Wednesday 24th March 2021
Lecturer in Financial Mathematics, Queen Mary University of London
Kathrin Glau: Lecturer in Financial Mathematics, Queen Mary University of London
Kathrin Glau currently is a Lecturer in Financial Mathematics at Queen Mary University of London & FELLOW co-founded by Marie Skłodowska Curie at École Polytechnique Fédérale de Lausanne. Between 2011 and 2017 she was Junior Professor at the Technical University of Munich. Prior to this she worked as a postdoctoral university assistant at the chair of Prof. Walter Schachermayer at the University of Vienna. In September 2010 she completed her Ph.D. on the topic of Feynman-Kac representations for option pricing in Lévy models at the chair of Ernst Eberlein.
Her research is driven by the interdisciplinary nature of computational finance and reaches across the borders of finance, stochastic analysis and numerical analysis. At the core of her current research is the design and implementation of complexity reduction techniques for finance. Key to her approach is the decomposition of algorithms in an offline phase, which is a learning step, and a fast and accurate online phase. The methods range from model order reduction of parametric partial differential equations to learning algorithms and are designed to facilitate such diverse tasks as uncertainty quantification and calibration, real-time pricing, real-time risk monitoring, and intra-day stress testing.
Many pricing problems boil down to the computation of a high dimensional integral, which is usually estimated using Monte Carlo. The convergence of this algorithm can be relatively slow depending on the variance of the function to be integrated. To resolve such a problem, one would perform some variance reduction techniques such as importance sampling, stratification, or control variates.
We will study two approaches for improving the convergence of Monte Carlo using Neural Networks. The first approach relies on the fact that many high dimensional financial problems are of low effective dimensions. We expose a method to reduce the dimension of such problems in order to keep only the necessary variables. The integration can then be done using fast numerical integration techniques such as Gaussian quadrature. The second approach consists in building an automatic control variate using neural networks. We learn the function to be integrated (which incorporates the diffusion model, the discretization as well as the payoff function) in order to build a network that is highly correlated to it. As the network that we use can be integrated exactly, we can use it as a control variate.
Zineb El Filali Ech-Chafiq:
Quantitative Analyst, Natixis
Zineb El Filali Ech-Chafiq: Quantitative Analyst, Natixis
We present the deep parametric PDE method to approximate multi-asset option prices simultaneously for a range of times, states and option parameters of interest. We use an unsupervised learning approach with deep neural networks to numerically solve the high-dimensional parametric partial differential equation. Motivated by the deep Galerkin method, the loss function is only based on the partial differential equation. After a single training phase, the option price for different time, state and parameter values can be computed in millisecond. We evaluate the performance based on the error in the price and the implied volatility with examples of up to 25 dimensions.
Postdoctoral Research Assistant: Queen Mary University of London
Linus Wunderlich: Postdoctoral Research Assistant: Queen Mary University of London
Stuart School of Business, Illinois Institute of Technology
Matthew Dixon: Stuart School of Business, Illinois Institute of Technology
Matthew Dixon, Ph.D, FRM, began his career as a quant in structured credit trading at Lehman Brothers. He has consulted for numerous investment management, trading and financial technology firms in machine learning and risk analytics. He is the author of the 2020 textbook “Machine Learning in Finance: From Theory to Practice” and has written over 20 peer reviewed papers on machine learning and computational finance, including SIAM J. Financial Mathematics and the Journal of Computational Finance. He is the recipient of an Illinois Tech innovation award, and his research has been funded by Intel and the NSF. Matthew has recently contributed to the CFA syllabus on machine learning and he currently serves on the CFA advisory committee for quantitative trading. He has been invited internationally to give talks at prestigious seminars organized by investment banks and universities in addition to being quoted in the Financial Times and Bloomberg Markets. He holds a Ph.D. in Applied Math from Imperial College, has held visiting academic appointments at Stanford and UC Davis, and is a tenure-track Assistant Professor at Illinois Tech.
Wednesday 24th March 2021
- Universal Quantum Computers vs Adiabatic Quantum Computers
- Quantum Annealing
- Reverse Quantum Annealing
- Quantum Random Walk
- Overview of some published research on quantum computing in finance
- Overview of the size of the current quantum computing market and its predicted future
PhD Student in Quantum Computing, UCL
Nedeen Alsharif: PhD Student in Quantum Computing, UCL
Presenter to be confirmed
Professor of Quantum Technologies, University of Sussex
Prof. Winfried Hensinger: Professor of Quantum Technologies, University of Sussex
Prof Winfried Hensinger heads the Sussex Ion Quantum Technology Group and he is the director of the Sussex Centre for Quantum Technologies. Hensinger’s group works on developing and constructing practical trapped-ion quantum computers as well quantum sensors. Hensinger produced the first ion trap microchip in the world and more recently, his group developed a new generation of quantum microchips featuring world record specifications. In 2016, Hensinger and his group invented a new approach to quantum computing with trapped ions where voltages applied to a quantum computer microchip can replace billions of laser beams which would have been required in previous proposals on how to build a quantum computer. In 2017, Hensinger announced the first practical blueprint for building a quantum computer in a paper published in Science Advances (http://advances.sciencemag.org/content/3/2/e1601540.full) giving rise to the assertion that is now possible to construct a large scale quantum computer. Hensinger recently founded, Universal Quantum, a full stack quantum computing company where he serves as Chief Scientist and Chairman.
Associate Director, Quantitative Analyst, Model Validation, Banco Santander
Ángel Rodríguez-Rozas: Associate Director, Quantitative Analyst, Model Validation, Banco Santander
Ángel Rodríguez Rozas holds a Ph.D. in Computational and Applied Mathematics from the University of Lisbon and an M.Sc. in Artificial Intelligence from the Universitat Rovira i Virgili (URV) and the Polytechnic University of Catalonia (UPC). He has authored more than 20 research articles in international peer-reviewed journals in many different areas, including artificial intelligence, numerical methods for PDEs, high-performance computing, plasma physics, the finite element method, seismic wave propagation, and oil&gas simulation and inversion of petrophysical measurements.
Ángel joined Banco Santander in 2018 where he is working as a Quant Analyst in the Internal Validation team, within the Risk Department. As part of his role, Ángel is responsible for leading the design and development of a numerical library for the internal validation of pricing models, including interest rates, FX, credit, commodities, equity, inflation, and xVA. His research efforts are currently focusing on the finance industry, investigating efficient numerical methods (Quasi- and Monte Carlo methods, Finite Elements) and quantum computing algorithms (digital and analog) for the pricing of financial derivatives.