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Initial Margin & SIMM Stream

 08:30: Registration and Morning Welcome Coffee  

09.00 – 09.45: Sarah B Tremel: Global Head of Analytics, Global Valuation Group, HSBC Bank 

Keynote: Usage of Machine Learning in Finance 

  • What is Machine Learning
  • Current usage in Finance, e.g. algo trading, fraud, etc
  • Other possible applications in the future
  • How to implement these technics

09.45 - 10.30: “ISDA SIMM: Evidenced Based Approach to Governance and Development

Presenter: Nnamdi Okaeme: Director - Risk and Capital, International Swaps and Derivatives Association, Inc. (ISDA)

10.30 – 11.00 Morning Break and Networking Opportunities

11.00 - 11.45: Counterparty Loss Modelling for CCAR

Image result for matthias arnsdorf jp morgan quant minds

  • Addressing incremental default risk for the trading book and counterparty risk  

Presenter: Matthias Arnsdorf: Managing Director & Head Of Counterparty Credit Risk Modeling Group, JP Morgan Chase 

11.45 - 12.30Initial Margin Simulation and Optimization via AAD  

The aggregate value of initial margin needed worldwide is estimated to exceed $300B which will have a profound impact on the OTC derivatives market: transforming counterparty credit risk into funding and liquidity risk. We want to target two challenges: (1) How should we forward simulate initial margin - fast, efficient, and generally applicable? (2) how should we measure and minimize its short-term liquidity & long-term funding impact?  

We will discuss:  

  • Forward simulating ISDA SIMM with AAD and forward sensitivities
  • Forward simulating CCP initial margins
  • Optimizing spot & forward initial margin (MVA) at bank level: funding and transaction costs

Presenter: To be confirmed

12.30 - 13.30: Lunch

13.30 - 14.15: Discrete Portfolio Optimisation with Applications in CCP Margin, Funding, and Regulatory Capital   Alexey Erekhinsky

  • Optimal resource allocation often has competing goals: CCP margin vs balance sheet
  • Partitioning of trades between entities as portfolio optimisation problem
  • Genetic algorithms for discrete optimisation (e.g. simulated annealing, bees algorithm)
  • Computational issues: parallelisation, non-linear objectives
  • Similarities and differences to machine learning   

Presenter: Alexey Erekhinsky: XVA modeller, Quantitative Strategies, Credit Suisse

14.15 - 15.00: Machine Learning and Dynamic Initial Margin 

  • Capital Benefits of Modelling Dynamic IM
  • Modelling options for Cleared and Uncleared Trades
  • Dynamic IM and MVA
  • Dynamic IM backtesting 

Presenter: Lucia Cipolina Kun: VP, Bank of America

15.00 - 15.30: Afternoon Break and Networking Opportunities

15.30 - 16.15: The Revised Basel CVA Framework 

Michael Pykhtin

  • 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 

16.15 - 17.00: Efficient MVA by Backward Differentiation  

  • 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 date
  • Particular difficulties with structured products: brute force MVA calculation time is unacceptably long
  • A new 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
  • The talk is based on 

Presenter: To be confirmed

17.00 – 18.00:  Panel


  • Vladimir Chorniy: Senior Technical Lead, BNP Paribas


  • Dong Qu: Managing Director, CVA and Risk Analytics, UniCredit
  • Massimo Morini: Head of Interest Rate and Credit Models, Gruppo Intesa Sanpaolo
  • Ignacio Ruiz: Founder & CEO, MoCaX Intelligence
  • Andrew Green: Managing Director and XVA Lead Quant, Scotiabank  
  • Nnamdi Okaeme: Director - Risk and Capital, International Swaps and Derivatives Association, Inc. (ISDA) (To be confirmed)
  • Mats Kjaer: Head of Quant XVA Analytics, Bloomberg LP 


MVA, Intial Margin & SIMM Topics: 

  • Initial Margin, a push for more model standardization? Good or bad?
  • How do you interpret the regulatory requirements to validate and monitor SIMM, and how would a firm best go about meeting those requirements?
  • SIMM relies on counterparts calculating their own sensitivities. Do the panelists foresee that causing any problems meeting requirements or additional costs?
  • Discuss Implementing SIMM for Non Cleared Initial Margin Rules
  • Explore the interaction between MVA and XVAs:
    • What does MVA mean for XVA overall? Can you simplify the valuation adjustments?
    • Understand the impacts of initial margin, bi-lateral initial margin and MVA on business models
    • Is it possible to ensure transparency of derivative pricing calculation to reduce disputes

XVA & Machine Learning  

  • Discuss the existing and potential applications of machine learning in XVA

Discuss the Impact of FRTB on XVA’s:

  • How will the latest proposed regulations impact CVA calculations
  • Review what are the most important factors to take into account when calculating the new CVA
  • Calculating & Implementing FRTB CVA. How will it affect banks’ internal modelling for counterparty risk and risk management?