Monday 22nd March 2021
Founder, CEO, Thalesians & Senior Quantitative Consultant, BNP Paribas
Paul Bilokon: Founder, CEO, Thalesians & Senior Quantitative Consultant, BNP Paribas
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.
David Jessop: Head of Investment Risk at Columbia Threadneedle Investments EMEA APAC
David is the Head of Investment Risk at Columbia Threadneedle Investments EMEA APAC. Previously the Global Head of Equities Quantitative Research at UBS. His areas of research include portfolio analysis and construction, style analysis and risk modelling. He also helps clients understand, use and implement the quantitative tools available from UBS. David joined UBS in 2002. Prior to this, he spent seven years at Citigroup as Head of Global Quantitative Marketing. Before moving to the sell side he spent six years at Morgan Grenfell Asset Management, where he managed index funds, asset allocation funds and also an option overwriting fund.
David graduated from Trinity College, Cambridge with an MA in Mathematics.
Joint research with Alexei Kondratyev.
Limit management, asset/liability management, insurance, and macro investing each require simulation in real world measure for time horizons of 5y to 30y and beyond. At time horizons that long, samples cannot be drawn directly from the historical data while Monte Carlo simulation encounters well documented challenges.
In this presentation, we propose the use of generative neural networks as an alternative to Monte Carlo simulation in real-world measure for long time horizons. For market risk, historical simulation is preferred by regulators and risk managers over Monte Carlo simulation because exogenous assumptions involved in selecting the Monte Carlo model SDE proved to be an important source of variability in calculation results. Generative machine learning models, which also do not require an explicit SDE, provide a natural extension of historical simulation to long time horizons and share many of its attractive properties. We describe two generative interest rate model frameworks: one for the term rates and the other for the forward rates.
To overcome the challenge of insufficient time series length compared to model horizon, we use unsupervised learning to calibrate the model to multi-currency datasets. Using historical datasets for government bond yields and swap rates, we show that incorporating data from additional currencies into the model does not result in a single model for the “average currency”. Rather, it reduces model error compared to single currency calibration while preserving unique calibration for each currency.
Executive Chairman and Head of Quant Research, CompatibL
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.
Inspired by a series of remarkable papers in recent years*, that use Deep Neural Networks (DNN) to substantially speed up the calibration of rough volatility models, we investigate the use of Chebyshev Tensors instead of DNNs. Given that Chebyshev Tensors (CT) can be, under certain circumstances, more efficient than DNNs at exploring the input space of the function that needs to be approximated – due to their exponential convergence – the problem of calibration of rough volatility models seems, a priori, a good candidate to explore the use of CTs.
In this piece of research, we built CTs, either directly or with the help of Tensor Extension Algorithms, to tackle the computational bottleneck associated with the calibration of rough volatility models. Results are encouraging as the accuracy of model calibration via CTs is similar to that when using DNNs, but with building times that were 100 more efficient in some of the experiments. Our tests indicate that when using CTs, the calibration of rough volatility models is around 40,000 times more efficient than if calibrated via “brute-force” (using the pricing function).
In this talk we will introduce the problem being tackled, discuss the fundamentals of the approach via CTs and show the numerical results obtained.
- The problem of pricing model calibration
- Brief overview of existing literature
- A candidate to help in the problem: Chebyshev Tensor; key properties and potential limitations
- How to use Chebyshev Tensors for pricing model calibration
- Direct approach
- In combination to Tensor Extension Algorithms
- Numerical results
- Constant term structure models
- Varying terms structure models
- C. Bayer, B. Stemper. (2018). Deep calibration of rough stochastic volatility models.
- B. Horvath, A. Muguruza, M. Thomas (2019) Deep Learning Volatility
Head of Counterparty Credit Risk Measurement and Analytics, Scotiabank
Ignacio Ruiz: Head of Counterparty Credit Risk Measurement and Analytics, Scotiabank
Ignacio Ruiz has been 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.
He holds a PhD in nano-physics from Cambridge University.
Head of Research and Development: MoCaX Intelligence
Mariano Zeron: Head of Research and Development: MoCaX Intelligence
Mariano leads our Research & Development work. He has vast experience in Chebyshev Spectral Decomposition, machine-learning and related disciplines, and their application to quantitative problems in the financial markets. Mariano holds a Ph.D. in Mathematics from Cambridge University.
There’s a reason why volatility surfaces maintain their mystique: no matter how we try to get a handle on them, no one really knows what never-before-seen surfaces are lurking out there. Variational autoencoders allow us to remove human bias from the puzzle and let the data speak for itself through unsupervised learning. In joint work with N. Fung, Z. Poulos and J. Hull at the University of Toronto, Riskfuel showed how these light-weight models can be used to produce synthetic volatility surfaces that are indistinguishable from those observed in the market. In particular, this allows us to
- robustly complete partially observed volatility surfaces,
- explore the space of possible volatility surfaces for stress testing purposes, and
- generate training data for AI-accelerated derivatives models
without making assumptions about the process driving the underlying asset or the shape of the surface.
