Free Webinar: Blanka Horvath: A Data-driven Market Simulator for Small Data Environments
Webinar
Thursday, May 14, 2020 - 06:30 PM
Until: Thursday, May 14, 2020 - 08:30 PM
(Adjusted for timezone: Europe/London)
Categories
PLEASE NOTE THAT THIS WEBINAR WILL START ON THURSDAY, 14 MAY, 2020, AT 6:30 PM ***LONDON TIME*** (01.30 PM ***EDT***)
In this talk we investigate how Deep Hedging brings a new impetus into the modelling of financial markets. While a DNN-based data-driven market generation unveils a new and highly flexible way of modelling financial time series, it is by no means "model-free". In fact, the concrete modelling choice is decisive for the features of the resulting generative model. After a very short walk through historical market models we proceed to neural network based generative modelling approaches for financial time series. We then investigate some of the challenges to achieve good results in the latter, and highlight some applications and pitfalls. While most generative models tend to rely on large amounts of training data, we present here a parsimonious generative model that works reliably even in environments where the amount of available training data is notoriously small. Furthermore, we discuss how a rough paths perspective combined with a parsimonious Variational Autoencoder framework provides a powerful way for encoding and evaluating financial time series data in such environments. Lastly, we also discuss some pricing and hedging considerations in a DNN framework and their connection to Market Generation. The talk is based on joint work with H. Buehler, I. Perez Arribaz, T. Lyons and B. Wood.
BIOGRAPHY
Blanka Horvath is a Lecturer in Financial Mathematics at King's College London and a researcher of Machine Learning in Finance programme of The Alan Turing Institute, as well as a Honorary Lecturer at Imperial College London. In her latest research she focusses on non-Markovian models of financial markets such as Rough Volatility models as well as on DNN-based market generation. Her work on DNN-based calibration of Rough Volatility models was awarded the Rising Star Award 2020 of Risk magazine. Blanka holds a PhD in Financial Mathematics from ETH Zurich, a Diplom in Mathematics from the University of Bonn and an MSc in Economics from the University of Hong Kong. Prior to moving to King’s College, she worked at JP Morgan on the refinements of the Deep Hedging programme and was a Postdoctoral Fellow of the Swiss National Science Foundation at Imperial College London.
To register, visit https://www.meetup.com/thalesians/events/270243997/. Link to join visible to attendees.
In this talk we investigate how Deep Hedging brings a new impetus into the modelling of financial markets. While a DNN-based data-driven market generation unveils a new and highly flexible way of modelling financial time series, it is by no means "model-free". In fact, the concrete modelling choice is decisive for the features of the resulting generative model. After a very short walk through historical market models we proceed to neural network based generative modelling approaches for financial time series. We then investigate some of the challenges to achieve good results in the latter, and highlight some applications and pitfalls. While most generative models tend to rely on large amounts of training data, we present here a parsimonious generative model that works reliably even in environments where the amount of available training data is notoriously small. Furthermore, we discuss how a rough paths perspective combined with a parsimonious Variational Autoencoder framework provides a powerful way for encoding and evaluating financial time series data in such environments. Lastly, we also discuss some pricing and hedging considerations in a DNN framework and their connection to Market Generation. The talk is based on joint work with H. Buehler, I. Perez Arribaz, T. Lyons and B. Wood.
BIOGRAPHY
Blanka Horvath is a Lecturer in Financial Mathematics at King's College London and a researcher of Machine Learning in Finance programme of The Alan Turing Institute, as well as a Honorary Lecturer at Imperial College London. In her latest research she focusses on non-Markovian models of financial markets such as Rough Volatility models as well as on DNN-based market generation. Her work on DNN-based calibration of Rough Volatility models was awarded the Rising Star Award 2020 of Risk magazine. Blanka holds a PhD in Financial Mathematics from ETH Zurich, a Diplom in Mathematics from the University of Bonn and an MSc in Economics from the University of Hong Kong. Prior to moving to King’s College, she worked at JP Morgan on the refinements of the Deep Hedging programme and was a Postdoctoral Fellow of the Swiss National Science Foundation at Imperial College London.
To register, visit https://www.meetup.com/thalesians/events/270243997/. Link to join visible to attendees.