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 08:00: Registration and Morning Welcome Coffee


Jörg Kienitz: Partner & Nikolai Nowaczyk: Consultant, Quaternion Risk Management

09.00 - 09.45: Keynote: Machine Learning Enhanced Trading

Presenter: Georgios Papaioannou: Trading Strategist, Bank of America Merrill Lynch

09.45 - 10.30: Machine Learning, AI & Quantum Computing in Quantitative Finance Panel: 


  • Paul Bilokon: Founder, CEO,Thalesians, Senior Quantitative ConsultantBNP Paribas  


  • Miquel Noguer Alonso: Adjunct Assistant Professor, Columbia University
  • Georgios Papaioannou: Trading Strategist, Bank of America Merrill Lynch
  • Abdel Lantere: Data Scientist, Quantitative Consultant, HSBC 
  • Alexei Kondratyev: Managing Director, Head of Prime Services Analytics, Standard Chartered Bank
  • Christoph Burgard: Head of Risk Analytics For Global Markets, Bank of America Merrill Lynch


  • What is the current state of utilisation of machine learning in finance?
  • What are the distinct features of machine learning problems in finance compared to other industries?
  • What are the best practices to overcome these difficulties?
  • What's the evolution of a team using machine learning in terms of day to day operations?
  • What is a typical front office 'Quant' skillset going to look like in three to five years time?
  • How do we deal with model risk in machine learning case?
  • How is machine learning expected to be regulated?
  • What applications can you list among its successes?
  • How much value is it adding over and above the “classical” techniques such as linear regression, convex optimisation, etc.?
  • Do you see high-performance computing (HPC) as a major enabler of machine learning?
  • What advances in HPC have caused the most progress?
  • What do you see as the most important machine learning techniques for the future?
  • What are the main pitfalls of using Machine Learning currently in trading strategies?
  • What new insights can Machine Learning offer into the analysis of financial time series?
  • Discuss the potential of Deep Learning in algorithmic trading?
  • Do you think machine learning and HPC will transform finance 5-10 years from now?
  • If so, how do you envisage this transformation?
  • Can you anticipate any pitfalls that we should watch out for.
  • Discuss quantum computing in quant finance:
    • Breakthroughs
    • Applications
    • Future uses

10.30 – 11.00: Morning Break and Networking Opportunities

11.00 - 11.45: The Revised Basel CVA Framework  

  • The need to revise the framework
  • The consultative paper
  • The industry response
  • The final rule

Presenter: Michael Pykhtin: Manager, Quantitative Risk, U.S. Federal Reserve Board 

11.45 - 12.30:  Back to CVA – Two Current Issues  

  • Loss given default
    • Impact of different LGD assumptions
    • Structural seniority and waterfall priority
    • Entry price and exit price
  • Wrong-way risk (WWR)
    • WWR in cross currency swaps
    • New evidence from the Quanto CDS market on Japanense names
    • Implied jump sizes across sector and rating
    • Evidence from the FX options market 

Presenter: Jon Gregory: Independent xVA Expert 

12.30 - 13.45: Lunch

13.45 - 14.30: Advanced Techniques for Exact SIMM-MVA Calculations

  • Initial margin (IM) and its projection to the future; MVA as a future IM interest
  • Complexity of the MVA: one needs (exotic) portfolio sensitivities calculation for each scenario and observation data
  • Particular difficulties with structured products: brute force MVA calculation time is unacceptably long
  • An efficient method for the exact MVA calculation based on the future differentiation and its comparison with known approximations
  • Numerical experiments for a Bermudan Swaption MVA: massive acceleration using the new method with respect to the brute force

Presenters: Alexandre Antonov, Director, Standard Chartered Bank

14.30 - 15.15: Dynamic IM and XVAs via Chebyshev Spectral Decomposition

  • The power of Chebyshev – MoCaX as a Smart interpolation scheme
  • Selection of interpolating points and functions
  • Chebyshev nodes Chebyshev polynomials in the context of Risk Calculations
  • Theoretical basis: three fundamental theorems
  • Example: Parametric Chebyshev interpolation for Risk Calculations
  • Practical cases studies: CVA, CVA on exotics, Accurate MVA, Ultra-fast XVA sensitivities
  • Commercial benefits: reduction of hardware costs, effective computation of risk metrics, hedging regulatory risk
  • Generic AAD for any pricer via Chebyshev Decomposition  

Presenter: Ignacio Ruiz: Founder & CEO, MoCaX Intelligence

15.15 – 15.45: Afternoon Break and Networking Opportunities

15.45 - 16.30: Topic and Presenter to be Confirmed  

16.30 - 17.15: Johnson Distributions in Finance - Applications to Dynamic Initial Margin Estimation (JLSMC Method)   

The estimation of dynamic initial margin (DIM) for general portfolios is a challenging problem. We consider different approaches and present a new approach, based on regression, that uses Johnson-type distributions, which are fitted to conditional moments estimated using least-squares Monte Carlo simulation (the JLSMC approach). This approach is compared to DIM estimates computed using nested Monte Carlo as a benchmark. Under a number of test cases, the two approaches are shown to be coherent. Furthermore, we show that estimates of DIM produced under the standard regression approach, which assumes portfolio changes are Gaussian, diverges significantly from the better estimates using the JLSMC and nested Monte Carlo approaches. The standard approach performs particularly poorly if the portfolio changes are far from Gaussian (e.g. for options). To further demonstrate the efficacy of the JLSMC approach we provide illustrative examples using Heston and Hull-White models for different derivatives such as European calls and puts as well as payer and receiver swaptions.  

A further advantage of the new approach is that it only relies on the quantities required for any exposure or XVA calculation.  

  • Dynamic Initial Margin and Methods for its Calculation
  • Monte Carlo Simulation and Least Squares Regression
  • Johnson Distributions
  • The JLSMC Method
  • Backtesting / Benchmarking 

Presenter: Jörg Kienitz: Partner & Nikolai Nowaczyk: Consultant, Quaternion Risk Management

20.00: Gala Dinner

Plage Beau Rivage, Nice