Day 1: Wednesday 6th April
08.30 – 09.00
Registration and Welcome Coffee
Double Session: Alternatives to Deep Neural Networks for Function Approximations in Finance
Vladimir Piterbarg:
MD, Head of Quantitative Analytics and Quantitative Development, NatWest Markets
Vladimir Piterbarg:
Vladimir Piterbarg: MD, Head of Quantitative Analytics and Quantitative Development at NatWest Markets
Alexandre Antonov:
Alexandre Antonov:
Alexandre Antonov: Quantitative Research & Development Lead, Abu Dhabi Investment Authority (ADIA)
Alexandre Antonov received his PhD degree from the Landau Institute for Theoretical Physics in 1997. He worked for Numerix during 1998-2017, Danske Bank as the Chief Analyst in Copenhagen and is currently the Quantitative Research & Development Lead at Abu Dhabi Investment Authority (ADIA).
His activity is concentrated on modeling and numerical methods for interest rates, cross currency, hybrid, credit and CVA/FVA/MVA. AA is a published author for multiple publications in mathematical finance and a frequent speaker at financial conferences.
He has received a Quant of Year Award of Risk magazine in 2016.
Morning Break and Networking Opportunities
New Developments in Deep Pricing
Youssef Elouerkhaoui:
Managing Director, Global Head of Markets Quantitative Analysis, Citi
Youssef Elouerkhaoui:
Youssef Elouerkhaoui: Managing Director, Global Head of Markets Quantitative Analysis, Citi
Youssed Elouerkhaoui is the global Head of Credit Quantitive Analysis at Citi. His group supports all aspects of modelling and product development across desks, thais includes: Flow Credit Trading, Correlation Trading, CDOs, Exotics and Emering Markets.
He also supports CVA, Funding and Regulatory Capital for Credit Markets. Prior to this, he was a Director in the Fixed Income Derivatives Quantitative Research Group at UBS, where he was in charge of developing and implementing models for the Structured Credit Desk. Before joining UBS, Youssef was a Quantitative Research Analyst at Credit Lyonnais supporting the Interest Rates Exotics business. He has also worked as a Senior Consultant in the Risk Analytics and Research Group at Ernst & Young. He is a graduate of Ecole Centrale Paris and he holds a PhD in Mathematics from Paris-Dauphine University.
Variational Encoder-Generator-Decoder (VEGD) Models for the Interest Rates
Abstract:
- We propose a variational encoder-generator-decoder (VEGD) model architecture in Q- and P-measure where:
- Latent space geometry is discovered by pretraining VAE encoder and decoder to optimally represent historical interest rate curves, rather than rate increments
- Probability distribution over the latent space is determined by the generator located between encoder and decoder
- Curve and calibration constraints in Q-measure are applied as additional biases of the decoder
- VEGD model learns the optimal mapping of state variables to latent variables and latent space geometry directly from the data, without committing to an SDE
- The proposed architecture permits building a wide variety of models with desirable properties depending on the available calibration data, just like with traditional SDE-based models
- Examples of using VEGD architecture to build machine learning counterparts of short rate models, forward rate models, and curve factor models are provided
Alexander Sokol:
Executive Chairman and Head of Quant Research, CompatibL
Alexander Sokol:
Alexander Sokol: Executive Chairman and Head of Quant Research, CompatibL
Alexander Sokol is the founder, Executive Chairman, and Head of Quant Research at CompatibL, a trading and risk technology company. He is also the co-founder of Numerix, where he served as CTO from 1996 to 2003, and the co-founder of Duality Group, where he served as CTO from 2017 to 2020.
Alexander won the Quant of the Year Award in 2018 together with Leif Andersen and Michael Pykhtin, for their joint work revealing the true scale of the settlement gap risk that remains even in the presence of initial margin. Alexander’s other notable research contributions include systemic wrong-way risk (with Michael Pykhtin, Risk Magazine), joint measure models, and the local price of risk (with John Hull and Alan White, Risk Magazine), and mean reversion skew (Risk Books, 2014).
Alexander earned his BA from the Moscow Institute of Physics and Technology at the age of 18, and a PhD from the L. D. Landau Institute for Theoretical Physics at the age of 22. He was the winner of the USSR Academy of Sciences Medal for Best Student Research of the Year in 1988.
