Description Usage Arguments Details Value Warning Author(s) References Examples
It estimates PIN using hierarchical agglomertaive clustering.
1 2 3 |
data |
Data frame with 2 variables |
likelihood |
Character strings for likelihood algorithm. Default is "LK". |
obj |
object variable |
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. This package uses the hclust() function of Mullner (2013) to cluster the data at default settings.
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 |
This function does not handle NA values. Therefore, the dataset should not contain any missing values.
Duygu Celik and Murat Tinic
D. Mullner. fastcluster: Fast hierarchical, agglomerative clustering routines for r and python. Journal of Statistical Software, 53(9):1-18, 2013.
Gan, Q., Wei, W. C., & Johnstone, D. A faster estimation method for the probability of informed trading using hierarchical agglomerative clustering. Quantitative Finance, 15(11), 1805-1821, 2015.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 | # 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 algorithm of Gan et. al. (2015).
# Default factorization is set to be "LK"
result=GAN(data)
print(result)
# Alpha: 0.3999998
# Delta: 0
# Mu: 442.1667
# Epsilon_b: 263.3333
# Epsilon_s: 424.9
# Likelihood Value: 44371.84
# PIN: 0.2044464
# Parameter estimates using the EHO factorization of Easley et. al. (2010)
# with the algorithm of Gan et. al. (2015)
result=GAN(data, likelihood="EHO")
print(result)
# Alpha: 0.3230001
# Delta: 0.4780001
# Mu: 481.3526
# Epsilon_b: 356.6359
# Epsilon_s: 313.136
# Likelihood Value: Inf
# PIN: 0.1884001
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.