World Business StrategiesServing the Global Financial Community since 2000

Thursday 27th September

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

Chair:

Jörg Kienitz:

Partner, Quaternion Risk Management

Jörg Kienitz: Partner, Quaternion Risk Management

Previously: Director FSI Assurance Deloitte GmbH and Co-Head of Quant Unit, Head of Quantitative Analytics, Dt. Postbank AG, Senior System Architect, Postbank Systems AG Financial Consultant, Reuters; Academic: Adj. Assoc. Prof. UCT, PD University of Wuppertal, PhD Math., Diploma Math. Books (Wiley): (A) Monte Carlo Frameworks in C++ (B) Financial Modelling – Theory, Implementation and Practice with Matlab Code, (Palgrave McMillan) (C) Interest Rate Derivatives Explained – Part I

Nikolai Nowaczyk:

Senior Consultant, Quaternion Risk Management

Nikolai Nowaczyk: Senior Consultant, Quaternion Risk Management

08.00 - 09.00
Registration and Morning Welcome Coffee
09.00 - 09.45
All Streams
Keynote: A Financially Motivated Extension of the Heston Model for Equities and FX
  • What Heston does well, and where it fails
  • How to fix the problems in a financially justifiable manner
  • Simultaneous fitting of the smile for many expires with constant parameters
  • Approximate analytic expressions for the Extended Heston
  • How good are the approximations?
  • How well do they fit the market?
  • What can we learn about the market price of volatility risk?

Riccardo Rebonato:

Professor of Finance, EDHEC Business School

Riccardo Rebonato: Professor of Finance, EDHEC Business School

Riccardo Rebonato is Professor of Finance at EDHEC Business School and author of journal articles and books on Mathematical Finance,covering derivatives pricing, risk management and asset allocation. Prior to this, he was Global Head of Rates and FX Analytics at PIMCO.

Academically, he is an editor of financial journals and was until 2016 a visiting lecturer at Oxford University and adjunct professor at Imperial College’s Tanaka Business School. He has served on the board of directors of the International Swaps and Derivatives Association (ISDA) and the board of trustees for the Global Association of Risk Professionals (GARP). He has been head of derivatives trading, head of research and head of market risk management at different international banks. He holds a doctorate in nuclear engineering and a PhD in condensed matter physics/science of materials from Stony Brook University, NY.

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

Moderator:

  • Paul Bilokon: Founder, CEO,Thalesians, Senior Quantitative Consultant, BNP Paribas 

Panelists:

  • Miquel Noguer Alonso: Adjunct Assistant Professor, Columbia University
  • Saeed Amen: Founder: Cuemacro
  • Alexei Kondratyev: Managing Director, Head of Data Analytics, Standard Chartered Bank
  • Jan Novotny

Topics:

  • 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

Paul Bilokon:

Founder, CEO, Thalesians & Senior Quantitative Consultant, BNP Paribas

Paul Bilokon: Founder, CEO, Thalesians & Senior Quantitative Consultant, BNP Paribas

Dr. Paul Bilokon is CEO and Founder of Thalesians Ltd and an expert in electronic and algorithmic trading across multiple asset classes, having helped build such businesses at Deutsche Bank and Citigroup. Before focussing on electronic trading, Paul worked on derivatives and has served in quantitative roles at Nomura, Lehman Brothers, and Morgan Stanley. Paul has been educated at Christ Church College, Oxford, and Imperial College. Apart from mathematical and computational finance, his academic interests include machine learning and mathematical logic.

Miquel Noguer Alonso:

Adjunct Assistant Professor, COLUMBIA UNIVERSITY

Miquel Noguer Alonso: Adjunct Assistant Professor, COLUMBIA UNIVERSITY

Miquel Noguer i Alonso is a financial markets practitioner with more than 20 years of experience in asset management, he is currently working for UBS AG (Switzerland). He worked as a CFO and CIO for a European bank from 2000 to 2006. He started his career at KPMG.

He is Adjunct Assistant Professor at Columbia University teaching Asset Allocation, Big Data in Finance, Fintech and Hedge Fund Professor at ESADE. He received an MBA and a Degree in business administration and economics in ESADE in 1993. In 2010 he earned a PhD in quantitative finance with a Summa Cum Laude distinction (UNED – Madrid Spain). He also holds the Certified European Financial Analyst diploma ( 2000 ).

His research interests range from asset allocation, big data to algorithmic trading and fintech. His academic collaborations include a visiting scholarship in Columbia University in 2013 in the Finance and Economics Department, in Fribourg University in 2010 in the mathematics department, and presentations in Indiana University, ESADE, London Business School, CAIA Association, AFI and several industry seminars.

Saeed Amen

Founder: Cuemacro

Saeed Amen: Founder: Cuemacro

Saeed has a decade of experience creating and successfully running systematic trading models at Lehman Brothers and Nomura. He is the founder of Cuemacro, Cuemacro is a company focused on understanding macro markets from a quantitative perspective. He is the author of ‘Trading Thalesians – What the ancient world can teach us about trading today’ (Palgrave Macmillan), and graduated with a first class honours master’s degree from Imperial College in Mathematics& Computer Science.

