Delete entries for which the mid-quote is outlying with respect to surrounding entries.
rmOutliersQuotes(qData, maxi = 10, window = 50, type = "advanced", tz = NULL)xts object or data.table depending on type of input.
a data.table or xts object at least containing the columns "BID" and "OFR".
an integer, indicating the maximum number of median absolute deviations allowed.
an integer, indicating the time window for which the "outlyingness" is considered.
should be "standard" or "advanced" (see details).
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".
Jonathan Cornelissen and Kris Boudt.
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:
Rolling centered median (excluding the observation under consideration)
Rolling median of the following window of observations
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.
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.