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World Business StrategiesServing the Global Financial Community since 2000

Thursday 25th March 2021

Stream One: Machine Learning
Reinforcement Learning for CVA Hedging.

EST: 08.00
GMT: 13.00
CET: 14.00

The objective The objective of the research is to study how the Reinforecement Learning approach might be used to find the optimal hedging strategy for variability in CVA and other xVA quantities.

Problem description: The counterparty credit risk is driven by market, credit and liquidity risks. The impact of those risks on such quantities as Credit Valuation Adjustment impacts directly PnL of xVA desk. Thus the traders construct a portfolio (hedging portfolio) to insure that there is small impact on total PnL while the markets are volatile. The traditional approach to ensure that the desk is hedged against the risks is to use sensitivity to the particular risk factors and also the trader’s view on the market. In this research we propose using RL approach in order to construct the right portfolio of the hedging instruments.

The value added of this research is that RL agent does not require knowledge of the sensitivities to hedge the xVA quantities and will be able to learn the optimal strategy when trading in discrete times and in case of transaction costs.

Ivan Zhdankin:

Systematic Trading, JPMorgan Chase & Co

Ivan Zhdankin: Systematic Trading, JPMorgan Chase & Co

Ivan Zhdankin is a quantitative researcher with experience in diverse areas of quantitative finance, including risk modelling, XVA, and electronic trading across asset classes, including commodity futures and G10 and emerging market currencies. Ivan was consulting various banks in quantitative modeling and has recently joined JP Morgan as a quantitative analyst. He has become one of the first researchers to generate convincing results in electronic alpha with neural nets. He has a solid mathematical background from New Economic School and Moscow State University, where he studied under the celebrated Albert Shiryaev, one of the developers of modern probability theory.

Stream One: Machine Learning
Learning Exotic Derivatives without Calibration

EST: 09.00
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 Fair Value Policy, Intesa Sanpaolo

Marco Bianchetti: Head of Fair Value Policy, 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
Topic to be confirmed

EST: 10.00
GMT: 15.00
CET: 16.00

Petter Kolm:

Professor, Courant Institute of Mathematical Sciences, NYU

Petter Kolm: Director of the Mathematics in Finance Master’s Program and Clinical Professor, Courant Institute of Mathematical Sciences, New York University

Petter Kolm is a Clinical Professor and the Director of the Mathematics in Finance Master’s Program at Courant Institute of Mathematical Sciences, NYU. Previously, Petter worked in the Quantitative Strategies Group at Goldman Sachs Asset Management where his responsibilities included researching and developing new quantitative investment strategies for the group’s hedge fund.  Petter has coauthored numerous academic articles and four books: 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). He holds a Ph.D. in Mathematics from Yale, an M.Phil. in Applied Mathematics from the Royal Institute of Technology, and an M.S. in Mathematics from ETH Zurich.

Petter is a member of the editorial boards of the International Journal of Portfolio Analysis and Management (IJPAM), Journal of Investment Strategies (JoIS), Journal of Portfolio Management (JPM) and Journal of Financial Data Science (JFDS). He is an Advisory Board Member of Betterment (one of the largest robo-advisors) and the Alternative Data Group (ADG). Petter is also on the Board of Directors of the International Association for Quantitative Finance (IAQF) and the Yale Graduate School Alumni Association (GSAA).

Petter’s teaching, work and research interests include alternative data, data science, econometrics, forecasting models, high frequency trading, machine learning, portfolio optimization w/ transaction costs and taxes, quantitative and systematic trading, risk management, robo-advisory and investing, smart beta strategies, transaction costs, and tax-aware investing.

Stream One: Machine Learning
Topic to be confirmed

EST: 11.00
GMT: 16.00
CET: 17.00

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

EST: 12.00
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: (to be confirmed)

Thursday 25th March 2021

Stream Two: Quantum Computing
Post-Quantum Cryptography (PQC)

EST: 08.00
GMT: 13.00
CET: 14.00

Presenter to be confirmed

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

Topic and Presenter to be confirmed

EST: 09.00
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

EST: 10.00
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

EST: 11.00
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

EST: 12.00
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: (to be confirmed)
  • Discount Structure
  • Super early bird discount
    30% until 22nd January 2021

  • Early bird discount
    15% until 19th February 2021

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

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

Only five days left to claim this discount!

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