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PINstimation 0.1.3.9000 (28.07.2024)

New Features

  • 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.

Updates

  • 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.

Bugfixes

  • 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.

Dependency Management

  • Future Package: Addressed two concerns related to the 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.

Documentation

  • Replaced the deprecated '@doctype package' tag with '_Package' to ensure proper generation of package documentation.

PINstimation 0.1.2

New Features

  • 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.

Bugfixes

  • 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.

PINstimation 0.1.1

New Features

  • 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).

Bugfixes

  • 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.

PINstimation 0.1.0

  • Added a NEWS.md file to track changes to the package.