This project explores to what extent the estimations of Easley et. al.’s (1996) probability of informed trading (PIN) and of Duarte and Young’s (2009) adjusted probability of informed trading (AdjPIN) can be improved in three ways. The current challenges faced by the estimation of probability of informed trading include the difficulty caused by too many parameters in the model setting, and the frequent floating point underflows in computing Pois-son mass functions due to high frequency trading. The methods proposed by this project to improve the efficiency of estimation are as follow. Firstly, this project would like to explore whether the expectation-maximization (EM) algorithm performs better than the traditional maximum likelihood method in terms of efficiency and precision, especially when the closed-form solutions can be obtained in the EM’s maximization step. Secondly, this project would like to compare the performance of the estimations when the initial values are given by Yan and Zhang’s (2012) method and by extending the idea of Finch et al. (1989). Finally, this project would like to investigate the possibilities of replacing Poisson with normal distributions to estimate the PIN and AdjPIN models.
|Effective start/end date||1/08/16 → 31/07/17|
UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This project contributes towards the following SDG(s):
- Probability of informed trading
- Information asymmetry
- mix-ture models
- expectation-maximization algorithm
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