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.

Jan Novotny:

Jan Novotny: 

Jan former front office quant at HSBC in the eFX markets working on predictive analytics and alpha signals. Prior to joining HSBC team, he was working in the Centre for Econometric Analysis on the high-frequency time series econometric models and was visiting lecturer at Cass Business Group, Warwick Business School and Politecnico di Milano. He co-authored number of papers in peer-reviewed journals in Finance and Physics, contributed to several books, and presented at numerous conferences and workshops all over the world. During his PhD studies, he co-founded Quantum Finance CZ.

10.30 - 11.00
Morning Break and Networking Opportunities
11.00 - 11.45
XVA, MVA & Initial Margin Stream
The Revised Basel CVA Framework
  • The need to revise the framework
  • The consultative paper
  • The industry response
  • The final rule

Michael Pykhtin:

Manager, Quantitative Risk, U.S. Federal Reserve Board

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

Michael Pykhtin is a manager in the Quantitative Risk section at the U.S. Federal Reserve Board. Prior to joining the Board in 2009 as a senior economist, he had a successful nine-year career as a quantitative researcher at Bank of America and KeyCorp. Michael has edited “Counterparty Risk Management” (Risk Books, 2014) and “Counterparty Credit Risk Modelling” (Risk Books, 2005). He is also a contributing author to several recent edited collections. Michael has published extensively in the leading industry journals; he has been an Associate Editor of the Journal of Credit Risk since 2007. Michael is a two-time recipient of Risk Magazine’s Quant of the Year award (for 2014 and 2018). Michael holds a Ph.D. degree in Physics from the University of Pennsylvania and an M.S. degree in Physics and Applied Mathematics from Moscow Institute of Physics and Technology.

11.45 - 12.30
XVA, MVA & Initial Margin Stream
Stochastic Algorithmic Differentiation (S-AAD) and MVA

Stochastic Automatic Differentiation: AAD for Monte-Carlo Simulation: Review, Recap and New Results

  • Stochastic Automatic Differentiation – AD/AAD for Monte-Carlo Simulations
  • Automatic Differentiation of Bermudan options, XVA, etc.
    • Efficient AAD with Clean, Simple and Generic Implementation
    • Differentiation of the Exercise Boundary? Yes!
    • Differentiation of the Regression? No!
  • Memory Efficient Tapeless Implementation
    • AAD with 40 lines of code
    • Bermudan AAD with 5 lines of additional code
  • AAD for Risk Measures (VaR, ES)

Exact and Fast MVA (with or without AAD)

  • Fast AAD Forward Sensitivities (aka Future Sensitivities)
    • 5 Million Sensitivities in 10 Seconds
    • Future Sensitivities: Forward Differentiation versus Backward Differentiation
  • Application (II): Fast and Efficient ISDA-SIMM MVA
    • ISDA-SIMM
    • MVA for Swaps, Swaptions and Bermudans
  • Melting Sensitivities: Fast Forward IM Approximations (with or without AAD)
    • The Computationally Expensive Parts in MVA Calculations
    • Transformation from Model Sensitivities to Market Rate Sensitivities
    • A simple Replication Argument for MVA Approximations

Christian Fries: 

Head of Model Development, DZ Bank

Christian Fries: Head of Model Development, DZ Bank

Christian Fries is head of model development at DZ Bank’s risk control and Professor for Applied Mathematical Finance at Department of Mathematics, LMU Munich.

His current research interests are hybrid interest rate models, Monte Carlo methods, and valuation under funding and counterparty risk. His papers and lecture notes may be downloaded from http://www.christian-fries.de/finmath

He is the author of “Mathematical Finance: Theory, Modeling, Implementation”, Wiley, 2007 and runs www.finmath.net.

12.30 - 13.45
Lunch
13.45 - 14.30
XVA, MVA & Initial Margin Stream
Financial Cash-Flow Scripting: Beyond Valuation
  • Cash-flow scripting and its usage beyond structuring and valuation of exotics
  • Visitor-based scripting as a flexible, efficient and practical representation of transactions in a derivatives system
  • Application to xVA: aggregation, compression and decoration
  • xVA with collateral and branching
  • Automated risk smoothing with fuzzy logic

Antoine Savine:

Quantitative Research, Danske Bank

Antoine Savine: Quantitative Research, Danske Bank

Antoine Savine has worked for various Investment Banks since 1995, along Bruno Dupire, Leif Andersen and Marek Musiela. He was Global Head of Quantitative Research for Fixed Income, Currency and Credit Derivatives for BNP-Paribas 1999-2009, and currently works in Copenhagen for Danske Bank, where his work with Jesper Andreasen earned the In-House System of the Year 2015 Risk Award. His upcoming publications in Wiley’s Computational Finance series are dedicated to teaching the technologies implemented in those award-winning systems.

Antoine also teaches Volatility Modeling and Numerical Finance in the University of Copenhagen’s Masters of Science in Mathematics-Economics. The curriculum for his Numerical Finance lectures is being published by Wiley under the name “AAD and Parallel Simulations”.

