World Business StrategiesServing the Global Financial Community since 2000

Thursday 25th March 2021

Stream One: Machine Learning
Reinforcement Learning for CVA Hedging.

EDT: 09.00
GMT: 13.00
CET: 14.00

Stream One: Machine Learning
Learning Exotic Derivatives without Calibration

EDT: 10.00
GMT: 14.00
CET: 15.00

Contents:

  • Where we are
  • From model parameters to market data
  • Configuring and training the ANNs
  • Results and stability analysis
  • Future work

Abstract

Machine Learning has worked his way into pricing several years ago. In this context we say pricing meaning ‘pricing according to a model’. After all, learning how to price is just learning a function depending on a very limited number of parameters. Neural networks have proven time over time to be quite effective in learning functions so it was a natural venue to apply them in this context. As a first step we test simple models, i.e. Black-Scholes, and complex payoffs like digital multi-asset options. Then we test more complex models.

A pricing model is defined by few parameters, which are calibrated to market data and then used to price assets not quoted in the market. The calibration procedure is quite delicate and time consuming, since it must be repeated frequently to catch every single glitch of the market. The idea behind this work is to use artificial neural network not only to price derivatives but also to replace the calibration procedure. In a ‘standard’ ANN approach we would train the network using model parameters. Instead, we try to replace model parameters with market data, such that, once trained, the network can provide reliable results without need of calibration. We test these concepts on toy models with available (semi) analytical solutions, i.e. the stochastic volatility Heston model and the One factor Hull & White model.

Marco Bianchetti:

Head of Internal Model Market Risk, Intesa Sanpaolo

Marco Bianchetti: Head of Internal Model Market Risk, Intesa Sanpaolo

Marco Bianchetti joined the Market Risk Management area of Intesa Marco joined the Financial and Market Risk Management area of Intesa Sanpaolo in 2008. His work covers pricing and risk management of financial instruments across all asset classes, with a focus on new products development, model validation, model risk management, interest rate modelling, funding and counterparty risk, fair and prudent valuation, applications of Quasi Monte Carlo in finance. He is in charge of the global Fair Value Policy of Intesa Sanpaolo group since Nov. 2015. Previously he worked for 8 years in the front office Financial Engineering area of Banca Caboto (now Banca IMI), developing pricing models and applications for interest rate and inflation trading desks. He is adjunct professor of Interest Rate Models at University of Bologna since 2015, and a frequent speaker at international conferences and trainings in quantitative finance. He holds a M.Sc. in theoretical nuclear physics and a Ph.D. in theoretical condensed matter physics.

Pietro Rossi:

Financial Analyst, Prometeia S.p.a

Pietro Rossi: Financial Analyst, Prometeia S.p.a

Pietro Rossi holds a degree in physics from the University of Parma and a Ph.D. from New York University. He has been actively doing research in the field of high energy physics in the early stage of his career and moved later on to high performance computing. In the last ten years he has been involved with mathematical finance. Currently his research activity splits between the use of Fourier transform Methods in finance and machine learning techniques applied to financial risk management.
Since 2018 he has a teaching appointment with the University of Bologna.

Stream One: Machine Learning
Natural Language Processing & the Machine Learning Revolution

EDT: 11.00
GMT: 15.00
CET: 16.00

Natural language processing has achieved remarkable progress over the last decade, and many companies now rely on NLP-based insights to drive their business intelligence. Recent developments involving deep learning models now provide us with access to modularized and domain-specific approaches to address historically challenging problems. We will briefly go over the evolution of NLP, and unpack the details behind some of the groundbreaking techniques that made these advances possible. We’ll also discuss how these exciting new approaches fit within the NLP landscape as it relates to investment management, and highlight the appropriate use cases for various frameworks along with their strengths and weaknesses.

Marko Kangrga:

Head of Quantitative Research, RavenPack

Marko Kangrga: Head of Quantitative Research, RavenPack

Marko is the Head of Quantitative Research for the Americas at RavenPack with over 10 years of experience in the finance industry. He focuses on exploring novel approaches and techniques for combining fundamental drivers with big data quantitative frameworks to identify alpha opportunities from a wide universe of securities across multiple asset classes. Previously, as the head trader/investment analyst at an event-driven hedge fund in New York, he was responsible for macro research, idea generation and risk management. Marko has experience in utilizing quantitative methods in portfolio construction, developing hedging strategies and trading structured derivative instruments.

