Loading...
Financial Engineering Workshop - Matthew Dixon (Illinois Institute of Technology)
Registration
Registration is now closed (this event already took place).
Details
Abstract
We present a finite dimensional Gaussian process (GP) regression for no-arbitrage interpolation and modelling the local volatility surface. In this setup, the MAP estimate serves the purpose of identifying the locations of the most likely arbitrages in the data and quantifying them. Furthermore, Hamiltonian Monte Carlo can be used to efficiently sample from the posterior price surface and provide UQ of the local volatility surface. We demonstrate the performance of this approach relative to SSVI and a NN approach on SPX options, and in particular the importance of embedding no-arbitrage constraints into the ML for 10x sharper uncertainty quantification where data coverage in the option surface is poor. This is joint work with Stéphane Crépey, Areski Cousin, and Marc Chataigner.
Bio
Matthew Dixon is a British applied mathematician working in the area of algorithmic finance. His research focuses on applying concepts in computational and applied mathematics to financial modeling, especially in the area of algorithmic trading and derivatives. Matthew's research is currently funded by Intel Corporation and he develops codes for high performance architectures. His work in deep learning with Diego Klabjan (NWU) has brought wide recognition and he is a frequently invited speaker at quant and fintech events around the world in addition to be referenced as a computational finance expert in multiple reputed media outlets including the Financial Times and Bloomberg Markets.
The Financial Engineering Workshop will take place online via Microsoft Teams.
For the link to the online seminar please register before 2pm on the day of the seminar otherwise you will not be given access.