An improved algorithm for cleaning ultrahigh frequency data
Verousis, Thanos Ap Gwilym, Owain
Verousis, Thanos
Ap Gwilym, Owain
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Publication Type
Journal article
Editor
Supervisor
Publication Year
2010
Journal
Journal of Derivatives & Hedge Funds
Book
Publication Volume
15
Publication Issue
Publication Begin page
323
Publication End page
340
Publication Number of pages
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Abstract
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.
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Keywords
Ultra High Frequency, Data Mining and Cleaning, Equity Options, LIFFE