A Faster Estimation Method for the Probability of Informed Trading Using Hierarchical Agglomerative Clustering

Forthcoming in Quantitative Finance

33 Pages Posted: 5 Dec 2013 Last revised: 17 Feb 2015

See all articles by Quan Gan

Quan Gan

The University of Sydney - Discipline of Finance; Financial Research Network (FIRN)

Wang Chun Wei

University of Queensland - Faculty of Business, Economics and Law; University of Queensland - Finance

David Johnstone

University of Sydney Business School; Financial Research Network (FIRN)

Date Written: February 15, 2015

Abstract

The probability of informed trading (PIN) is a commonly used market microstructure measure for detecting the level of information asymmetry. Estimating PIN can be problematic due to corner solutions, local maxima and floating point exceptions (FPE). Yan and Zhang (2012) show that whilst factorization can solve FPE, boundary solutions appear frequently in maximum likelihood estimation for PIN. A grid search initial value algorithm is suggested to overcome this problem. We present a faster method for reducing the likelihood of boundary solutions and local maxima based on hierarchical agglomerative clustering (HAC). We show that HAC can be used to determine an accurate and fast starting value approximation for PIN. This assists the maximum likelihood estimation process in both speed and accuracy.

Keywords: PIN, CPIN, Market microstructure, Cluster analysis, Mixture model, Skellam distribution

JEL Classification: C13, C38, C46, D53, G10, G12, G14

Suggested Citation

Gan, Quan and Wei, Wang Chun and Johnstone, David, A Faster Estimation Method for the Probability of Informed Trading Using Hierarchical Agglomerative Clustering (February 15, 2015). Forthcoming in Quantitative Finance. Available at SSRN: https://ssrn.com/abstract=2363814 or http://dx.doi.org/10.2139/ssrn.2363814

Quan Gan

The University of Sydney - Discipline of Finance ( email )

Discipline of Finance
University of Sydney
Sydney, NSW 2006
Australia

HOME PAGE: http://sydney.edu.au/business/staff/quang

Financial Research Network (FIRN)

C/- University of Queensland Business School
St Lucia, 4071 Brisbane
Queensland
Australia

HOME PAGE: http://www.firn.org.au

Wang Chun Wei (Contact Author)

University of Queensland - Faculty of Business, Economics and Law ( email )

4072 Brisbane, Queensland
Australia

University of Queensland - Finance ( email )

Australia

David Johnstone

University of Sydney Business School ( email )

Instute of Transport and Logistics Studies (C37)
The University of Sydney
Sydney, NSW 2133
Australia

Financial Research Network (FIRN)

C/- University of Queensland Business School
St Lucia, 4071 Brisbane
Queensland
Australia

HOME PAGE: http://www.firn.org.au

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