New Features
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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 toTRUEif the
trade is buyer-initiated, orFALSEif 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.
New Bugfixes
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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.