World Business StrategiesServing the Global Financial Community since 2000

Tuesday 23rd March 2021

Stream One: AI
Frameworks for Model Risk Management of AI

EDT: 09.00
GMT: 13.00
CET: 14.00

  • Model risk components
    • Overview of market practice
    • Technological evolutions
  • Adapting for AI
    • Typical ML model dependencies
    • Frameworks
      • designing AI-safety
      • assessment list for trustworthy AI
      • quantitative tests
    • Design considerations
    • Limitations

Jos Gheerardyn:

Co-founder and CEO, Yields.io

Jos Gheerardyn: Co-founder and CEO of Yields.io

Jos is the co-founder and CEO of Yields.io. Prior to his current role he has been active in quantitative finance both as a manager and as an analyst. Over the past 15 years he has been working with leading international investment banks as well as with award winning start-up companies. He is the author of multiple patents applying quantitative risk management techniques on imbalance markets. Jos holds a PhD in superstring theory from the University of Leuven.

Sponsor: Yields.io
Stream One: AI
"Why Causal AI Prevents Overfitting"

EDT: 10.00
GMT: 14.00
CET: 15.00

Darko Matovski:

CEO, causaLens

Darko Matovski: CEO, causaLens

Dr. Darko Matovski is the CEO of causaLens. The company is leading Causal AI research, a way for machines to understand cause & effect, and serves some of the most sophisticated organisations. Darko has also worked for cutting edge hedge funds and research institutions. For example, the National Physical Laboratory in London (where Alan Turing worked) and Man Group in London. Darko has a PhD in Machine Learning and an MBA.

Sponsor: causaLens
Stream One: AI
The Agony of Consensus

EDT: 11.00
GMT: 15.00
CET: 16.00

Abstract

Artificial intelligence and cryptocurrencies have taken finance into the realm of bold 21st century economic and tech experiments. In AI, the need for modeling and measuring consensus is pervasive across workflows, from data labeling to ensemble modeling and federated learning. In the case of cryptocurrencies, underlying the various digital coins are sophisticated, distributed ledgers that rely on consensus algorithms to eliminate the need of a centralized authority to validate transactions. In this talk, I highlight the most important aspects of modeling consensus taking into account computer science, finance, social, and environmental perspectives.

Ioana Boier:

Independent

Ioana Boier: Independent

I have a Ph.D. in Computer Science from Purdue University. In addition, I have completed graduate coursework in Financial Mathematics at NYU and Big Data at Harvard University. Prior to joining Citadel, I was a Director in the Global Markets Division at BNP Paribas where I managed the Interest Rate Options & Inflation quantitative research team. Before transitioning into Finance, I was a research staff member at the IBM T. J. Watson Research Center.

Stream One: AI
New Tool for Market Monitoring: MarketScribe

EDT: 12.00
GMT: 16.00
CET: 17.00

Abstract: The market group serves as “eyes and ears” of the Federal Reserve System and Treasure Department in terms of monetary policy, financial stability policy, foreign exchange policy and debt management policy.  One of the key responsibility of the market group is market monitoring, which provides the rigorous analysis of how the financial markets are reacting to, and interpreting monetary policy.  One importance piece of this process is the event studies of FOMC communications and other policy communications such as governor and Fed president’s speeches/interviews. This is time sensitive work, and currently is very human resource intensive because it is conducted manually. Built with advanced AI techniques, the MarketScribe is an NLP tool transcribe and summarize the public speeches, and visualize the market impact of these communications on a word-to-word basis. The tool outputs a word document including the summary and full transcription of the speech. The tool also includes an interactive dashboard to visualize the transcription analysis together with the market data movement. It is now been utilized in the market continues group for the quick overview of the market during the FOMC time.

Knarig Arabshian:

Senior Knowledge Engineer in Technology Innovation, Federal Reserve Bank of New York

Knarig Arabshian: Senior Associate Knowledge Engineer in Technology Innovation, Federal Reserve Bank of New York

I am a Senior Associate Knowledge Engineer in Technology Strategy & Innovation at theFederal Reserve Bank of New York where I conduct research in semantic web technologies and text analytics for structuring financial data.

Previously, I was an Assistant Professor in the Computer Science Department at Hofstra University in Hempstead, NY and a Member of Technical Staff at Bell Labs in Murray Hill, NJ. I have also taught as an Adjunct Professor at Columbia University twice. I received my PhD in Computer Science from Columbia University in 2008, where I worked in theIRT Lab under the advisment of Henning Schulzrinne.