Antoine holds a Masters from the University of Paris (Jussieu) and a PhD from the University of Copenhagen, both in Mathematics.

14.30 - 15.15
XVA, MVA & Initial Margin Stream
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

Ignacio Ruiz:

Founder & CEO, MoCaX Intelligence

Ignacio Ruiz: Founder & CEO, MoCaX Intelligence

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.

15.15 - 15.45
XVA, MVA & Initial Margin Stream
Afternoon Break and Networking Opportunities
15.45 - 16.30
XVA, MVA & Initial Margin Stream
Advanced techniques for 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

Alexandre Antonov:

Director, Standard Chartered Bank

Alexandre Antonov, Director, Standard Chartered Bank

Alexandre Antonov received his PhD degree from the Landau Institute for Theoretical Physics in 1997. He worked for Numerix during 1998-2017 and recently he has joined Standard Chartered bank in London as a director.

His activity is concentrated on modeling and numerical methods for interest rates, cross currency, hybrid, credit and CVA/FVA/MVA. AA is a published author for multiple publications in mathematical finance and a frequent speaker at financial conferences.

He has received a Quant of Year Award of Risk magazine in 2016.

16.30 - 17.15
XVA, MVA & Initial Margin Stream
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 

Jörg Kienitz:

Partner, Quaternion Risk Management

Jörg Kienitz: Partner, Quaternion Risk Management

Previously: Director FSI Assurance Deloitte GmbH and Co-Head of Quant Unit, Head of Quantitative Analytics, Dt. Postbank AG, Senior System Architect, Postbank Systems AG Financial Consultant, Reuters; Academic: Adj. Assoc. Prof. UCT, PD University of Wuppertal, PhD Math., Diploma Math. Books (Wiley): (A) Monte Carlo Frameworks in C++ (B) Financial Modelling – Theory, Implementation and Practice with Matlab Code, (Palgrave McMillan) (C) Interest Rate Derivatives Explained – Part I

Nikolai Nowaczyk:

Senior Consultant, Quaternion Risk Management

Nikolai Nowaczyk: Senior Consultant, Quaternion Risk Management

20.00 -
Gala Dinner

PLAGE BEAU RIVAGE, NICE

The Gala Dinner is complimentary for all conference delegates.

Plage Beau Rivage is a mini paradise where you can enjoy a wonderful gourmet experience. The restaurant provides a modern setting, fantastic wooded terrace and breathtaking view of the beach and sea. At the restaurant, discover a fine and inventive cuisine, combining Mediterranean flavours and Asian notes. Providing a decidedly chic and relaxed atmosphere, Plage Beau Rivage is ideal for all types of dinners and receptions.

Contact Information:

Plage Beau Rivage
107 quai des Etats-Unis
06300 Nice
France
Phone +33 (0)4 9200 4680
Website

Directions Map from Hotel Aston La Scala to Plage Beau Rivage

Menu:

Typical Niçoise pissaladière: Onions and anchovies pie

*******

Red mullet fillets, spicy Mediterranean gravy and spicy rice OR vegetarian option

*******

Apple pie with brown sugar and salted toffee ice-cream

Drinks:

Red and White Wine (AOC Côtes de Provence – Hermitage St Pons)
Water
Coffee

Thursday 27th September

Stream Chair: Paul Bilokon: Founder, CEO, Thalesians & Senior Quantitative Consultant, BNP Paribas

Chair:

Paul Bilokon:

Founder, CEO, Thalesians & Senior Quantitative Consultant, BNP Paribas

Paul Bilokon: Founder, CEO, Thalesians & Senior Quantitative Consultant, BNP Paribas

Dr. Paul Bilokon is CEO and Founder of Thalesians Ltd and an expert in electronic and algorithmic trading across multiple asset classes, having helped build such businesses at Deutsche Bank and Citigroup. Before focussing on electronic trading, Paul worked on derivatives and has served in quantitative roles at Nomura, Lehman Brothers, and Morgan Stanley. Paul has been educated at Christ Church College, Oxford, and Imperial College. Apart from mathematical and computational finance, his academic interests include machine learning and mathematical logic.

08.00 - 09.00
Registration and Morning Welcome Coffee
09.00 - 09.45
All Streams
Keynote: A Financially Motivated Extension of the Heston Model for Equities and FX
  • What Heston does well, and where it fails
  • How to fix the problems in a financially justifiable manner
  • Simultaneous fitting of the smile for many expires with constant parameters
  • Approximate analytic expressions for the Extended Heston
  • How good are the approximations?
  • How well do they fit the market?
  • What can we learn about the market price of volatility risk?

Riccardo Rebonato:

Professor of Finance, EDHEC Business School

Riccardo Rebonato: Professor of Finance, EDHEC Business School

Riccardo Rebonato is Professor of Finance at EDHEC Business School and author of journal articles and books on Mathematical Finance,covering derivatives pricing, risk management and asset allocation. Prior to this, he was Global Head of Rates and FX Analytics at PIMCO.

