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Activity Number: 663
Type: Contributed
Date/Time: Thursday, August 4, 2016 : 8:30 AM to 10:20 AM
Sponsor: Business and Economic Statistics Section
Abstract #318728 View Presentation
Title: Bayesian Estimate of PIN Model
Author(s): Yu HUAN* and Junni ZHANG and Mingjin WANG
Companies: Peking University and Peking University and Peking University
Keywords: Gibbs sampling ; Adaptive rejection sampling ; Financial market ; Market microstructure

The probability of informed trading (PIN), first proposed by Easley et al. (1996, Journal of Finance, 51, p1405-1436), is a commonly used direct measure of markets' information asymmetry risk in the microstructure and asset pricing literature. The popular MLE method often generates infeasible and biased estimator because of the complicated mixture Poisson form of the likelihood function. In this paper, a Bayesian approach, based on Gibbs sampling combined with adaptive rejection sampling algorithm, is proposed for the estimation of PIN model. Results in simulation analysis suggest that the Bayesian approach has lower RMSE than both the ordinary MLE and the improved MLE suggested recently by Easley et al. (2010, Journal of Financial and Quantitative Analysis, 45, p293-309). We apply our approach to data from China stock markets.

Authors who are presenting talks have a * after their name.

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