Director of Research and Development, Riskfuel
Maxime Bergeron: Director of Research and Development, Riskfuel
Maxime Bergeron is the Director of Research and Development at Riskfuel, a capital markets focused startup that is developing ultra-fast AI-based valuation technologies. There, his work is focused on applied machine learning and the topology of high dimensional data. Prior to joining Riskfuel, he was a faculty member at the University of Chicago. He holds a PhD in Mathematics from the University of British Columbia.
Monday 22nd March 2021
We introduce a first theory of price impact in presence of an interest-rates term structure. We explain how one can formulate instantaneous and transient price impact on bonds with different maturities, including a cross price impact that is endogenous to the term structure. We connect the introduced impact to classic no-arbitrage theory for interest rate markets, showing that impact can be embedded in the pricing measure and that no-arbitrage can be preserved. We present pricing examples in presence of price impact and numerical examples of how impact changes the shape of the term structure. Finally, to show that our approach is applicable we solve an optimal execution problem in interest rate markets with the type of price impact we developed in the paper.
PhD Student, Imperial College London
Federico Graceffa: PhD Student, Imperial College London
I am a Phd student in Applied and Financial Mathematics at Imperial College London (UK), working mainly on random dynamical systems, nonlinear valuation of financial derivatives, price impact and interest rates theory.
MD, Head of Quantitative Analytics and Quantitative Development, NatWest Markets
Vladimir Piterbarg: MD, Head of Quantitative Analytics and Quantitative Development at NatWest Markets
Professor and Dept. Chair of FRE Tandon, New York University
Peter Carr: Professor and Dept. Chair of FRE Tandon, New York University
Dr. Peter Carr is the Chair of the Finance and Risk Engineering Department at NYU Tandon School of Engineering. He has headed various quant groups in the financial industry for the last twenty years. He also presently serves as a trustee for the National Museum of Mathematics and WorldQuant University. Prior to joining the financial industry, Dr. Carr was a finance professor for 8 years at Cornell University, after obtaining his Ph.D. from UCLA in 1989. He has over 85 publications in academic and industry-oriented journals and serves as an associate editor for 8 journals related to mathematical finance. He was selected as Quant of the Year by Risk Magazine in 2003 and Financial Engineer of the Year by IAQF/Sungard in 2010. From 2011 to 2014, Dr. Carr was included in Institutional Investor’s Tech 50, an annual listing of the 50 most influential people in financial technology.
In the 2 years Dr. Carr been FRE dept. chair, applications increased from 1,300 per year to 1,900 per year. The number of FRE Masters students in residence was the highest in any 2-year period. For the incoming 2018 class, current verbal GRE is 169/170 and GPA is 3.82. FRE moved up in Quantnet rankings both years. An online summer course was initiated last summer and an on-campus bootcamp will be initiated this summer. Six electives on machine learning in finance were introduced. The distance learning room will become operational this summer.
Quant libraries are being developed over a number of years, with the focus often being on functionality than performance. New requirements from the business and the regulators arise, making it essential to have easy to read, develop and maintain code. And, at the same time, getting the top performance is essential.
In this presentation, we talk about every quant’s practical need: maximum re-use of legacy code, efficient memory use, clear code, performance, parallel programming and AAD. We will introduce a novel approach to achieve the best of both worlds: keeping the simplicity of clear Object-Oriented analytics and getting the top performance of optimized Data-Oriented design. We will demonstrate the actual implementation of this approach using a well-known open-source quant library – QuantLib, yielding 150x performance improvement on a single core for xVA calculations.
We will also talk about developer tools that can make quant life a lot easier for times when things don’t work as expected.
Head of Automatic Adjoint Differentiation, Matlogica
Dmitri Goloubentsev: Head of Automatic Adjoint Differentiation, Matlogica
Dmitri has 15 years of combined experience in model development working on C++ quant libraries. He worked as a Senior Quant Analyst in interest rate derivatives and played a leading role in delivering XVA solution at a major Canadian bank. Prior to focusing on AAD, he was responsible for construction of SIMM/MVA model. Dmitri earned his degree in Maths and Applied Maths from the Moscow State University.
Senior Director, Head of Methodology and Analytics, Capital Markets Risk Management, CIBC
Hany Farag: Senior Director, Head of Methodology and Analytics, Capital Markets Risk Management, CIBC
Hany Farag is Senior Director and Head of Risk Methodology and Analytics at CIBC. Prior to his current position he was a partner at Eastmoor Capital Partners, LLP; Managing Director and Head of FX Statistical Arbitrage at CIBC; and Head of Quantitative Research at OANDA Corporation. Prior to his industry positions he was a Postdoctoral Fellow at Caltech and at Rice University. He holds a PhD in Mathematical Analysis from Yale, a MS in Theoretical Physics from Yale, and a BSC in Electronics and Communication Engineering from Ain Shams.