Xue Rui:

Senior Associate, Enterprise Architecture, Federal Reserve Bank of New York

Xue Rui: Senior Associate, Enterprise Architecture, Federal Reserve Bank of New York

Xue graduates from University of Notre Dame with Ph.D in physics and Master in Electric Engineering. She is a Senior Associate in Federal Reserve Bank of New York. Her work focuses on developing and deploying NLP and AI technology in the bank. Prior to joining the Federal Reserve Bank, Xue has been working as Scientist in General Electric Global Research Center. Xue’s research interests focus on artificial intelligence, including natural language processing, image analysis, and computer vision.  She holds 20 + peer reviewed publications and 10 + patents.

Both Streams
xVA 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 xVA, 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:

Andrey Chirikhin:

Head of Structured Credit QA, Barclays Investment Bank

Andrey Chirikhin: Head of Structured Credit QA, Barclays Investment Bank

Andrey was formerly Head of Modelling and Quantitative Analytics for L1 Treasury, part of a USD 25bn privately held investment vehicle LetterOne. Prior to LetterOne, Andrey was MD and Head of CVA and CCR quantitative Analytics at RBS. There he has created and run the front office cross asset CVA quant team. He also restructured and led the risk-side quant team charged with delivering a new Basel III compliant internal CCR methodology. The system utilizing the newly delivered methodology has won the 2013 Internal System of the year Risk award. In his 20 year career in investment banking, Andrey held several leadership and senior quant positions at Goldman Sachs, HSBC and Dresdner Kleinwort. Andrey Chirikhin holds PhD in Theoretical Statistics from Warwick University (UK), MBA from INSDEAD and MSc in Applied Mathematics from Moscow Institute for Physics and Technology (Phystech).

Since 2018 Andrey runs his own company, Quantitative Recipes, that advises on wide rage of XVA, long-term market modelling for risk and quant infrastructure.

Stéphane Crépey:

Professor of Mathematics Université de Paris, Laboratoire de Probabilités, Statistique et Modélisation

Stéphane Crépey: Professor of Mathematics at the Université de Paris, Laboratoire de Probabilités, Statistique et Modélisation (LPSM)

Stéphane Crépey is the Professor of Mathematics at the Université de Paris, Laboratoire de Probabilités, Statistique et Modélisation (LPSM). Formerly professor at the Mathematics Department of University of Evry (France), head of Probability and Mathematical Finance and head of the Engineering and Finance branch (M2IF) of the Paris-Saclay Master Program in Financial Mathematics. His research interests are financial modeling, counterparty and credit risk, numerical finance, as well as related mathematical topics in the fields of backward stochastic differential equations and partial differential equations. He is the author of numerous research papers and two books: “Financial Modeling: A Backward Stochastic Differential Equations Perspective” (S. Crépey, Springer Finance Textbook Series, 2013) and “Counterparty Risk and Funding, a Tale of Two Puzzles” (S. Crépey, T. Bielecki and D. Brigo, Chapman & Hall/CRC Financial Mathematics Series, 2014).

He is an associate editor of SIAM Journal on Financial Mathematics, International Journal of Theoretical and Applied Finance, and a member of the scientific council of the French financial markets authority (AMF). Stéphane Crépey graduated from ENSAE and he holds a PhD in applied mathematics from Ecole Polytechnique and INRIA Sophia Antipolis.

Ignacio Ruiz:

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.

Ignacio has several publications in the space of quantitative risk management and pricing. He has also published a comprehensive guide to the subject of XVA Desks and Risk Management.

He holds a PhD in nano-physics from Cambridge University.

Justin Chan:

Head of Product Management, Risk, FIS

Justin Chan: Head of Product Management, Risk, FIS

Justin Chan has over 11 years of experience in financial risk management and capital markets. Mr. Chan has a deep focus on quantitative modelling in areas such as xVA, credit exposure, and collateral simulations. He is currently responsible for the Risk Quantitative Strategy and Innovation program at FIS. Prior to FIS, Mr. Chan worked at Manulife Financial as a manager in corporate risk management.

Mr. Chan studied engineering science (BASc), and theoretical physics (MSc) at University of Toronto, where he also holds a Master of Mathematical Finance (MMF) degree.

Dmitri Goloubentsev:

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.

