Estimation of the Probability of Informed Trading Models Via an Expectation-Conditional Maximization Algorithm

30 Pages Posted: 8 Jun 2022

See all articles by Montasser Ghachem

Montasser Ghachem

Stockholm University - Department of Economics

Oguz Ersan

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

Multiple version iconThere are 2 versions of this paper

Date Written: May 23, 2022

Abstract

The PIN model and its extensions have proven challenging in their estimation, as they suffer from several computational problems. We set in this paper to address these computational issues by proposing the use of the expectation-conditional maximization (ECM) algorithm to estimate the various models of probability of informed trading. In particular, we derive optimal estimates of two of the extensions of the original PIN model, which are the MPIN model as introduced by Ersan (2016), and the adjusted PIN of Duarte and Young (2009)), as well as its restricted variants. The derivation provides a reliable and mathematically sound method for the estimation of the number of information layers for the MPIN model, as well as, stable estimates for the adjusted PIN model despite the large number of free variables. We show that the maximum likelihood estimation via the ECM algorithm is faster, and more reliable, and provides a viable alternative to the standard methods used in the literature. In addition to providing more accurate estimates of probability parameters, the ECM algorithm allows for an endogenous determination of the number of layers in the MPIN model. This paper has served as the basis of the implementation of the ECM estimation in the R package dedicated to the estimation of probability of informed trading models: PINstimation.

Keywords: Multilayer probability of informed trading, MPIN, adjusted PIN model, expectation conditional maximization, information asymmetry, private information

JEL Classification: C13, C38, G14, G17

Suggested Citation

Ghachem, Montasser and Ersan, Oguz, Estimation of the Probability of Informed Trading Models Via an Expectation-Conditional Maximization Algorithm (May 23, 2022). Available at SSRN: https://ssrn.com/abstract=4117952 or http://dx.doi.org/10.2139/ssrn.4117952

Montasser Ghachem (Contact Author)

Stockholm University - Department of Economics ( email )

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

Oguz Ersan

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

Cibali Mah., Fatih
Istanbul, 34083
Turkey

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