Sponsor: RavenPack
Stream One: Machine Learning
Inverse Reinforcement Learning for Finance: G-learner and GIRL, the Invisible Hand, and other stories

EDT: 12.00
GMT: 16.00
CET: 17.00

  • Tasks solved by RL and IRL, and why they are relevant for finance
  • Sketch of math involved
  • Simple examples
  • Use cases for IRL
  • Outlook of future developments

Igor Halperin:

AI Research Associate, Fidelity Investments

Igor Halperin: AI Research Associate, Fidelity Investments

Igor Halperin is an AI Research Associate at Fidelity Investments. His research focuses on using methods of reinforcement learning, information theory, neuroscience and physics for financial problems such as portfolio optimization, dynamic risk management, and inference of sequential decision-making processes of financial agents. Igor has an extensive industrial and academic experience in statistical and financial modeling, in particular in the areas of option pricing, credit portfolio risk modeling, portfolio optimization, and operational risk modeling. Prior to joining Fidelity, Igor worked as a Research Professor of Financial Machine Learning at NYU Tandon School of Engineering.  Before that, Igor was an Executive Director of Quantitative Research at JPMorgan, and a quantitative researcher at Bloomberg LP. Igor has published numerous articles in finance and physics journals, and is a frequent speaker at financial conferences. He has co-authored the books “Machine Learning in Finance: From Theory to Practice” (Springer 2020) and “Credit Risk Frontiers” (Bloomberg LP, 2012). Igor has a Ph.D. in theoretical high energy physics from Tel Aviv University, and a M.Sc. in nuclear physics from St. Petersburg State Technical University.

Both Streams
Quantum Computing Networking & Informal Discussion Rooms

EDT: 13.00
GMT: 17.00
CET: 18.00

Chill out and chat informally at the end of the day on all things Quantum Computing, with the global quants community. The main meeting room will be moderated by WBS Training with mics open on request or simply grab a coffee or a glass of wine and jump into a breakout room:

  • Main Meeting Room
  • Private Rooms
  • Breakout Rooms One (Maximum 6)
  • Breakout Rooms Two (Maximum 12)
Main Room Experts:

Alexei Kondratyev:

Managing Director, Head of Data Analytics, Standard Chartered Bank

Alexei Kondratyev: Managing Director, Head of Data Analytics, Standard Chartered Bank

In his role as Managing Director and Head of Data Analytics at Standard Chartered Bank, Alexei is responsible for providing data analytics services to Financial Markets sales and trading.

He joined Standard Chartered Bank in 2010 from Barclays Capital where he managed a model development team within Credit Risk Analytics. Prior to joining Barclays Capital in 2004, he was a senior quantitative analyst at Dresdner Bank in Frankfurt.

Alexei holds MSc in Theoretical Nuclear Physics from the University of Kiev and PhD in Mathematical Physics from the Institute for Mathematics, National Academy of Sciences of Ukraine.

Nedeen Alsharif:

PhD Student in Quantum Computing, UCL

Nedeen Alsharif: PhD Student in Quantum Computing, UCL

Ángel Rodríguez-Rozas:

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.

Thursday 25th March 2021

Stream Two: Quantum Computing
Toward pricing financial derivatives with an IBM quantum computer

EDT: 09.00
GMT: 13.00
CET: 14.00

Pricing interest-rate financial derivatives is a major problem in finance, in which it is crucial to accurately reproduce the time evolution of interest rates. Several stochastic dynamics have been proposed in the literature to model either the instantaneous interest rate or the instantaneous forward rate. A successful approach to model the latter is the celebrated Heath-Jarrow-Morton framework, in which its dynamics is entirely specified by volatility factors. In its multifactor version, this model considers several noisy components to capture at best the dynamics of several time-maturing forward rates. However, as no general analytical solution is available, there is a trade-off between the number of noisy factors considered and the computational time to perform a numerical simulation. Here, we employ the quantum principal component analysis to reduce the number of noisy factors required to accurately simulate the time evolution of several time-maturing forward rates. The principal components are experimentally estimated with the five-qubit IBMQX2 quantum computer for 2×2 and 3×3 cross-correlation matrices, which are based on historical data for two and three time-maturing forward rates. This paper is a step towards the design of a general quantum algorithm to fully simulate on quantum computers the Heath-Jarrow-Morton model for pricing interest-rate financial derivatives. It shows indeed that practical applications of quantum computers in finance will be achievable in the near future.

