We introduce a new function called classify_trades()
that enables users to classify high-frequency (HF) trades individually,
without aggregating them.
For each HF trade, the function assigns a variable that is set to
TRUE if the trade is buyer-initiated, or FALSE
if it is seller-initiated.
The aggregate_trades() function enables users to
aggregate high-frequency (HF) trades at different frequencies. In the
previous version, HF trades were automatically aggregated into daily
trade data. However, with the updated version, users can now specify the
desired frequency, such as every 15 minutes.
We identified and corrected an error in the
mpin_ecm() function. Previously, the function would
sometimes produce inconsistent results as the posterior distribution
allowed for the existence of information layers with a probability of
zero. We have now fixed this issue and the function produces correct
results.
We have made some updates to the mpin_ml() function
to better handle cases where the MPIN estimation fails for all initial
parameter sets. Specifically, we have fixed an error in the display of
the estimation results when such failure occurs. With these updates, the
function should now be able to handle such failures more robustly and
provide appropriate feedback.
We have simplified the ECM estimation functions, with a particular focus on the adjpin() function. We have improved the convergence condition of the iterative process used in the ECM estimation. Moreover, we rounded the values of the parameters at each iteration to a relevant number of decimals. This shall result in a faster convergence and prevent issues with decreasing likelihood values.
The functions pin(), pin_*(),
mpin_ml(), mpin_ecm(), adjpin(),
vpin(), and aggregate_trades() accept now, for
their arguments data, datasets of type matrix.
In the previous version, it only accepted dataframes, which did not
allow users, for instance, to use rollapply() of the
package zoo.
Introduction of the function pin_bayes() that
estimates the original pin model using a bayesian approach as described
in Griffin et al.(2021).
Fixed an error in the function initials_pin_ea() as
it used to produce some parameter sets with negative values for trade
intensity rates. The negative trade intensity rates are set to
zero.
Fixed two errors in the function vpin(): (1) A bug
in the calculation steps of vpin (2) The argument verbose
does not work properly.
Fixed an issue with resetting the plan for the future
(future::plan) used for parallel processing.
NEWS.md file to track changes to the
package.