Academically, he is an editor of financial journals and was until 2016 a visiting lecturer at Oxford University and adjunct professor at Imperial College’s Tanaka Business School. He has served on the board of directors of the International Swaps and Derivatives Association (ISDA) and the board of trustees for the Global Association of Risk Professionals (GARP). He has been head of derivatives trading, head of research and head of market risk management at different international banks. He holds a doctorate in nuclear engineering and a PhD in condensed matter physics/science of materials from Stony Brook University, NY.

09.45 - 10.30
All Streams
Machine Learning & AI in Quantitative Finance Panel

Moderator:

  • Paul Bilokon: Founder, CEO,Thalesians, Senior Quantitative Consultant, BNP Paribas

Panelists:

  • Miquel Noguer Alonso: Adjunct Assistant Professor, Columbia University
  • Saeed Amen: Founder: Cuemacro
  • Alexei Kondratyev: Managing Director, Head of Data Analytics, Standard Chartered Bank
  • Jan Novotny

Topics:

  • 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

Paul Bilokon:

Founder, CEO, Thalesians & Senior Quantitative Consultant, BNP Paribas

Paul Bilokon: Founder, CEO, Thalesians & Senior Quantitative Consultant, BNP Paribas

Dr. Paul Bilokon is CEO and Founder of Thalesians Ltd and an expert in electronic and algorithmic trading across multiple asset classes, having helped build such businesses at Deutsche Bank and Citigroup. Before focussing on electronic trading, Paul worked on derivatives and has served in quantitative roles at Nomura, Lehman Brothers, and Morgan Stanley. Paul has been educated at Christ Church College, Oxford, and Imperial College. Apart from mathematical and computational finance, his academic interests include machine learning and mathematical logic.

Miquel Noguer Alonso:

Adjunct Assistant Professor, COLUMBIA UNIVERSITY

Miquel Noguer Alonso: Adjunct Assistant Professor, COLUMBIA UNIVERSITY

Miquel Noguer i Alonso is a financial markets practitioner with more than 20 years of experience in asset management, he is currently working for UBS AG (Switzerland). He worked as a CFO and CIO for a European bank from 2000 to 2006. He started his career at KPMG.

He is Adjunct Assistant Professor at Columbia University teaching Asset Allocation, Big Data in Finance, Fintech and Hedge Fund Professor at ESADE. He received an MBA and a Degree in business administration and economics in ESADE in 1993. In 2010 he earned a PhD in quantitative finance with a Summa Cum Laude distinction (UNED – Madrid Spain). He also holds the Certified European Financial Analyst diploma ( 2000 ).

His research interests range from asset allocation, big data to algorithmic trading and fintech. His academic collaborations include a visiting scholarship in Columbia University in 2013 in the Finance and Economics Department, in Fribourg University in 2010 in the mathematics department, and presentations in Indiana University, ESADE, London Business School, CAIA Association, AFI and several industry seminars.

Saeed Amen

Founder: Cuemacro

Saeed Amen: Founder: Cuemacro

Saeed has a decade of experience creating and successfully running systematic trading models at Lehman Brothers and Nomura. He is the founder of Cuemacro, Cuemacro is a company focused on understanding macro markets from a quantitative perspective. He is the author of ‘Trading Thalesians – What the ancient world can teach us about trading today’ (Palgrave Macmillan), and graduated with a first class honours master’s degree from Imperial College in Mathematics& Computer Science.

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.

Jan Novotny:

Jan Novotny: 

Jan former front office quant at HSBC in the eFX markets working on predictive analytics and alpha signals. Prior to joining HSBC team, he was working in the Centre for Econometric Analysis on the high-frequency time series econometric models and was visiting lecturer at Cass Business Group, Warwick Business School and Politecnico di Milano. He co-authored number of papers in peer-reviewed journals in Finance and Physics, contributed to several books, and presented at numerous conferences and workshops all over the world. During his PhD studies, he co-founded Quantum Finance CZ.

10.30 - 11.00
Morning Break and Networking Opportunities
11.00 - 11.45
Machine Learning & Quantum Computing Techniques Stream
Deep Learning in Finance – LSTN’s 
  • Modern Data Analysis
  • Times Series Models Univariate
  • Linear Factor Models
  • Multivariate Time Series
  • Modern Financial Engineering
  • Long Short Term Memory Networks
    • Results
    • Conclusions

Miquel Noguer Alonso:

Adjunct Assistant Professor, COLUMBIA UNIVERSITY

Miquel Noguer Alonso: Adjunct Assistant Professor, COLUMBIA UNIVERSITY

Miquel Noguer i Alonso is a financial markets practitioner with more than 20 years of experience in asset management, he is currently working for UBS AG (Switzerland). He worked as a CFO and CIO for a European bank from 2000 to 2006. He started his career at KPMG.

He is Adjunct Assistant Professor at Columbia University teaching Asset Allocation, Big Data in Finance, Fintech and Hedge Fund Professor at ESADE. He received an MBA and a Degree in business administration and economics in ESADE in 1993. In 2010 he earned a PhD in quantitative finance with a Summa Cum Laude distinction (UNED – Madrid Spain). He also holds the Certified European Financial Analyst diploma ( 2000 ).