Ana Martín Fernández:

PhD Student, University of the Basque Country, member of the QUTIS group and Chief Scientist Officer (CSO), Quantum Mads

Ana Martín Fernández: PhD Student, University of the Basque Country, member of the QUTIS group and Chief Scientist Officer (CSO), Quantum Mads

Stream Two: Quantum Computing
Reverse Stress Testing of xVA using Quantum Computing

Topic and Presenter to be confirmed

EDT: 10.00
GMT: 14.00
CET: 15.00

Assad Bouayoun:

XVA and Credit Derivative Quant, Daiwa Capital Markets

Assad Bouayoun: XVA and Credit Derivative Quant, Daiwa Capital Markets

Assad Bouayoun is a senior XVA Quantitative Analyst with more than 15 years’ experience in leading banks. He has designed industry standard hedging and pricing systems, first in equity derivative at Commerzbank, then in credit derivatives at Credit Agricole, in XVA at Lloyds in Model Validation at RBS in Model Development. Assad has an extensive experience in developing enterprise wide analytics to improve the financial management of derivative portfolios, in particular large scale hybrid Monte-Carlo and Exposure computation. Assad is currently building the prototype of a new XVA platform integrating cutting-edge technologies (GPU, Cloud computing) and numerical methods (AAD) to enable fast and accurate XVA and sensitivities computation. He holds a MSc in Mathematical Trading and Finance from CASS business school and a Master in Applied Mathematics and Computer Science from Université de Technologie de Compiegne (France).

Stream Two: Quantum Computing
Quantum Advantage

EDT: 11.00
GMT: 15.00
CET: 16.00

Abstract:  Ever since Google demonstrated quantum supremacy in 2019 on a specially constructed problem, the search for quantum advantage – finding a real-world practical use case where a quantum processor would outperform the best available classical one – is the main focus of quantum computing research. It is highly likely that the emerging discipline of Quantum Machine Learning (QML) will be the first to produce a definite evidence of quantum advantage using a hybrid quantum-classical approach to training and running the Quantum Neural Networks. With exceptionally fast rate of quantum hardware development we can detect the first signs of the quantum advantage on finance-related use cases.

Alexei Kondratyev:

Managing Director, Head of Data Analytics, Standard Chartered Bank

Alexei Kondratyev: Managing Director, Head of Data Analytics, Standard Chartered Bank

In his role as Managing Director and Head of Data Analytics at Standard Chartered Bank, Alexei is responsible for providing data analytics services to Financial Markets sales and trading.

He joined Standard Chartered Bank in 2010 from Barclays Capital where he managed a model development team within Credit Risk Analytics. Prior to joining Barclays Capital in 2004, he was a senior quantitative analyst at Dresdner Bank in Frankfurt.

Alexei holds MSc in Theoretical Nuclear Physics from the University of Kiev and PhD in Mathematical Physics from the Institute for Mathematics, National Academy of Sciences of Ukraine.

Stream Two: Quantum Computing
Case Study – Quantum Optimisation

EDT: 12.00
GMT: 16.00
CET: 17.00

Davide Venturelli:

Associate Director, Quantum Computing, USRA

Davide Venturelli: Associate Director, Quantum Computing, USRA

Davide Venturelli works in the NASA Intelligent System Division (TI) as a research scientist under the NASA Academic Mission Service contract. He is currently Quantum Computing team lead and Science Operations Manager of the Research Institute of Advanced Computer Science (RIACS) at USRA. Venturelli is currently invested in research projects dealing with quantum optimization applications and their near-term implementation in real hardware.

Both Streams
Quantum Computing Networking & Informal Discussion Rooms

EDT: 13.00
GMT: 17.00
CET: 18.00

Chill out and chat informally at the end of the day on all things Quantum Computing, with the global quants community. The main meeting room will be moderated by WBS Training with mics open on request or simply grab a coffee or a glass of wine and jump into a breakout room:

  • Main Meeting Room
  • Private Rooms
  • Breakout Rooms One (Maximum 6)
  • Breakout Rooms Two (Maximum 12)
Main Room Experts:

Alexei Kondratyev:

Managing Director, Head of Data Analytics, Standard Chartered Bank

Alexei Kondratyev: Managing Director, Head of Data Analytics, Standard Chartered Bank

In his role as Managing Director and Head of Data Analytics at Standard Chartered Bank, Alexei is responsible for providing data analytics services to Financial Markets sales and trading.

He joined Standard Chartered Bank in 2010 from Barclays Capital where he managed a model development team within Credit Risk Analytics. Prior to joining Barclays Capital in 2004, he was a senior quantitative analyst at Dresdner Bank in Frankfurt.

Alexei holds MSc in Theoretical Nuclear Physics from the University of Kiev and PhD in Mathematical Physics from the Institute for Mathematics, National Academy of Sciences of Ukraine.

Nedeen Alsharif:

PhD Student in Quantum Computing, UCL

Nedeen Alsharif: PhD Student in Quantum Computing, UCL

Ángel Rodríguez-Rozas:

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.

  • Discount Structure
  • Special Offer
    When two colleagues attend the 3rd goes free!

  • 70% Academic Discount
    (FULL-TIME Students Only)

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