Quarterly PIN estimates
Estimation of model parameters and probability of informed trading for quarterly data.
numeric vector of daily buys
numeric vector of daily sells
numeric lower bounds for optimization, must have length of 5
numeric upper bounds for optimization, must have length of 5
logical Compute confidence intervals for PIN?
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.
numsells need to have same length.
Calculation of confidence interval for the probability of informed trading is disabled by default. For more details see
A list of lists. 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:
Matrix containing the parameter estimates as well as their estimated standard errors, t-values and p-values.
Value of likelihood function returned by
Value(s) of the 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
confint = TRUE; confidence interval for the probability of informed trading
Easley, David et al. (2002)
Is Information Risk a Determinant of Asset Returns?
The Journal of Finance, Volume 57, Number 5, pp. 2185 - 2221
Easley, David et al. (1996)
Liquidity, Information, and Infrequently Traded Stocks
The Journal of Finance, Volume 51, Number 4, pp. 1405 - 1436
Easley, David et al. (2010)
Factoring Information into Returns
Journal of Financial and Quantitative Analysis, Volume 45, Issue 2, pp. 293 - 309
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
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
Grolemund, Garett and Wickham, Hadley (2011)
Dates and Times Made Easy with lubridate
Journal of Statistical Software, Volume 40, Issue 3, pp. 1 - 25
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
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
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# 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"))
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