Core function for optimization routines
1 2 3 4 
numbuys 
numeric: vector of daily buys 
numsells 
numeric: vector of daily sells 
factorization 
character: factorization of likelihood function: either 'EHO' or 'Lin_Ke', defaults to: 'Lin_Ke' 
init_vals 
numeric: matrix of initial values: either generated by 
lower 
numeric: lower bounds for optimization, must have length of 5 
upper 
numeric: upper bounds for optimization, must have length of 5 
num_best_res 
Number of optimization runs for which results should be returned, either numeric or 'all',
only relevant if 
only_converged 
logical: Return only results for which the likelihood converged?
Defaults to 
confint 
logical: Compute confidence intervals for PIN?
Defaults to 
ci_control 
list: see Details 
Grants the most control over optimization procedure.
Userfriendly wrappers for estimation with trading data of arbitrary length and quarterly data
are implemented with pin_est
and qpin
, respectively.
nlminb
function in the stats package is used for maximization.
Vectors for numbuys
and numsells
need to have same length.
Confidence intervals for the probability of informed trading are calculated via MonteCarloSimulation
if confint = TRUE
. Settings of the confidence interval simulation can be specified via a named list for ci_control
.
Valid list names are n
, seed
, level
and ncores
which pass
number of simulation runs (defaults to 10000), seed for RNG (defaults to seed = NULL
),
confidence level (defaults to 0.95) and number of cpu cores utilized (defaults to 1).
If num_best_res
= 1, a list with following elements is returned:
Matrix containing the parameter estimates as well as their estimated standard errors, tvalues and pvalues.
Value of likelihood function returned by nlminb
Estimated probability of informed trading
Convergence code for nlminb optimization
Convergence message returned by the nlminb optimizer
Number of iterations until convergence of nlminb optimizer
Vector of initial values
If confint = TRUE
; confidence interval for the probability of informed trading
If num_best_res
> 1, a named list of lists is returned. Each component of the outer list
is again a list structured as shown above.
Naming scheme for the outer list is 'Best1',..., 'Bestnum_best_res
'.
Easley, David et al. (2002)
Is Information Risk a Determinant of Asset Returns?
The Journal of Finance, Volume 57, Number 5, pp. 2185  2221
doi: 10.1111/15406261.00493
Easley, David et al. (1996)
Liquidity, Information, and Infrequently Traded Stocks
The Journal of Finance, Volume 51, Number 4, pp. 1405  1436
doi: 10.1111/j.15406261.1996.tb04074.x
Easley, David et al. (2010)
Factoring Information into Returns
Journal of Financial and Quantitative Analysis, Volume 45, Issue 2, pp. 293  309
doi: 10.1017/S0022109010000074
Ersan, Oguz and Alici, Asli (2016)
An unbiased computation methodology for estimating the probability of informed trading (PIN)
Journal of International Financial Markets, Institutions and Money, Volume 43, pp. 74  94
doi: 10.1016/j.intfin.2016.04.001
Gan, Quan et al. (2015)
A faster estimation method for the probability of informed trading
using hierarchical agglomerative clustering
Quantitative Finance, Volume 15, Issue 11, pp. 1805  1821
doi: 10.1080/14697688.2015.1023336
Lin, HsiouWei William and Ke, WenChyan (2011)
A computing bias in estimating the probability of informed trading
Journal of Financial Markets, Volume 14, Issue 4, pp. 625  640
doi: 10.1016/j.finmar.2011.03.001
Revolution Analytics and Steve Weston (2015)
doParallel: Foreach Parallel Adaptor for the 'parallel' Package
R package version 1.0.10
Revolution Analytics and Steve Weston (2015)
foreach: Provides Foreach Looping Construct for R
R package version 1.4.3
Yan, Yuxing and Zhang, Shaojun (2012)
An improved estimation method and empirical properties of the probability of informed trading
Journal of Banking & Finance, Volume 36, Issue 2, pp. 454  467
doi: 10.1016/j.jbankfin.2011.08.003
nlminb
,
initial_vals
pin_est
qpin
pin_confint
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  # Loading simulated data for frequently traded stock
data("BSfrequent")
# Generate several matrices of initial values utilizing all methods implemented
inits_grid < initial_vals(numbuys = BSfrequent[,"Buys"],
numsells = BSfrequent[,"Sells"],
method = "Grid")
inits_hac < initial_vals(numbuys = BSfrequent[,"Buys"],
numsells = BSfrequent[,"Sells"],
method = "HAC")
inits_hac_ref < initial_vals(numbuys = BSfrequent[,"Buys"],
numsells = BSfrequent[,"Sells"],
method = "HAC_Ref")
# Optimization with different matrices of initial values
pin_core_grid < pin_est_core(numbuys = BSfrequent[,"Buys"],
numsells = BSfrequent[,"Sells"],
factorization = "Lin_Ke", init_vals = inits_grid,
lower = rep(0,5), upper = c(1,1, rep(Inf,3)),
num_best_res = 5)
pin_core_hac < pin_est_core(numbuys = BSfrequent[,"Buys"],
numsells = BSfrequent[,"Sells"],
factorization = "Lin_Ke", init_vals = inits_hac,
lower = rep(0,5), upper = c(1,1, rep(Inf,3)))
pin_core_hac_ref < pin_est_core(numbuys = BSfrequent[,"Buys"],
numsells = BSfrequent[,"Sells"],
factorization = "Lin_Ke", init_vals = inits_hac_ref,
lower = rep(0,5), upper = c(1,1, rep(Inf,3)))
## Not run:
pin_core_hac < pin_est_core(numbuys = BSfrequent[,"Buys"],
numsells = BSfrequent[,"Sells"],
factorization = "Lin_Ke", init_vals = inits_hac,
lower = rep(0,5), upper = c(1,1, rep(Inf,3)),
confint = TRUE)
## End(Not run)

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