A Methodological Approach to the Computational Problems in the Estimation of Adjusted Pin Model
It is well documented that computational problems may lead to large biases in the estimation of probability of informed trading (PIN) models. While effective remedial solutions have been suggested for the case of original PIN model (Easley et al., 1996), computational problems for its most broadly applied extension, the adjusted PIN model of Duarte & Young (2009), are yet to be addressed. Given its larger parameter set, estimates of the AdjPIN model are more likely to suffer from computational problems. We address these computational problems by developing an estimation method comprising a) a logarithmic factorization of the likelihood function, and b) an algorithm to strategically generate initial parameter sets. Given the widespread use of the AdjPIN model, and the largely superior accuracy of our suggested methods over existing best-practices, the use of those methods in further studies is likely to become a prerequisite to obtain replicable, accurate, and unbiased estimates
Year of publication: |
2023
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Authors: | Ersan, Oguz ; Ghachem, Montasser |
Publisher: |
[S.l.] : SSRN |
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