Estimates the Probability of Informed Trading (PIN) using the
initial sets from the algorithm in
Ersan and Alici (2016)
.
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 Ersan and Alici (2016) . The default value is4.- 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
verboseis set toFALSE. The default value isTRUE.
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 Easley et al. (2010)"LK"refers to the factorization in Lin and Ke (2011)"E"refers to the factorization in Ersan (2016)"NONE"refers to the original likelihood function - with no factorization
The function pin_ea() implements the algorithm detailed in
Ersan and Alici (2016)
.
The higher the number of the additional layers (xtraclusters), the
better is the estimation. Ersan and Alici (2016)
,
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
Ersan and Alici (2016)
algorithm.
For further information on the initial parameter set determination, see
initials_pin_ea().
References
Easley D, Hvidkjaer S, Ohara M (2010).
“Factoring information into returns.”
Journal of Financial and Quantitative Analysis, 45(2), 293--309.
ISSN 00221090.
Ersan O (2016).
“Multilayer Probability of Informed Trading.”
Available at SSRN 2874420.
Ersan O, Alici A (2016).
“An unbiased computation methodology for estimating the probability of informed trading (PIN).”
Journal of International Financial Markets, Institutions and Money, 43, 74--94.
ISSN 10424431.
Lin H, Ke W (2011).
“A computing bias in estimating the probability of informed trading.”
Journal of Financial Markets, 14(4), 625-640.
ISSN 1386-4181.
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)
#> [1] 0.5661721
# Display the estimated parameters
show(estimate@parameters)
#> alpha delta mu eps.b eps.s
#> 0.7499975 0.1333342 1193.5179655 357.2659099 328.6291793
# 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))
#> alpha delta mu eps.b eps.s
#> 1 0.7500000 0.13333333 1213.3422 336.1429 336.1852
#> 2 0.6333333 0.10526316 1262.0469 446.8846 356.4107
#> 3 0.5333333 0.09375000 1329.2577 531.6774 367.4561
#> 4 0.4833333 0.03448276 1364.5773 556.6875 399.6780
#> 5 0.1000000 0.00000000 928.7648 1076.1852 423.2833