Title:
Generative models based on point processes for financial time series simulation
Generative models based on point processes for financial time series simulation
| dc.contributor.author | Wei, Qi | |
| dc.contributor.corporatename | Georgia Institute of Technology. Machine Learning | en_US |
| dc.contributor.corporatename | J.P. Morgan Chase & Co. | en_US |
| dc.date.accessioned | 2021-04-22T18:06:42Z | |
| dc.date.available | 2021-04-22T18:06:42Z | |
| dc.date.issued | 2021-04-07 | |
| dc.description | Presented online on April 7, 2021 at 12:15 p.m. | en_US |
| dc.description | Qi 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. | |
| dc.description | Runtime: 61:45 minutes | |
| dc.description.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). | en_US |
| dc.format.extent | 61:45 minutes | |
| dc.identifier.uri | http://hdl.handle.net/1853/64454 | |
| dc.language.iso | en_US | en_US |
| dc.relation.ispartofseries | Machine Learning @ Georgia Tech (ML@GT) Seminar Series | |
| dc.subject | Generative model | en_US |
| dc.subject | Point process | en_US |
| dc.subject | State dependence | en_US |
| dc.subject | Time series | en_US |
| dc.title | Generative models based on point processes for financial time series simulation | en_US |
| dc.type | Moving Image | |
| dc.type.genre | Lecture | |
| dspace.entity.type | Publication | |
| local.contributor.corporatename | Machine Learning Center | |
| local.contributor.corporatename | College of Computing | |
| local.relation.ispartofseries | ML@GT Seminar Series | |
| relation.isOrgUnitOfPublication | 46450b94-7ae8-4849-a910-5ae38611c691 | |
| relation.isOrgUnitOfPublication | c8892b3c-8db6-4b7b-a33a-1b67f7db2021 | |
| relation.isSeriesOfPublication | 9fb2e77c-08ff-46d7-b903-747cf7406244 |
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