pinbasic (version 1.2.2)

qpin: Quarterly PIN estimates

Description

Estimation of model parameters and probability of informed trading for quarterly data.

Usage

qpin(numbuys = NULL, numsells = NULL, dates = NULL,
  nlminb_control = list(), confint = FALSE, ci_control = list(),
  posterior = TRUE)

Arguments

numbuys

numeric: vector of daily buys

numsells

numeric: vector of daily sells

dates

see Details

nlminb_control

list: Control list for nlminb

confint

logical: Compute confidence intervals for PIN? Defaults to FALSE

ci_control

list: see pin_est_core

posterior

logical: Should posterior probabilities for conditions of trading days be computed?

Value

A list of lists with class 'qpin'. The length of the outer list equals the number of available quarters in the data. Naming scheme for the outer list is 'Year.QuarterNumber', where QuarterNumber equals an integer from 1 to 4. The inner list is structured as follows:

Results

Matrix containing the parameter estimates as well as their estimated standard errors, t-values and p-values.

ll

Value of likelihood function returned by nlminb

pin

Value(s) of the estimated probability of informed trading

conv

Convergence code for nlminb optimization

message

Convergence message returned by the nlminb optimizer

iterations

Number of iterations until convergence of nlminb optimizer

init_vals

Vector of initial values

confint

If confint = TRUE; confidence interval for the probability of informed trading

Details

Wrapper around pin_est function and therefore inherits its settings for optimization. Data is split into quarters with the quarter function from lubridate package. According to the help page of this function dates argument must be a date-time object of class POSIXct, POSIXlt, Date, chron, yearmon, yearqtr, zoo, zooreg, timeDate, xts, its, ti, jul, timeSeries, fts or anything else that can be converted with as.POSIXlt. nlminb function in the stats package is used for maximization. Vectors for numbuys and numsells need to have same length. Calculation of confidence interval for the probability of informed trading is disabled by default. For more details see pin_est_core or pin_confint.

References

Easley, David et al. (2002) Is Information Risk a Determinant of Asset Returns? The Journal of Finance, Volume 57, Number 5, pp. 2185 - 2221 10.1111/1540-6261.00493

Easley, David et al. (1996) Liquidity, Information, and Infrequently Traded Stocks The Journal of Finance, Volume 51, Number 4, pp. 1405 - 1436 10.1111/j.1540-6261.1996.tb04074.x

Easley, David et al. (2010) Factoring Information into Returns Journal of Financial and Quantitative Analysis, Volume 45, Issue 2, pp. 293 - 309 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 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 10.1080/14697688.2015.1023336

Grolemund, Garett and Wickham, Hadley (2011) Dates and Times Made Easy with lubridate Journal of Statistical Software, Volume 40, Issue 3, pp. 1 - 25 10.18637/jss.v040.i03

Lin, Hsiou-Wei William and Ke, Wen-Chyan (2011) A computing bias in estimating the probability of informed trading Journal of Financial Markets, Volume 14, Issue 4, pp. 625 - 640 10.1016/j.finmar.2011.03.001

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 10.1016/j.jbankfin.2011.08.003

See Also

nlminb, initial_vals pin_est pin_est_core pin_confint quarter year

Examples

# NOT RUN {
# Loading one year of simulated daily buys and sells

data('BSfrequent2015')

# Quarterly estimates for model parameters and the probability of informed trading
# Rownames of 'BSfrequent2015' equal the business days in 2015.

qpin2015 <- qpin(numbuys = BSfrequent2015[,"Buys"],
                 numsells = BSfrequent2015[,"Sells"],
                 dates = as.Date(rownames(BSfrequent2015), format = "%Y-%m-%d"))
# }