His research interests range from asset allocation, big data to algorithmic trading and fintech. His academic collaborations include a visiting scholarship in Columbia University in 2013 in the Finance and Economics Department, in Fribourg University in 2010 in the mathematics department, and presentations in Indiana University, ESADE, London Business School, CAIA Association, AFI and several industry seminars.

11.45 - 12.30
Machine Learning & Quantum Computing Techniques Stream
MVA using Machine Learning Techniques 
  • Initial Margin: why and what?
  • IM Impacts on pricing (on different valuation adjustments
  • Brute force computations; more elaborate techniques: AAD, American Monte Carlo
  • How can Machine Learning help?

Gilles Artaud: 

Head of Model Internal Audit, Group Crédit Agricole

Gilles Artaud: Market and Counterparty Risk, Credit Agricole-CIB

Gilles Artaud has been working in investment banking for the last 20 years, where he held various positions within Quant, Front Office and Risk Department, working all along on many underlying types, pricing, validation, regulatory and economic capital, market risk and counterparty credit risk topics.

After setting in place the methodology and library for CCR and CVA, he lead XVA, initial margins on non-cleared transactions, and many regulatory topics.

His current “hot” topics are XVAs (CVA DVA FVA AVA MVA…) and impact of new regulatory requirements on derivatives, among which SA-CCR, NSFR, FRTB and FRTB-CVA and Artificial Intelligence technologies in Risk Management.

12.30 - 12.45
Lunch
13.45 - 14.30
Machine Learning & Quantum Computing Techniques Stream
From Artificial Intelligence to Machine Learning, from Logic to Probability

Applications of Artificial Intelligence (AI) and Machine Learning (ML) are rapidly gaining steam in quantitative finace. These terms are often used interchangeably. However, the pioneering work on AI by participants of the Dartmouth Summer Research Project — Marvin Minsky, Nathaniel Rochester, and Claude Shannon — was more symbolic than numerical, and often used the language of logic. Recent advances in ML — especially Deep Learning — are more numerical than symbolic, and often use the language of probability. In this talk we shall show how to connect these two worldviews.

Paul Bilokon:

Founder, CEO, Thalesians & Senior Quantitative Consultant, BNP Paribas

Paul Bilokon: Founder, CEO, Thalesians & Senior Quantitative Consultant, BNP Paribas

Dr. Paul Bilokon is CEO and Founder of Thalesians Ltd and an expert in electronic and algorithmic trading across multiple asset classes, having helped build such businesses at Deutsche Bank and Citigroup. Before focussing on electronic trading, Paul worked on derivatives and has served in quantitative roles at Nomura, Lehman Brothers, and Morgan Stanley. Paul has been educated at Christ Church College, Oxford, and Imperial College. Apart from mathematical and computational finance, his academic interests include machine learning and mathematical logic.

14.30 - 15.15
Machine Learning & Quantum Computing Techniques Stream
Quantum Annealing for Multi-Period XVA Reverse Stress Testing
  • Modelling
    • XVA reverse stress testing formula
    • XVA reverse stress testing as a QUBO problem ( Quadratic Unconstrained Binary Optimisation)
    • Generalisation to multi-period case
  • Optimisation using annealing
    • Quantum annealing
    • Simulated annealing
  • Applications
    • Simple portfolio of swaps
    • Firm level management

Assad Bouayoun:

Senior XVA Quantitative Consultant, HSBC Global Banking and Markets

Assad Bouayoun: Senior XVA Quantitative Consultant, HSBC Global Banking and 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).

Sheir Yarkoni:

Data Scientist, D-Wave Systems Inc

 Sheir Yarkoni: Data Scientist, D-Wave Systems Inc

15.15 - 15.45
Afternoon Break and Networking Opportunities
15.45 - 16.30
Machine Learning & Quantum Computing Techniques Stream
Second Quantization of Banks

Second Quantization of Banks

Christoph Burgard:

Head of Risk Analytics For Global Markets, Bank of America Merrill Lynch

Christoph Burgard: Head of Risk Analytics For Global Markets, Bank of America Merrill Lynch

Christoph Burgard heads the Risk Analytics team for Global Markets at Bank of America Merrill Lynch, which he joined in November 2015. Prior to this he spent 16 years at Barclays, where he was leading the Equity Derivatives and XVA front office Quantitative Analytics teams for the investment bank as well as the ALM modelling area for the bank’s treasury department. Christoph was named Risk Magazine’s Quant of the Year 2015 for his pioneering work on FVA. He has a PhD in Particle Physics from Hamburg University and was a research fellow at CERN and DESY.

