In pinbasic: Fast and Stable Estimation of the Probability of Informed Trading (PIN)

Initial Values

The previous sections give options to evaluate likelihood functions in the EKOP and EHO model in a stable and effective way. However, we did not have to care about initial values for the evaluations beforehand because we knew the data generating process.

There is not one rule or algorithm delivering the best starting values which ensure the maximization to end in the global maximum for every optimization run. One possibility would be to perform an appropriate number of maximization runs with different sets of random starting values. This procedure can be cumbersome because it is unclear how many runs are really needed to reach the global maximum, instead of landing in one of possibly several local maxima. Furthermore, running several hundred or even thousands of optimizations can be very time-consuming.

The next sections discuss three methods which aim to solve the problem of suitable initial values. Earlier, we demonstrated that there is practically no difference in terms of execution time between the simple and extended static model. Hence, for the remainder of this chapter we will concentrate on the EHO setup.

pinbasic documentation built on May 2, 2019, 2:07 a.m.