rmOutliersQuotes {highfrequency}

Remove outliers in quotes

Description

Delete entries for which the mid-quote is outlying with respect to surrounding entries.

Usage

rmOutliersQuotes(qData, maxi = 10, window = 50, type = "advanced", tz = NULL)

Arguments

qData

a data.table or xts object at least containing the columns "BID" and "OFR".

maxi

an integer, indicating the maximum number of median absolute deviations allowed.

window

an integer, indicating the time window for which the "outlyingness" is considered.

type

should be "standard" or "advanced" (see details).

tz

fallback time zone used in case we we are unable to identify the timezone of the data, by default: tz = NULL. With the non-disk functionality, we attempt to extract the timezone from the DT column (or index) of the data, which may fail. In case of failure we use tz if specified, and if it is not specified, we use "UTC".

Details

• If type = "standard": Function deletes entries for which the mid-quote deviated by more than "maxi" median absolute deviations from a rolling centered median (excluding the observation under consideration) of window observations.

• If type = "advanced": Function deletes entries for which the mid-quote deviates by more than "maxi" median absolute deviations from the value closest to the mid-quote of these three options:

  1. Rolling centered median (excluding the observation under consideration)

  2. Rolling median of the following window of observations

  3. Rolling median of the previous window of observations

  The advantage of this procedure compared to the "standard" proposed by Barndorff-Nielsen et al. (2010) is that it will not incorrectly remove large price jumps. Therefore this procedure has been set as the default for removing outliers.

  Note that the median absolute deviation is taken over the entire day. In case it is zero (which can happen if mid-quotes don't change much), the median absolute deviation is taken over a subsample without constant mid-quotes.

Value

xts object or data.table depending on type of input.

Author(s)

Jonathan Cornelissen and Kris Boudt.

References

Barndorff-Nielsen, O. E., P. R. Hansen, A. Lunde, and N. Shephard (2009). Realized kernels in practice: Trades and quotes. Econometrics Journal, 12, C1-C32.

Brownlees, C.T., and Gallo, G.M. (2006). Financial econometric analysis at ultra-high frequency: Data handling concerns. Computational Statistics & Data Analysis, 51, 2232-2245.