Tuesday 23rd March 2021

Stream Two: xVA
Hierarchical Simulation for Deep XVA Analysis

EDT: 09.00
GMT: 13.00
CET: 14.00

Talk by Stéphane Crépey, based on joint researches with Lokman Abbas-Turki, Claudio Albanese, Rodney Hoskinson, and Bouazza Saadeddine.

Greensill defaulted on March 8, 2021. They fell short of capital because they lent to Gupta against future invoices which then did not materialise.  If KVA had been implemented in the banking context, the Greensill blow up could have been avoided because of (at least) two reasons. First, if you compute risk margins on a run off basis, you cannot include invoices which do not exist yet as collateral, so you can’t lend against them. A simulation of capital requirements on a run-off basis would have highlighted the ensuing lack of collateral. Second, in a proper risk margin calculation, Greensill would also have had to include the asset side in the calculation, i.e. the loan to Gupta which is rated junk. In a credit risk simulation with binary default probabilities for Gupta, the contribution to the tail linked to Gupta’s default would have been enormous. With this motivating example in mind, we show how the XVA metrics can be learned pathwise by neural net regression, taking the mark to market cube of the bank and a simulation of client defaults (as opposed to their probabilities simply in nowadays XVA computations) as input data.

Stéphane Crépey:

Professor of Mathematics Université de Paris, Laboratoire de Probabilités, Statistique et Modélisation

Stéphane Crépey: Professor of Mathematics at the Université de Paris, Laboratoire de Probabilités, Statistique et Modélisation (LPSM)

Stéphane Crépey is the Professor of Mathematics at the Université de Paris, Laboratoire de Probabilités, Statistique et Modélisation (LPSM). Formerly professor at the Mathematics Department of University of Evry (France), head of Probability and Mathematical Finance and head of the Engineering and Finance branch (M2IF) of the Paris-Saclay Master Program in Financial Mathematics. His research interests are financial modeling, counterparty and credit risk, numerical finance, as well as related mathematical topics in the fields of backward stochastic differential equations and partial differential equations. He is the author of numerous research papers and two books: “Financial Modeling: A Backward Stochastic Differential Equations Perspective” (S. Crépey, Springer Finance Textbook Series, 2013) and “Counterparty Risk and Funding, a Tale of Two Puzzles” (S. Crépey, T. Bielecki and D. Brigo, Chapman & Hall/CRC Financial Mathematics Series, 2014).

He is an associate editor of SIAM Journal on Financial Mathematics, International Journal of Theoretical and Applied Finance, and a member of the scientific council of the French financial markets authority (AMF). Stéphane Crépey graduated from ENSAE and he holds a PhD in applied mathematics from Ecole Polytechnique and INRIA Sophia Antipolis.

Stream Two: xVA
Dynamically Controlled Kernel Estimation

EDT: 10.00
GMT: 14.00
CET: 15.00

Gordon Lee:

XVA and Capital Quantitative Analyst, UBS

Gordon Lee: XVA and Capital Quantitative Analyst, UBS

Stream Two: xVA
KVA Under IMM and Advanced Approaches

EDT: 11.00
GMT: 15.00
CET: 16.00

The two largest components of Capital Valuation Adjustment (KVA) are the costs of Counterparty Credit Risk (CCR) and CVA capital. For a bank using the most advanced capital models – Internal Models Method for CCR and the incoming SA-CVA capital –an accurate KVA involves forward simulating expected exposures (EE) over the lifetime of the portfolio – potentially a Monte Carlo in a Monte Carlo. We present a practical regression-based solution.

Justin Chan:

Head of Product Management, Risk, FIS

Justin Chan: Head of Product Management, Risk, FIS

Justin Chan has over 11 years of experience in financial risk management and capital markets. Mr. Chan has a deep focus on quantitative modelling in areas such as xVA, credit exposure, and collateral simulations. He is currently responsible for the Risk Quantitative Strategy and Innovation program at FIS. Prior to FIS, Mr. Chan worked at Manulife Financial as a manager in corporate risk management.

Mr. Chan studied engineering science (BASc), and theoretical physics (MSc) at University of Toronto, where he also holds a Master of Mathematical Finance (MMF) degree.