Lunch Break
“Explainability of Learning Models”
Harsh Prasad:
Harsh Prasad:
Harsh Prasad: Principal & CEO, Qxplain
Harsh is currently the Principal & CEO at Qxplain, he previously worked at Morgan Stanley in the Quant Analytics Group. He started his career as a programmer focussed on developing data driven algos in the areas of speech recognition, image processing and bioinformatics. He then moved to financial risk management and over the last 12 years has worked in various roles through the life cycle of models. In these roles, he has been continuously enthusiastic to applying machine learning in problems related to behavioural assumptions, data quality, recommender systems, model benchmarking and text analytics. His current role requires him reviewing all Machine Learning models used by the firm and providing direction to shaping AIML governance framework and strategy. He is also a visiting lecturer with universities and training institutions.
Forecasting Intraday Stock Returns with Deep Learning Using the Limit Order Book
Petter Kolm:
Clinical Full Professor and Director, Courant Institute of Mathematical Sciences, NYU
Petter Kolm:
Petter Kolm: Clinical Full Professor and Director of the M.S. in Mathematics in Finance Program, Courant Institute of Mathematical Sciences, New York University & Partner, CorePoint-Partners.com
Petter Kolm is Clinical Full Professor and Director of the M.S. in Mathematics in Finance Program at the Courant Institute of Mathematical Sciences, New York University, since 2007. He is also Partner at CorePoint-Partners.com. Previously, Petter worked in the Quantitative Strategies group at Goldman Sachs Asset Management, developing proprietary investment strategies, portfolio and risk analytics in equities, fixed income and commodities.
Petter is the co-author of numerous academic journal articles and several well-known finance books including, Financial Modeling of the Equity Market: From CAPM to Cointegration (Wiley, 2006); Trends in Quantitative Finance (CFA Research Institute, 2006); Robust Portfolio Management and Optimization (Wiley, 2007); and Quantitative Equity Investing: Techniques and Strategies (Wiley, 2010).
Petter is a frequent speaker, panelist and moderator at academic and industry conferences and events. He is a member of the editorial boards of the International Journal of Portfolio Analysis and Management (IJPAM), Journal of Financial Data Science (JFDS), Journal of Investment Strategies (JoIS), and Journal of Portfolio Management (JPM). Petter is an Advisory Board Member of Alternative Data Group (ADG), AISignals and Operations in Trading (Aisot), Betterment (one of the largest robo-advisors) and Volatility and Risk Institute at NYU Stern. He is also on the Board of Directors of the International Association for Quantitative Finance (IAQF) and Scientific Advisory Board Member of the Artificial Intelligence Finance Institute (AIFI).
As an advisory board member, consultant, and expert witness, Petter has provided services in areas including alternative data, data science, econometrics, forecasting models, high frequency trading, machine learning, portfolio optimization with transaction costs, quantitative and systematic trading, risk management, robo-advisory, smart beta strategies, trading strategies, transaction costs, and tax-aware investing.
He holds a Ph.D. in Mathematics from Yale University; an M.Phil. in Applied Mathematics from the Royal Institute of Technology, Stockholm, Sweden; and an M.S. in Mathematics from ETH Zurich, Switzerland
Function approximation in Risk Calculations: When to use Deep Neural Networks and when to use Chebyshev Tensors
Ignacio Ruiz:
Ignacio Ruiz:
Ignacio Ruiz: Founder, MoCaX Intelligence
Ignacio Ruiz has been the Head of Counterparty Credit Risk Measurement and Analytics, Scotiabank, the head strategist for Counterparty Credit Risk, exposure measurement, for Credit Suisse, as well as the Head of Risk Methodology, equities, for BNP Paribas. In 2010, Ignacio set up iRuiz Consulting as an independent advisory business in this field. In 2014, Ignacio founded iRuiz Technologies to develop and commercialise MoCaX Intelligence.
Ignacio has several publications in the space of quantitative risk management and pricing. He has also published a comprehensive guide to the subject of XVA Desks and Risk Management.
He holds a PhD in nano-physics from Cambridge University.
Afternoon Break and Networking Opportunities
Machine Learning for Quant Strategies in Crypto Assets using On-Chain Data
- On-chain fundamental and flows data for crypto assets
- Features engineering
- Generalized fused and group Lasso ML methods for feature selection and model training
- Efficient solutions for high-dimensional estimation problem
- Simulation of quant strategies using trained Lasso models
Artur Sepp:
Head Quant, LGT Bank
Artur Sepp:
Artur Sepp: Head Quant, LGT Bank
Artur Sepp is the head quant at LGT bank in Zurich focusing on quantitative asset allocation and systematic investment strategies. Artur has over 15 years of experience in financial markets including heading quant research and portfolio management at a systematic hedge fund and a family office as well as leading development of front-office quant strategies and derivatives at private (Julius Baer) and investment banks (Merrill Lynch/BofA). Artur has a PhD in Mathematical Statistics from University of Tartu, an MSc in Industrial Engineering and Management Sciences from Northwestern University, and a BA cum laude in Mathematical Economics from Tallinn University of Technology. His expertise covers quantitative investing and asset allocation, quantitative modelling of derivative securities, machine learning and data science, and blockchain applications within decentralised finance. He is the author and co-author of several research articles on quantitative finance published in key journals. Artur won Risk Magazine’s Quant of the Year Award (2024). He is an active practitioner of martial arts in his free time.