16.30 - 17.15
Machine Learning & Quantum Computing Techniques Stream
Modern Infrastructure for Modern Analytics:
  • A Practical Case From the Field With ING and GridGain

Tim Wood:

Head, HPC & Model Integration, ING Financial Markets

Tim Wood: Head, HPC & Model Integration, ING Financial Markets

Tim Carley:

Managing Director EMEA, GridGain

Tim Carley: Managing Director EMEA, GridGain

20.00 -
Gala Dinner

PLAGE BEAU RIVAGE, NICE

The Gala Dinner is complimentary for all conference delegates.

Plage Beau Rivage is a mini paradise where you can enjoy a wonderful gourmet experience. The restaurant provides a modern setting, fantastic wooded terrace and breathtaking view of the beach and sea. At the restaurant, discover a fine and inventive cuisine, combining Mediterranean flavours and Asian notes. Providing a decidedly chic and relaxed atmosphere, Plage Beau Rivage is ideal for all types of dinners and receptions.

Contact Information:

Plage Beau Rivage
107 quai des Etats-Unis
06300 Nice
France
Phone +33 (0)4 9200 4680
Website

Directions Map from Hotel Aston La Scala to Plage Beau Rivage

Menu:

Typical Niçoise pissaladière: Onions and anchovies pie

*******

Red mullet fillets, spicy Mediterranean gravy and spicy rice OR vegetarian option

*******

Apple pie with brown sugar and salted toffee ice-cream

Drinks:

Red and White Wine (AOC Côtes de Provence – Hermitage St Pons)
Water
Coffee

Thursday 27th September

Stream Chair: Juliusz Jabłecki: Divisional Head, Narodowy Bank Polski

Chair:

Juliusz Jabłecki:

Divisional Head, Narodowy Bank Polski

Juliusz Jabłecki: Divisional Head, Narodowy Bank Polski

Juliusz Jabłecki is Assistant Professor of Finance at University of Warsaw, Poland, as well as head of division at Narodowy Bank Polski, Poland’s central bank. Prior to joining the central bank he worked as risk management expert for Bank Pekao. Juliusz has published numerous works in important academic and professional outlets, including Risk, Journal of Derivatives, Journal of Credit Risk, Journal of Computational Finance. He is the co-author of “Volatility as an Asset Class: Obvious Benefits and Hidden Risks” (2015). His interests include derivatives pricing, interest rate modeling, volatility and risk management. Juliusz holds a Ph.D. in economics from the University of Warsaw.

08.00 - 09.00
Registration and Morning Welcome Coffee
09.00 - 09.45
All Streams
Keynote: A Financially Motivated Extension of the Heston Model for Equities and FX
  • What Heston does well, and where it fails
  • How to fix the problems in a financially justifiable manner
  • Simultaneous fitting of the smile for many expires with constant parameters
  • Approximate analytic expressions for the Extended Heston
  • How good are the approximations?
  • How well do they fit the market?
  • What can we learn about the market price of volatility risk?

Riccardo Rebonato:

Professor of Finance, EDHEC Business School

Riccardo Rebonato: Professor of Finance, EDHEC Business School

Riccardo Rebonato is Professor of Finance at EDHEC Business School and author of journal articles and books on Mathematical Finance,covering derivatives pricing, risk management and asset allocation. Prior to this, he was Global Head of Rates and FX Analytics at PIMCO.

Academically, he is an editor of financial journals and was until 2016 a visiting lecturer at Oxford University and adjunct professor at Imperial College’s Tanaka Business School. He has served on the board of directors of the International Swaps and Derivatives Association (ISDA) and the board of trustees for the Global Association of Risk Professionals (GARP). He has been head of derivatives trading, head of research and head of market risk management at different international banks. He holds a doctorate in nuclear engineering and a PhD in condensed matter physics/science of materials from Stony Brook University, NY.

09.45 - 10.30
All Streams
Machine Learning & AI in Quantitative Finance Panel

Moderator:

  • Paul Bilokon: Founder, CEO,Thalesians, Senior Quantitative Consultant, BNP Paribas 

Panelists:

  • Miquel Noguer Alonso: Adjunct Assistant Professor, Columbia University
  • Saeed Amen: Founder: Cuemacro
  • Alexei Kondratyev: Managing Director, Head of Data Analytics, Standard Chartered Bank
  • Jan Novotny

Topics:

  • 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

Paul Bilokon:

Founder, CEO, Thalesians & Senior Quantitative Consultant, BNP Paribas

Paul Bilokon: Founder, CEO, Thalesians & Senior Quantitative Consultant, BNP Paribas

Dr. Paul Bilokon is CEO and Founder of Thalesians Ltd and an expert in electronic and algorithmic trading across multiple asset classes, having helped build such businesses at Deutsche Bank and Citigroup. Before focussing on electronic trading, Paul worked on derivatives and has served in quantitative roles at Nomura, Lehman Brothers, and Morgan Stanley. Paul has been educated at Christ Church College, Oxford, and Imperial College. Apart from mathematical and computational finance, his academic interests include machine learning and mathematical logic.

Miquel Noguer Alonso:

Adjunct Assistant Professor, COLUMBIA UNIVERSITY

Miquel Noguer Alonso: Adjunct Assistant Professor, COLUMBIA UNIVERSITY

Miquel Noguer i Alonso is a financial markets practitioner with more than 20 years of experience in asset management, he is currently working for UBS AG (Switzerland). He worked as a CFO and CIO for a European bank from 2000 to 2006. He started his career at KPMG.