Sponsor: FIS
Stream Two: xVA
XVA, the Front Office way: Extended model, Scripting and Big compute

EDT: 12.00
GMT: 16.00
CET: 17.00

Andrey Chirikhin:

Head of Structured Credit QA, Barclays Investment Bank

Andrey Chirikhin: Head of Structured Credit QA, Barclays Investment Bank

Andrey was formerly Head of Modelling and Quantitative Analytics for L1 Treasury, part of a USD 25bn privately held investment vehicle LetterOne. Prior to LetterOne, Andrey was MD and Head of CVA and CCR quantitative Analytics at RBS. There he has created and run the front office cross asset CVA quant team. He also restructured and led the risk-side quant team charged with delivering a new Basel III compliant internal CCR methodology. The system utilizing the newly delivered methodology has won the 2013 Internal System of the year Risk award. In his 20 year career in investment banking, Andrey held several leadership and senior quant positions at Goldman Sachs, HSBC and Dresdner Kleinwort. Andrey Chirikhin holds PhD in Theoretical Statistics from Warwick University (UK), MBA from INSDEAD and MSc in Applied Mathematics from Moscow Institute for Physics and Technology (Phystech).

Since 2018 Andrey runs his own company, Quantitative Recipes, that advises on wide rage of XVA, long-term market modelling for risk and quant infrastructure.

Both Streams
xVA 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 xVA, 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: 

Andrey Chirikhin:

Head of Structured Credit QA, Barclays Investment Bank

Andrey Chirikhin: Head of Structured Credit QA, Barclays Investment Bank

Andrey was formerly Head of Modelling and Quantitative Analytics for L1 Treasury, part of a USD 25bn privately held investment vehicle LetterOne. Prior to LetterOne, Andrey was MD and Head of CVA and CCR quantitative Analytics at RBS. There he has created and run the front office cross asset CVA quant team. He also restructured and led the risk-side quant team charged with delivering a new Basel III compliant internal CCR methodology. The system utilizing the newly delivered methodology has won the 2013 Internal System of the year Risk award. In his 20 year career in investment banking, Andrey held several leadership and senior quant positions at Goldman Sachs, HSBC and Dresdner Kleinwort. Andrey Chirikhin holds PhD in Theoretical Statistics from Warwick University (UK), MBA from INSDEAD and MSc in Applied Mathematics from Moscow Institute for Physics and Technology (Phystech).

Since 2018 Andrey runs his own company, Quantitative Recipes, that advises on wide rage of XVA, long-term market modelling for risk and quant infrastructure.

Stéphane Crépey:

Professor of Mathematics Université de Paris, Laboratoire de Probabilités, Statistique et Modélisation

Stéphane Crépey: Professor of Mathematics at the Université de Paris, Laboratoire de Probabilités, Statistique et Modélisation (LPSM)

Stéphane Crépey is the Professor of Mathematics at the Université de Paris, Laboratoire de Probabilités, Statistique et Modélisation (LPSM). Formerly professor at the Mathematics Department of University of Evry (France), head of Probability and Mathematical Finance and head of the Engineering and Finance branch (M2IF) of the Paris-Saclay Master Program in Financial Mathematics. His research interests are financial modeling, counterparty and credit risk, numerical finance, as well as related mathematical topics in the fields of backward stochastic differential equations and partial differential equations. He is the author of numerous research papers and two books: “Financial Modeling: A Backward Stochastic Differential Equations Perspective” (S. Crépey, Springer Finance Textbook Series, 2013) and “Counterparty Risk and Funding, a Tale of Two Puzzles” (S. Crépey, T. Bielecki and D. Brigo, Chapman & Hall/CRC Financial Mathematics Series, 2014).

He is an associate editor of SIAM Journal on Financial Mathematics, International Journal of Theoretical and Applied Finance, and a member of the scientific council of the French financial markets authority (AMF). Stéphane Crépey graduated from ENSAE and he holds a PhD in applied mathematics from Ecole Polytechnique and INRIA Sophia Antipolis.

Ignacio Ruiz:

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.

Ignacio has several publications in the space of quantitative risk management and pricing. He has also published a comprehensive guide to the subject of XVA Desks and Risk Management.

He holds a PhD in nano-physics from Cambridge University.

Justin Chan:

Head of Product Management, Risk, FIS

Justin Chan: Head of Product Management, Risk, FIS

Justin Chan has over 11 years of experience in financial risk management and capital markets. Mr. Chan has a deep focus on quantitative modelling in areas such as xVA, credit exposure, and collateral simulations. He is currently responsible for the Risk Quantitative Strategy and Innovation program at FIS. Prior to FIS, Mr. Chan worked at Manulife Financial as a manager in corporate risk management.

Mr. Chan studied engineering science (BASc), and theoretical physics (MSc) at University of Toronto, where he also holds a Master of Mathematical Finance (MMF) degree.

Dmitri Goloubentsev:

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.

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