Seminar: Vladimir V. Piterbarg: Alternatives to Deep Neural Networks in Finance

PLEASE NOTE THAT THIS IN-PERSON EVENT WILL START ON WEDNESDAY, 13 SEPTEMBER, 2023, AT 6:30 PM BST (LONDON TIME) (1.30 PM EDT (NEW YORK TIME)) at G-Research offices.

This event is sponsored by G-Research (Silver Sponsor), First Derivatives plc (Bronze Sponsor) and KX, Inc. (Bronze Sponsor).

This event is hosted by G-Research, Europe's leading quantitative finance research firm: We hire the brightest minds in the world to tackle some of the biggest questions in finance. We pair this expertise with machine learning, big data, and some of the most advanced technology available to predict movements in financial markets.

Venue: G-Research, Whittington House, 19-30 Alfred Place, London, WC1E 7EA

Arrival time of 18:00 (London) and talk starts at 18:30 (London).

Seminar will last between 1 to 1.5 hours, followed by networking / food / drinks.

Please note that your Thalesians Meetup profile must include your full name in order to be admitted by the venue, G-Research (health and safety regulations). If it doesn't include it, please email it to alex@thalesians.com along with your profile name.

FULL TITLE: Alternatives to Deep Neural Networks in Finance

ABSTRACT

We develop two methods for approximating slow-to-calculate functions, and for conditional expected value calculations: the generalized stochastic sampling (gSS) and the functional tensor train (fTT) methods. We propose them as highly-performing alternatives to generic deep neural networks (DNNs) currently routinely recommended in derivatives pricing and other quantitative finance applications. The new methods not only outperform DNNs for typical financial problems but also, unlike DNNs, satisfy stringent finance requirements such as predictability and explainability.

BIOGRAPHY

Vladimir V. Piterbarg is Managing Director, Head of Quantitative Analytics and Quantitative Development at NatWest Markets and Visiting Professor in the Department of Mathematics at Imperial College London.

Previously he has served as Head of Quantitative Analytics at Rokos Family Office, Head of Quantitative Analytics at Barclays Capital, and Co-head of Quantitative Research at Bank of America.

He taught at the University of Chicago Mathematical Finance programme for a number of years and is a prolific and respected researcher in the area of interest rate modeling.

Vladimir V. Piterbarg won two Risk Magazine's Quant of the Year Awards (2006 and 2011), and holds a PhD in Mathematics (Probability Theory) from the University of Southern California. He serves as an associate editor of The Journal of Computational Finance and The Journal of Investment Strategies.