Current events
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| + | '''Thursday, April 30, 2020, 1PM-2PM''' (Eastern US; GMT-4); [https://siam.zoom.us/webinar/register/WN_s8rIcHwiS-uPM3Dkuok-Wg Registration Link] | ||
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| + | ''Speaker:'' '''[https://sites.google.com/site/blankanorahorvath/home Blanka Horvath]''', Department of Mathematics, King's College London, UK | ||
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| + | [[Image:Horvath1.jpg]] | ||
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| + | ''Title:'' A Data-driven Market Simulator for Small Data Environments | ||
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| + | ''[https://siam.zoom.us/rec/share/yvJ_Buj25GNJcp3s9HqYfo1xRYHnX6a8hCAarvQJyU7fcgPdtmMS1Z04wH7LmW1f Recorded Video]'' | ||
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| + | ''Abstract:'' 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. | ||
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| + | ''Moderator:'' [http://www.columbia.edu/~ac3827/ Agostino Capponi], Department of Industrial Engineering and Operations Research, Columbia University | ||
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| '''Thursday, April 16, 2020, 1PM-2PM''' (Eastern US; GMT-4) | '''Thursday, April 16, 2020, 1PM-2PM''' (Eastern US; GMT-4) | ||
Revision as of 10:44, 6 June 2020
SIAG/FME virtual seminars series
The series of virtual talks, started by the SIAM Activity Group on Financial Mathematics and Engineering (SIAG/FME), aims at keeping the mathematical finance community connected worldwide beyond traditional formats. The goal is to host a diverse, across all dimensions, lineup of prominent speakers that will present the latest developments in the area of financial mathematics and engineering.
\diamond The talks will run every other week, and at least until the next SIAG/FME Biennial Meeting in June 2021
\diamond All talks will be delivered remotely using Zoom.
\diamond The talks are open to the public. Due to security reasons, all attendees have to register.
\diamond The registration link will be posted on this web-site, next to the each seminar date below. The detailed information about each talk, and the registration link will be also distributed via SIAG/FME Mailing List.
\diamond The registration is quick (asks only for your name and email), and once registered, you will receive an email with the link to the meeting(s), which is unique to you, so please do not share that email. The registration is usually valid for multiple future talks.
SIAG/FME Seminar Series Committee:
\quad Agostino Capponi (SIAG/FME Chair, Columbia University)
\quad Igor Cialenco (SIAG/FME Program Director, Illinois Institute of Technology)
\quad Sebastian Jaimungal (University of Toronto)
\quad Ronnie Sircar (Princeton University)
Forthcoming Talks
Thursday, May 14, 2020, 1PM-2:30PM (Eastern US; GMT-4);
Speaker: Bruno Dupire, Head of Quantitative Research, Bloomberg
Title: The Geometry of Money and the Perils of Parameterization
Abstract: TBA
Moderator: Ronnie Sircar, ORFE, Princeton University
Thursday, May 28, 2020, 1PM-2PM (Eastern US; GMT-4);
Speaker:
Title: TBA
Abstract: TBA
Moderator:
Thursday, June 11, 2020, 1PM-2PM (Eastern US; GMT-4);
Speaker:
Title: TBA
Abstract: TBA
Host and moderator:
Thursday, June 25, 2020, 1PM-2PM (Eastern US; GMT-4);
Speaker:
Title: TBA
Abstract: TBA
Moderator:
Thursday, July 09, 2020, 1PM-2PM (Eastern US; GMT-4);
Speaker:
Title: TBA
Abstract: TBA
Moderator:
Past Talks
Thursday, April 30, 2020, 1PM-2PM (Eastern US; GMT-4); Registration Link
Speaker: Blanka Horvath, Department of Mathematics, King's College London, UK
Title: A Data-driven Market Simulator for Small Data Environments
Abstract: 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.
Moderator: Agostino Capponi, Department of Industrial Engineering and Operations Research, Columbia University
Thursday, April 16, 2020, 1PM-2PM (Eastern US; GMT-4)
Speaker: Mete Soner, Department of Operations Research and Financial Engineering, Princeton University
Title: Trading with impact
Abstract: It is well known that large trades cause unfavorable price impact resulting in trading losses. These losses are particularly high when the underlying instrument is not liquid enough or when the trade size is large. Other type of market frictions such as transaction costs also cause similar effects. When one considers hedging or portfolio management or equilibrium models these effects must be taken into account. After describing widely used approaches of Cetin, Jarrow & Protter and Almgren & Chris, I first study the impact of resilience and then the structure of the optimal portfolios. This talk will be a summary of many results obtained jointly with many people including, Peter Bank, Bruno Bouchard, Umut Cetin, Ludovic Moreau, Johannes Muhle-Karbe, Nizar Touzi and Moritz Voss.
Moderator: Sebastian Jaimungal, Department of Statistical Sciences, University of Toronto
