finance
Divisions: Faculty of Social Sciences
Faculty of Social Sciences > Essex Business School
Faculty of Social Sciences > Essex Business School > Essex Finance Centre
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 14 Mar 2019 19:32
Last Modified: 06 Jan 2022 13:53

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