Panel: Machine Learning & Quantum Computing
Moderator:
Paul Bilokon:
CEO, Thalesians, Visiting Professor, Imperial College
Paul Bilokon:
Paul Bilokon: CEO, Thalesians, Visiting Professor, Imperial College
Dr. Paul Bilokon is CEO and Founder of Thalesians Ltd and an expert in electronic and algorithmic trading across multiple asset classes, having helped build such businesses at Deutsche Bank and Citigroup. Before focussing on electronic trading, Paul worked on derivatives and has served in quantitative roles at Nomura, Lehman Brothers, and Morgan Stanley. Paul has been educated at Christ Church College, Oxford, and Imperial College. Apart from mathematical and computational finance, his academic interests include machine learning and mathematical logic.
Blanka Horvath:
Associate Professor in Mathematical and Computational Finance, University of Oxford
Blanka Horvath:
Blanka Horvath: Associate Professor in Mathematical and Computational Finance, University of Oxford and Researcher, The Alan Turing Institute
Blanka research interests are in the area of Stochastic Analysis and Mathematical Finance.
Including asymptotic and numerical methods for option pricing, smile asymptotics for local- and stochastic volatility models (the SABR model and fractional volatility models in particular), Laplace methods on Wiener space and heat kernel expansions.
Blanka completed her PhD in Financial Mathematics at ETHZürich with Josef Teichmann and Johannes Muhle-Karbe. She holds a Diploma in Mathematics from the University of Bonn and an MSc in Economics from the University of Hong Kong.
Alexander Sokol:
Executive Chairman and Head of Quant Research, CompatibL
Alexander Sokol:
Alexander Sokol: Executive Chairman and Head of Quant Research, CompatibL
Alexander Sokol is the founder, Executive Chairman, and Head of Quant Research at CompatibL, a trading and risk technology company. He is also the co-founder of Numerix, where he served as CTO from 1996 to 2003, and the co-founder of Duality Group, where he served as CTO from 2017 to 2020.
Alexander won the Quant of the Year Award in 2018 together with Leif Andersen and Michael Pykhtin, for their joint work revealing the true scale of the settlement gap risk that remains even in the presence of initial margin. Alexander’s other notable research contributions include systemic wrong-way risk (with Michael Pykhtin, Risk Magazine), joint measure models, and the local price of risk (with John Hull and Alan White, Risk Magazine), and mean reversion skew (Risk Books, 2014).
Alexander earned his BA from the Moscow Institute of Physics and Technology at the age of 18, and a PhD from the L. D. Landau Institute for Theoretical Physics at the age of 22. He was the winner of the USSR Academy of Sciences Medal for Best Student Research of the Year in 1988.
Vladyslav Ivanov:
Quantitative Researcher, Outremont Technologies
Vladyslav Ivanov:
Vladyslav Ivanov: Quantitative Researcher, Outremont Technologies
Vladyslav Ivanov is a Quantitative Researcher with proven experience in leading systematic trading strategies research and applying statistical learning to problems in quantitative finance. Prior to joining Outremont Technologies, Vladyslav worked at a Chicago Proprietary Trading firm, where he conducted alternative data strategies research and was a product owner of the research framework. He also worked in Quantitative Research at a leading New York Hedge Fund, where he designed and implemented a market regimes analysis system, collaborated with the portfolio manager on alpha strategies, and built large-scale data processing systems.
Vladyslav holds a Bachelor’s degree in Financial Economics with a sequence in Data Science from Claremont McKenna College.
David Garvin:
Principal Researcher, NEC Australia
David Garvin:
David Garvin: Principal Researcher, NEC Australia
David focusses on researching, developing and implementing financial industry applications of quantum computing.
David has over 20 years experience as a Front-Office Quant in the Finance Industry. Previously, he has been the Global Head of Quantitative Analysis at the Commonwealth Bank of Australia. Prior to that, he was a Director at Deutsche Bank and a Quant Analyst at Morgan Grenfell. He has covered all asset classes and been involved in management, modelling, risk and analytics, derivatives and structured products, machine learning and electronic trading.
David holds a PhD in Artificial Intelligence from Cambridge University and an MBA (Exec) from the Australian Graduate School of Management. He has authored articles in finance, computing, physics and engineering.
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