A faster estimation method for the probability of informed trading using hierarchical agglomerative clustering

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 [J. Bank. Finance, 2012, 36, 454-467...

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Bibliographic Details
Published inQuantitative finance Vol. 15; no. 11; pp. 1805 - 1821
Main Authors Gan, Quan, Wei, Wang Chun, Johnstone, David
Format Journal Article
LanguageEnglish
Published Bristol Routledge 02.11.2015
Taylor & Francis Ltd
Subjects
Cluster analysis
Estimating techniques
Market microstructure
Maximum likelihood method
Mixture model
PIN
Securities trading
Skellam distribution
Studies
Online AccessGet full text
ISSN1469-7688
1469-7696
DOI10.1080/14697688.2015.1023336

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Summary: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 [J. Bank. Finance, 2012, 36, 454-467] 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.
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ISSN:1469-7688
1469-7696
DOI:10.1080/14697688.2015.1023336