He is Adjunct Assistant Professor at Columbia University teaching Asset Allocation, Big Data in Finance, Fintech and Hedge Fund Professor at ESADE. He received an MBA and a Degree in business administration and economics in ESADE in 1993. In 2010 he earned a PhD in quantitative finance with a Summa Cum Laude distinction (UNED – Madrid Spain). He also holds the Certified European Financial Analyst diploma ( 2000 ).

His research interests range from asset allocation, big data to algorithmic trading and fintech. His academic collaborations include a visiting scholarship in Columbia University in 2013 in the Finance and Economics Department, in Fribourg University in 2010 in the mathematics department, and presentations in Indiana University, ESADE, London Business School, CAIA Association, AFI and several industry seminars.

Saeed Amen

Founder: Cuemacro

Saeed Amen: Founder: Cuemacro

Saeed has a decade of experience creating and successfully running systematic trading models at Lehman Brothers and Nomura. He is the founder of Cuemacro, Cuemacro is a company focused on understanding macro markets from a quantitative perspective. He is the author of ‘Trading Thalesians – What the ancient world can teach us about trading today’ (Palgrave Macmillan), and graduated with a first class honours master’s degree from Imperial College in Mathematics& Computer Science.

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.

Jan Novotny:

Jan Novotny: 

Jan former front office quant at HSBC in the eFX markets working on predictive analytics and alpha signals. Prior to joining HSBC team, he was working in the Centre for Econometric Analysis on the high-frequency time series econometric models and was visiting lecturer at Cass Business Group, Warwick Business School and Politecnico di Milano. He co-authored number of papers in peer-reviewed journals in Finance and Physics, contributed to several books, and presented at numerous conferences and workshops all over the world. During his PhD studies, he co-founded Quantum Finance CZ.

10.30 - 11.00
Morning Break and Networking Opportunities
11.00 - 11.45
Volatility & Modelling Techniques Stream
Tough Vol
  • Fractional Brownian motion and analogies with interest rate modeling
  • Fractional volatility: definition, motivation and empirical findings
  • Expansions and short maturity limits for skew, smile, delta and digitals
  • Connection with Hawkes processes and market micro structure models
  • Numerical implementation

Jesper Andreasen: 

Global Head of Quantitative Research, Saxo Bank

Jesper Andreasen: Global Head Of Quantitative Research, Saxo Bank  

Jesper Andreasen is head of Quantitative Research at Saxo Bank in Copenhagen. Jesper has previously held senior positions in the quantitative research departments of Danske Bank, Bank of America, Nordea, and General Re Financial Products. Jesper’s recent research focusses on efficient and accurate methods for computing credit and market risk. Jesper holds a PhD in mathematical finance from Aarhus University, Denmark. He received Risk Magazine’s Quant of the Year awards in 2001 and 2012, joint with Leif Andersen and Brian Huge respectively, and is an honorary professor of mathematical finance at Copenhagen University.

11.45 - 12.30
Volatility & Modelling Techniques Stream
Value and Other Rewarded Factors for Smart Beta in Fixed Income
  • “Where there is a risk there is a reward” – is this true?
  • Defining the value factor for fixed income.
  • Extracting value using an economically justifiable proxy
  • Are risks other than duration rewarded in the yield curve?
  • Extracting compensation for slope risk using conditional strategies
  • Creating diversified smart-beta portfolio with exposure to value, level and slope factors

Riccardo Rebonato:

Professor of Finance, EDHEC Business School

Riccardo Rebonato: Professor of Finance, EDHEC Business School

Riccardo Rebonato is Professor of Finance at EDHEC Business School and author of journal articles and books on Mathematical Finance,covering derivatives pricing, risk management and asset allocation. Prior to this, he was Global Head of Rates and FX Analytics at PIMCO.

Academically, he is an editor of financial journals and was until 2016 a visiting lecturer at Oxford University and adjunct professor at Imperial College’s Tanaka Business School. He has served on the board of directors of the International Swaps and Derivatives Association (ISDA) and the board of trustees for the Global Association of Risk Professionals (GARP). He has been head of derivatives trading, head of research and head of market risk management at different international banks. He holds a doctorate in nuclear engineering and a PhD in condensed matter physics/science of materials from Stony Brook University, NY.

12.30 - 13.45
Lunch
13.45 - 14.30
Volatility & Modelling Techniques Stream
‘Local-Stochastic Volatility for Vanilla Modelling: A Tractable and Arbitrage Free Approach’ 
  • Mixing of Local and Stochastic Volatilities via Lamperti transform: replication formulae
  • Normal SABR: Jamshidian’s Trick and measure change for accurate option pricing proxies. Application to SABR Local Vol
  • Heston Local Vol

Dominique Bang: 

Director, Head of Interest Rates Vanilla Modelling, Bank of America Merrill Lynch

Dominique Bang: Director, Head of Interest Rates Vanilla Modelling, Bank of America Merrill Lynch

Dominique Bang received his PhD from Observatory of Paris (2002) in the field of ‘Mathematical Methods applied to Celestial Mechanics’. He moved into quantitative finance in 2006. Dominique has since been working in Bank Of America Merrill Lynch in the Interest Rates Quantitative Team. As a Director, he is now more focusing on Interest Rates Vanilla and Quasi-Vanilla products.

