Fast and Stable Estimation of the Probability of Informed Trading (PIN)

Utilities for fast and stable estimation of the probability of informed trading (PIN) in the model introduced by Easley et al. (2002) are implemented. Since the basic model developed by Easley et al. (1996) is nested in the former due to equating the intensity of uninformed buys and sells, functions can also be applied to this simpler model structure, if needed. State-of-the-art factorization of the model likelihood function as well as most recent algorithms for generating initial values for optimization routines are implemented. In total, two likelihood factorizations and three methodologies for starting values are included. Furthermore, functions for simulating datasets of daily aggregated buys and sells, calculating confidence intervals for the probability of informed trading and posterior probabilities of trading days' conditions are available.


  • simulateBS for simulating daily buys and sells data
  • pin_confint: computes confidence intervals for the probability of informed trading
  • pin_est_core, pin_est and qpin gained two new argument: confint and ci_control
  • updated plotting structure for qpin_plot, facets are now grouped by probability parameters, intensity parameters and the probability of informed trading
  • initial_vals together with method = "HAC_Ref" now returns a number of sets of initial values depending on num_clust argument, not only one set
  • Vignette was added
  • initial release

Reference manual

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1.0.1 by Andreas Recktenwald, a month ago

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Authors: Andreas Recktenwald [aut, cre]

Documentation:   PDF Manual  

Task views: Empirical Finance

GPL-3 | file LICENSE license

Imports stats, fastcluster, lubridate, ggplot2, reshape2, scales, foreach, doParallel, parallel, iterators

Suggests knitr, rmarkdown, formatR, utils

See at CRAN