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Qi Wei, a vice president and machine learning scientist at JP Morgan Chase will give a virtual seminar on Wednesday, April 7 at 12:15 p.m. ET. This event is open to all Georgia Tech students, faculty, staff, and interested members of the public.
Register Here
TALK TITLE
Generative models based on point processes for financial time series simulation
ABSTRACT
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).
ABOUT QI
Qi Wei received his Ph.D. in machine learning and image processing from the National Polytechnic Institute of Toulouse (INP-ENSEEIHT), University of Toulouse, France in September 2015 and a bachelor's degree in electrical engineering from Beihang University (BUAA), Bejing, China in July 2010. His 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 in 2015.
Qi worked on multi-band image processing as a research associate with Signal Process Laboratory at the University of Cambridge. Qi has also worked as a research associate at Duke University, research scientist at Siemens Corporate Technology. Since 2018, he has served as a vice president and machine learning scientist at JP Morgan Chase. His research is focused on machine/deep learning, time series analysis, computer vision/image processing, and Bayesian statistical inference.
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