EA {InfoTrad} | R Documentation |
Ersan and Alici (2016) Cluster analysis with the altered steps.
Description
It estimates PIN using Ersan and Alici (2016) modified clustering algorithm.
Usage
EA(data, likelihood = c("LK", "EHO"))
## S3 method for class 'EA_class'
print(obj)
Arguments
data |
Data frame with 2 variables |
likelihood |
Character strings for likelihood algorithm. Default is "LK". |
obj |
object variable |
Details
Argument for data must be a data frame with 2 columns that only contain numbers. Not any other type. You do not have to give names to the columns. We will assign first one as "Buy" and second as "Sell", therefore you should put order numbers with respect to this order.
Value
Returns a list of parameter estimates (output)
alpha |
A Number |
delta |
A Number |
mu |
A Number |
eb |
A Number |
es |
A Number |
LikVal |
A Number |
PIN |
A Number |
Warning
This function does not handle NA values. Therefore the datasets should not contain any missing value. This function do not conduct the iterative estimation procedure proposed in the same paper.
Author(s)
Duygu Celik and Murat Tinic
References
Ersan, Oguz, and Asli Alici . "An unbiased computation methodology for estimating the probability of informed trading (PIN)." Journal of International Financial Markets, Institutions and Money 43 (2016): 74-94.
Examples
# Sample Data
# Buy Sell
#1 350 382
#2 250 500
#3 500 463
#4 552 550
#5 163 200
#6 345 323
#7 847 456
#8 923 342
#9 123 578
#10 349 455
Buy=c(350,250,500,552,163,345,847,923,123,349)
Sell=c(382,500,463,550,200,323,456,342,578,455)
data=cbind(Buy,Sell)
# Parameter estimates using the LK factorization of Lin and Ke (2011)
# with the modified clustering algorithm of Ersan and Alici (2016).
# Default factorization is set to be "LK"
result=EA(data)
print(result)
# Alpha: 0.9511418
# Delta: 0.2694005
# Mu: 76.7224
# Epsilon_b: 493.7045
# Epsilon_s: 377.4877
# Likelihood Value: 43973.71
# PIN: 0.07728924
# Parameter estimates using the EHO factorization of Easley et. al. (2010)
# with the modified clustering algorithm of Ersan and Alici (2016).
result=EA(data,likelihood="EHO")
print(result)
# Alpha: 0.9511418
# Delta: 0.2694005
# Mu: 76.7224
# Epsilon_b: 493.7045
# Epsilon_s: 377.4877
# Likelihood Value: 43973.71
# PIN: 0.07728924