Generates random initial parameter sets to be used in the estimation of the
AdjPIN model of Duarte and Young (2009)
.
Usage
initials_adjpin_rnd(data, restricted = list(), num_init = 20,
verbose = TRUE)Arguments
- data
A dataframe with 2 variables: the first corresponds to buyer-initiated trades (buys), and the second corresponds to seller-initiated trades (sells).
- restricted
A binary list that allows estimating restricted AdjPIN models by specifying which model parameters are assumed to be equal. It contains one or multiple of the following four elements
{theta, mu, eps, d}. For instance, Ifthetais set toTRUE, then the probability of liquidity shock in no-information days, and in information days is assumed to be the same (\(\theta\)=\(\theta'\)). If any of the remaining rate elements{mu, eps, d}is set toTRUE, (saymu=TRUE), then the rate is assumed to be the same on the buy side, and on the sell side (\(\mu\)b=\(\mu\)s). If more than one element is set toTRUE, then the restrictions are combined. For instance, if the argumentrestrictedis set tolist(theta=TRUE, eps=TRUE, d=TRUE), then the restricted AdjPIN model is estimated, where \(\theta\)=\(\theta'\), \(\epsilon\)b=\(\epsilon\)s, and \(\Delta\)b=\(\Delta\)s. If the value of the argumentrestrictedis the empty list (list()), then all parameters of the model are assumed to be independent, and the unrestricted model is estimated. The default value is the empty listlist().- num_init
An integer corresponds to the number of initial parameter sets to be generated. The default value is
20.- 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
verboseis set toFALSE. The default value isTRUE.
Value
Returns a dataframe of numerical vectors of ten elements {\(\alpha\), \(\delta\), \(\theta\), \(\theta'\), \(\epsilon\)b, \(\epsilon\)s, \(\mu\)b, \(\mu\)s, \(\Delta\)b, \(\Delta\)s}.
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 buy rate parameters {\(\epsilon\)b, \(\mu\)b, \(\Delta\)b} are randomly generated
from the interval (minB, maxB), where minB (maxB) is the smallest
(largest) value of buys in the dataset, under the condition that
\(\epsilon\)b+\(\mu\)b+\(\Delta\)b< maxB. Analogously, the sell rate parameters
{\(\epsilon\)s, \(\mu\)s, \(\Delta\)s} are randomly generated from the interval (minS, maxS),
where minS (maxS) is the smallest(largest) value of sells in the
dataset, under the condition that \(\epsilon\)s+\(\mu\)s+\(\Delta\)s < maxS.
References
Duarte J, Young L (2009). “Why is PIN priced?” Journal of Financial Economics, 91(2), 119--138. ISSN 0304405X.
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 transactions ('B') and seller-initiated
# transactions ('S') on that day. To know more, type ?dailytrades
xdata <- dailytrades
# Obtain a dataframe of 40 random initial parameters for the MLE of
# the AdjPIN model using the initials_adjpin_rnd().
initial.sets <- initials_adjpin_rnd(xdata, num_init = 40)
#> The function initials_adjpin_rnd(...) has generated 40 initial parameter sets.
#>
To display the initial sets, store them in a variable or call (initials_adjpin_rnd(...)).
#>
To hide these messages, set the argument 'silent' to TRUE (silent = TRUE).
#>
# Use the dataframe to estimate the AdjPIN model using the adjpin()
# function.
estimate <- adjpin(xdata, initialsets = initial.sets, verbose = FALSE)
# Show the value of adjusted PIN
show(estimate@adjpin)
#> [1] 0.2951414