14.30 - 15.15
Volatility & Modelling Techniques Stream
Bermudan Swaptions made Simple

Do we really need a term structure model to price Bermudans?

  • Equity-like local volatility model for interest rate underlyings
  • Approximate swap rate diffusion with “short rate” and “dividend”
  • Pricing European and Bermudan swaptions without intensive yield curve modeling using techniques borrowed from the equity space
  • Numerical examples

Juliusz Jabłecki:

Divisional Head, Narodowy Bank Polski

Juliusz Jabłecki: Divisional Head, Narodowy Bank Polski

Juliusz Jabłecki is Assistant Professor of Finance at University of Warsaw, Poland, as well as head of division at Narodowy Bank Polski, Poland’s central bank. Prior to joining the central bank he worked as risk management expert for Bank Pekao. Juliusz has published numerous works in important academic and professional outlets, including Risk, Journal of Derivatives, Journal of Credit Risk, Journal of Computational Finance. He is the co-author of “Volatility as an Asset Class: Obvious Benefits and Hidden Risks” (2015). His interests include derivatives pricing, interest rate modeling, volatility and risk management. Juliusz holds a Ph.D. in economics from the University of Warsaw.

14.30 - 15.45
Afternoon Break and Networking Opportunities
15.45 - 16.30
Volatility & Modelling Techniques Stream
Smart Derivative Contracts

Smart Derivative Contracts: Detaching Transaction from Counterparty Credit Risk

  •  Review: Collateralization, Settle-to-Market, Initial Margins, Default Fund, Fire Drill
  • Smart Contract (as we know it from Cryptocurrencies)
  • A Smart Derivative Contract (with or without Blockchain and DLT).
  • Funding and Capital Cost Reductions, Operational Efficiency –  A preliminary  analysis.

Christian Fries: 

Head of Model Development, DZ Bank

Christian Fries: Head of Model Development, DZ Bank

Christian Fries is head of model development at DZ Bank’s risk control and Professor for Applied Mathematical Finance at Department of Mathematics, LMU Munich.

His current research interests are hybrid interest rate models, Monte Carlo methods, and valuation under funding and counterparty risk. His papers and lecture notes may be downloaded from http://www.christian-fries.de/finmath

He is the author of “Mathematical Finance: Theory, Modeling, Implementation”, Wiley, 2007 and runs www.finmath.net.

Peter Kohl-Landgraf

XVA Management, DZ Bank

Peter Kohl-Landgraf, XVA Management, DZ BANK

16.30 - 17.15
Volatility & Modelling Techniques Stream
Composite Average Option Valuation with Smiles
  • The economic purpose of composite options
  • Turning a multiplication into a subtraction
  • Generic bilinear option valuation: lots of digitals!
  • Solid bivariate cumulative normals
  • Does it work?

Peter Jaeckel:

Deputy Head of Quantitative Research, VTB Capital

Peter Jaeckel: Deputy Head of Quantitative Research, VTB Capital

Peter Jäckel received his DPhil from Oxford University in 1995. In 1997, he moved into quantitative analysis and financial modelling when he joined Nikko Securities. Following that he worked as a quantitative analyst at NatWest, Commerzbank Securities, ABN AMRO, and now VTB Capital where he is the Deputy Head of Quantitative Research. Peter is the author of “Monte Carlo Methods in Finance” published by John Wiley & Sons. Some of his publications can be found at WWW.JAECKEL.ORG.

20.00 -
Gala Dinner

PLAGE BEAU RIVAGE, NICE

The Gala Dinner is complimentary for all conference delegates.

Plage Beau Rivage is a mini paradise where you can enjoy a wonderful gourmet experience. The restaurant provides a modern setting, fantastic wooded terrace and breathtaking view of the beach and sea. At the restaurant, discover a fine and inventive cuisine, combining Mediterranean flavours and Asian notes. Providing a decidedly chic and relaxed atmosphere, Plage Beau Rivage is ideal for all types of dinners and receptions.

Contact Information:

Plage Beau Rivage
107 quai des Etats-Unis
06300 Nice
France
Phone +33 (0)4 9200 4680
Website

Directions Map from Hotel Aston La Scala to Plage Beau Rivage

Menu:

Typical Niçoise pissaladière: Onions and anchovies pie

*******

Red mullet fillets, spicy Mediterranean gravy and spicy rice OR vegetarian option

*******

Apple pie with brown sugar and salted toffee ice-cream

Drinks:

Red and White Wine (AOC Côtes de Provence – Hermitage St Pons)
Water
Coffee

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

  • Conference + Workshop
    £150 Discount

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

Event Email Reminder

Error