PINstimation (version 0.1.2)

initials_pin_ea: Initial parameter sets of Ersan & Alici (2016)

Description

Based on the algorithm in Ersan and Alici (2016), generates initial parameter sets for the maximum likelihood estimation of the PIN model.

Usage

initials_pin_ea(data, xtraclusters = 4, verbose = TRUE)

Value

Returns a dataframe of initial sets each consisting of five variables {\(\alpha\), \(\delta\), \(\mu\), \(\epsilon\)_b, \(\epsilon\)_s}.

Arguments

data

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

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 information messages about the initial parameter sets, including the number of the initial parameter sets generated. No message is shown 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 function initials_pin_ea() uses a hierarchical agglomerative clustering (HAC) to find initial parameter sets for the maximum likelihood estimation. The steps in ErsanAlici2016;textualPINstimation algorithm differ from those used by Gan2015;textualPINstimation, and are summarized below.

Via the use of HAC, daily absolute order imbalances (AOIs) are grouped in 2+J (default J=4) clusters. After sorting the clusters based on AOIs, they are combined into two larger groups of days (event and no-event) by merging neighboring clusters with each other. Consequently, those groups are formed in #comb(5, 1) = 5 different ways. For each of the 5 configurations with which, days are grouped into two (event group and no-event group), the procedure below is applied to obtain initial parameter sets.

Days in the event group (the one with larger mean AOI) are distributed into two groups, i.e. good-event days (days with positive OI) and bad-event days (days with negative OI). Initial parameters are obtained from the frequencies, and average trade rates of three types of days. See ErsanAlici2016;textualPINstimation for further details.

The higher the number of the additional clusters (xtraclusters), the better is the estimation. ErsanAlici2016;textualPINstimation, however, have shown the benefit of increasing this number beyond 4 is marginal, and statistically insignificant.

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

# Obtain a dataframe of initial parameters for the maximum likelihood
# estimation using the algorithm of Ersan and Alici (2016).

init.sets <- initials_pin_ea(xdata)

# Use the obtained dataframe to estimate the PIN model using the function
# pin() with custom initial parameter sets

estimate.1 <- pin(xdata, initialsets = init.sets, verbose = FALSE)

# pin_ea() directly estimates the PIN model using initial parameter sets
# generated using the algorithm of Ersan & Alici (2016).

estimate.2 <- pin_ea(xdata, verbose = FALSE)

# Check that the obtained results are identical

show(estimate.1@parameters)
show(estimate.2@parameters)