In market microstructure literature, informed trading is a phenomenon where traders with private information trade profitably against market makers who then provide liquidity at a loss. Developed by Easley et al. (1996), the Probability of Informed Trading (PIN) is a widely used measure of informed trading due to its explanatory ability for various market states such as spreads and volatility. With the increasing prevalence of high frequency trading (HFT), the PIN measure was updated to a volume-based measure called Volume-synchronized Probability of Informed Trading (VPIN). Despite their widespread applications, both the PIN and VPIN measure have been scrutinised for their theoretical and computational problems. This thesis addresses several of these issues and makes two contributions....[ Read more ]

In market microstructure literature, informed trading is a phenomenon where traders with private information trade profitably against market makers who then provide liquidity at a loss. Developed by Easley et al. (1996), the Probability of Informed Trading (PIN) is a widely used measure of informed trading due to its explanatory ability for various market states such as spreads and volatility. With the increasing prevalence of high frequency trading (HFT), the PIN measure was updated to a volume-based measure called Volume-synchronized Probability of Informed Trading (VPIN). Despite their widespread applications, both the PIN and VPIN measure have been scrutinised for their theoretical and computational problems. This thesis addresses several of these issues and makes two contributions. In particular, we use the Geometric Poisson distribution in place of the Poisson distribution to model trade size endogenously, and we develop a Bayesian-based approach to detect periods where information events occurred. Doing so is integral to making accurate estimates of the key parameters in PIN. Our findings also provide insights on the usefulness of the parameters estimated.