Test of Pin Algorithms Through Simulation
16 Pages Posted: 8 Oct 2015
Date Written: October 7, 2015
Abstract
I use simulated series of buys and sells to test nine different PIN estimation algorithms implemented in SAS, proc NLP. I conclude that the algorithms do a good job estimating PIN, on average, when applied to series generated from high-PIN data generating process (PIN>0.2). However, when applied to series generated from zero-PIN or low-PIN (PIN<0.2) data generation process, the algorithms yield overstated PIN estimates. Supplying initial values to parameters in the estimation has strong effect on the estimated PIN. Factorization of the likelihood function plays a less important role. Not supplying initial values dramatically overstates PINs for zero-PIN and low-PIN data: zero-PIN data shows PIN as high as 0.26; low-PIN data tend to have PINs overstated by 0.05 to 0.10. Supplying initial values as in Lin and Ke (2011) eliminates the bias for low-PIN data but not for zero-PIN data. Supplying initial values which correspond to the null hypothesis (of no informed trading) yields zero PIN estimates for low-Pin as well as high-PIN data.
Keywords: PIN, probability of informed trading, market microstructure, simulation
JEL Classification: G12, G14, C13, C63
Suggested Citation: Suggested Citation