PINstimation (version 0.1.2)

pin_ea: PIN estimation - initial parameter sets of Ersan & Alici (2016)

Description

Estimates the Probability of Informed Trading (PIN) using the initial sets from the algorithm in ErsanAlici2016;textualPINstimation.

Usage

pin_ea(data, factorization, xtraclusters = 4, verbose = TRUE)

Value

Returns an object of class estimate.pin

Arguments

data

A dataframe with 2 variables: the first corresponds to buyer-initiated trades (buys), and the second corresponds to seller-initiated trades (sells).

factorization

A character string from {"E", "EHO", "LK", "NONE"} referring to a given factorization. The default value is "E".

xtraclusters

An integer used to divide trading days into #(2 + xtraclusters) clusters, thereby resulting in #comb(1 + xtraclusters, 1) initial parameter sets in line with ErsanAlici2016;textualPINstimation. The default value is 4.

verbose

A binary variable that determines whether detailed information about the steps of the estimation of the PIN model is displayed. No output is produced when verbose is set to FALSE. The default value is TRUE.

Details

The argument 'data' should be a numeric dataframe, and contain at least two variables. Only the first two variables will be considered: The first variable is assumed to correspond to the total number of buyer-initiated trades, while the second variable is assumed to correspond to the total number of seller-initiated trades. Each row or observation correspond to a trading day. NA values will be ignored.

The factorization variable takes one of four values:

• "EHO" refers to the factorization in Easley2010;textualPINstimation

• "LK" refers to the factorization in WilliamLin2011;textualPINstimation

• "E" refers to the factorization in Ersan2016;textualPINstimation

• "NONE" refers to the original likelihood function - with no factorization

The function pin_ea() implements the algorithm detailed in ErsanAlici2016;textualPINstimation. The higher the number of the additional layers (xtraclusters), the better is the estimation. ErsanAlici2016;textualPINstimation, however, have shown the benefit of increasing this number beyond 5 is marginal, and statistically insignificant.

The function initials_pin_ea() provides the initial parameter sets obtained through the implementation of the ErsanAlici2016;textualPINstimation algorithm. For further information on the initial parameter set determination, see initials_pin_ea().

References

Examples

# There is a preloaded quarterly dataset called 'dailytrades' with 60
# observations. Each observation corresponds to a day and contains the
# total number of buyer-initiated trades ('B') and seller-initiated
# trades ('S') on that day. To know more, type ?dailytrades

xdata <- dailytrades

# Estimate the PIN model using the factorization of Ersan (2016), and initial
# parameter sets generated using the algorithm of Ersan and Alici (2016).
# The argument xtraclusters is omitted so will take its default value 4.

estimate <- pin_ea(xdata, verbose = FALSE)

# Display the estimated PIN value

show(estimate@pin)

# Display the estimated parameters

show(estimate@parameters)

# Store the initial parameter sets used for MLE in a dataframe variable,
# and display its first five rows

initialsets <- estimate@initialsets
show(head(initialsets, 5))