adjpin(): The function now includes the time spent on generating initial
parameter sets in the total time displayed in the output. This enhancement
provides a more comprehensive view of the time taken for the entire process.
ivpin(): This function implements an improved version of the
Volume-Synchronized Probability of Informed Trading (VPIN) based on the work of
Lin and Ke (2017). By employing a maximum likelihood estimation, ivpin()
enhances the stability of VPIN estimates, especially in cases with small volume
buckets or infrequent informed trades. The function captures the information
embedded in volume time, generating more consistent and reliable results. It is
designed to improve the predictability of flow toxicity in trading environments.
initials_adjpin_rnd(): Updated the implementation for generating random
initial parameter sets to align with the algorithm described in Ersan and
Ghachem (2024).initials_adjpin(): Aligned the function, which generates initial parameter
sets for the adjusted PIN model, with the algorithm outlined in Ersan and
Ghachem (2024).solve_eqx: Enhanced the format and performance of polynomial root
calculations within the conditional-maximization steps of the ECM algorithm.initials_adjpin_cl(): Fixed an issue with the calculation of the
likelihood value according to the algorithm of Cheng and Lai (2021).detectlayers_eg(): Corrected the return value to a single number when
the number of information days is equal to 1. Previously, it incorrectly
returned a vector.mpin_ecm(): Rectified an issue where observations with zero probability
in the E-step of the ECM algorithm were assigned a fixed number of clusters
(6). Now, the function assigns a uniform probability of 1/cls, where cls
is the total number of clusters, to each cluster.future package.
The updated code now uses lexical scoping with the local() function to manage
the variable .lwbound between parallel function calls, preventing unexpected
results. Additionally, the maximum size of futures is no longer set to +Inf
upon package loading, leaving this option adjustable by the user.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.