ML@GT will host a virtual seminar featuring Qi Wei, Vice President and ML/AI Lead at JP Morgan Chase.
Registration is required. Register here.
Generative models based on point processes for financial time series simulation
In this seminar, I will talk about generative models based on point processes for financial time series simulation. Specifically, we focus on a recently developed state-dependent Hawkes (sdHawkes) process to model the limit order book dynamics [Morariu-Patrichi, 2018]. The sdHawkes model consists of an oracle Hawkes process and a state process following Markov transition. The Hawkes and state processes are fully coupled, which enables the point process captures the self-and cross-excitation as well as the interaction between events and states. We will go through the model formulation in sdHawkes, the simulation of sdHawkes, its maximum likelihood estimation, and more importantly, its application to high-frequency data modeling that captures the interactions between the order flow and the state of the current market.
Morariu-Patrichi, Maxime, and Mikko S. Pakkanen. "State-dependent Hawkes processes and their application to limit order book modelling." arXiv preprint arXiv:1809.08060 (2018).
Qi Wei received his Ph.D. degree in machine learning and image processing from the National Polytechnic Institute of Toulouse (INPENSEEIHT), University of Toulouse, France in September 2015, and Bachelor degree in Electrical Engineering from Beihang University (BUAA), Beijing, China in July 2010. Wei's doctoral thesis Bayesian Fusion of Multi-band Images: A Powerful Tool for Super-resolution was rated as one of the best theses (awarded Prix Leopold Escande) at the University of Toulouse, 2015.
Wei has worked on multiband image processing as a Research Associate with Signal Processing Laboratory, University of Cambridge, UK, and as a Research Associate at Duke University, US. He has also worked at Siemens Corporate Technology as a Research Scientist. Since 2018, Wei served as a vice president and machine learning scientist at JPMorgan. His research has been focused on machine/deep learning, time series analysis, computer vision/image processing, Bayesian statistical inference, etc.