A methodological approach to the computational problems in the estimation of adjusted PIN model

23 Pages Posted: 2 Jun 2022

See all articles by Oguz Ersan

Oguz Ersan

Kadir Has University, International Trade and Finance, Faculty of Economics, Administrative and Social Sciences

Montasser Ghachem

Stockholm University - Department of Economics

Multiple version iconThere are 2 versions of this paper

Date Written: May 23, 2022

Abstract

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 and 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. We show that the introduced method outperforms existing best-practices and strongly suggest its use in further studies.

Keywords: Multilayer probability of informed trading, MPIN, layer detection algorithm, cluster analysis, information asymmetry, private information.

JEL Classification: C13, C38, G14, G17

Suggested Citation

Ersan, Oguz and Ghachem, Montasser, A methodological approach to the computational problems in the estimation of adjusted PIN model (May 23, 2022). Available at SSRN: https://ssrn.com/abstract=4117954 or http://dx.doi.org/10.2139/ssrn.4117954

Oguz Ersan (Contact Author)

Kadir Has University, International Trade and Finance, Faculty of Economics, Administrative and Social Sciences ( email )

Cibali Mah., Fatih
Istanbul, 34083
Turkey

Montasser Ghachem

Stockholm University - Department of Economics ( email )

Universitetsvägen 10 A
House A, floor 4 and 7
Frescati, Stockholm
Sweden

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