World Business StrategiesServing the Global Financial Community since 2000

Thursday 17th October

Stream Chair:

Tony Guida:

Executive Director – Senior Quant Research, RAM Active Investments

Tony Guida: Executive Director – Senior Quant Research, RAM Active Investments

Tony Guida is Executive Director – Senior Quant Research at RAM Active Investments. Before this, Tony was a Senior Investment Manager in quantitative equity at the investment manager of a major UK pension fund in London, where he managed multifactor systematic equity portfolios. During his career, he held such positions as senior consultant for smart beta and risk allocation at EDHEC RISK Scientific Beta and senior research and investment committee for Minimum Variance Strategies, where he led the factor investing research group for institutional clients, and a regular speaker at quant conferences.

08.00 - 09.00
Registration and Morning Welcome Coffee
09.00 - 09.45
All Streams
Keynote: The Perils of Parameterization
  • Market-makers adopt parametric forms. How consistent is it?
  • The geometry of arbitrage. Separating today from tomorrow’s manifold
  • The problem with recalibration. Arbitrage in Black-Scholes and Heston models
  • Does the FX market know that high strike implied variance should never increase?

Abstract:
Automation, risk management and taste for Markov models lead markets to adopt parametric forms, for volatility for instance. It means that in the space of asset price vectors, the possibles states at a future date lie on a low dimensional manifold that sometimes can be separated from the current price vector by a hyperplane, creating an arbitrage. We illustrate this principle with several situations (European type profiles, sticky strike assumption, term structure parameterization, recalibration issues with Black-Scholes, Heston and SABR models). We show that if every day the implied variance, defined as the square of implied volatility times the residual maturity, converges as strikes go to infinity (common assumption in FX options), this level can never go up. In the case of a market that uses a Black-Scholes model every day (flat volatility surface every day but its level may change from one day to the next), we construct explicitely a portfolio of options that gains in value whenever the volatility level has changed, at any time before the first maturity, for any spot price.

Bruno Dupire:

Head of Quantitative Research, Bloomberg L.P.

Bruno Dupire: Head of Quantitative Research, Bloomberg L.P.

Bruno Dupire is head of Quantitative Research at Bloomberg L.P., which he joined in 2004. Prior to this assignment in New York, he has headed the Derivatives Research teams at Société Générale, Paribas Capital Markets and Nikko Financial Products where he was a Managing Director. He is best known for having pioneered the widely used Local Volatility model (simplest extension of the Black-Scholes-Merton model to fit all option prices) in 1993 and the Functional Itô Calculus (framework for path dependency) in 2009. He is a Fellow and Adjunct Professor at NYU and he is in the Risk magazine “Hall of Fame”. He is the recipient of the 2006 “Cutting edge research” award of Wilmott Magazine and of the Risk Magazine “Lifetime Achievement” award for 2008.

09.45 - 10.45
All Streams
Machine Learning, AI & Quantum Computing in Quantitative Finance Panel

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

Bruno Dupire:

Head of Quantitative Research, Bloomberg L.P.

Bruno Dupire: Head of Quantitative Research, Bloomberg L.P.

Bruno Dupire is head of Quantitative Research at Bloomberg L.P., which he joined in 2004. Prior to this assignment in New York, he has headed the Derivatives Research teams at Société Générale, Paribas Capital Markets and Nikko Financial Products where he was a Managing Director. He is best known for having pioneered the widely used Local Volatility model (simplest extension of the Black-Scholes-Merton model to fit all option prices) in 1993 and the Functional Itô Calculus (framework for path dependency) in 2009. He is a Fellow and Adjunct Professor at NYU and he is in the Risk magazine “Hall of Fame”. He is the recipient of the 2006 “Cutting edge research” award of Wilmott Magazine and of the Risk Magazine “Lifetime Achievement” award for 2008.

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.

Tony Guida:

Executive Director – Senior Quant Research, RAM Active Investments

Tony Guida: Executive Director – Senior Quant Research, RAM Active Investments

Tony Guida is Executive Director – Senior Quant Research at RAM Active Investments. Before this, Tony was a Senior Investment Manager in quantitative equity at the investment manager of a major UK pension fund in London, where he managed multifactor systematic equity portfolios. During his career, he held such positions as senior consultant for smart beta and risk allocation at EDHEC RISK Scientific Beta and senior research and investment committee for Minimum Variance Strategies, where he led the factor investing research group for institutional clients, and a regular speaker at quant conferences.

Artur Sepp:

Head of Research, Quantica Capital AG

Artur Sepp: Head of Research, Quantica Capital AG

Artur Sepp is Head of Research at Quantica Capital AG in Zurich focusing on systematic data-driven trading strategies. Artur has extensive experience working as a Quantitative Strategist in leading roles since 2006. Prior to joining Quantica, Artur worked at Julius Baer in Zurich developing algorithmic solutions and strategies for the wealth management and portfolio advisory. Before, Artur worked as a front office quant strategist for equity and credit derivatives trading at Bank of America Merrill Lynch in London and Merrill Lynch in New York.  Artur has a PhD in Statistics, an MSc in Industrial Engineering from Northwestern University, and a BA in Mathematical Economics. Artur’s research area and expertise are on econometric data analysis, machine learning, and computational methods with their applications for quantitative trading strategies and asset allocation. He is the author and co-author of several research articles on quantitative finance published in leading journals and he is known for his contributions to stochastic volatility and credit risk modelling. Artur is a member of the editorial board of the Journal of Computational Finance.

Blanka Horvath:

Honorary Lecturer, Department of Mathematics, Imperial College London

Blanka Horvath: Honorary Lecturer, Department of Mathematics, Imperial College London

Blanka is a Honorary Lecturer in the Department of Mathematics at Imperial College London and a Lecturer at King’s College London. Her research interests are in the area of Stochastic Analysis and Mathematical Finance.

Her interests include asymptotic and numerical methods for option pricing, smile asymptotics for local- and stochastic volatility models (the SABR model and fractional volatility models in particular), Laplace methods on Wiener space and heat kernel expansions.

Blanka completed her PhD in Financial Mathematics at ETHZürich with Josef Teichmann and Johannes Muhle-Karbe. She holds a Diploma in Mathematics from the University of Bonn and an MSc in Economics from the University of Hong Kong.

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.

10.45 - 11.15
Morning Break and Networking Opportunities
11.15 - 12.00
Machine Learning & Quantum Computing Techniques Stream
Reverse Quantum Annealing Approaches to Portfolio Optimization Problems
  • Finding optimal parameters for the reverse quantum annealing protocol

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.

12.00 - 12.45
Machine Learning & Quantum Computing Techniques Stream
Applying Machine Learning for Troubleshooting CVA Exposure Calculation
  • Applying convolutional neural network to characterizing and troubleshooting CVA exposures used in XVA and Risk.
  • How we choose the model specification to strike a balance between model performance and decision speed.
  • Compare the model performance with human analyst.
  • Possible extension for this model to other area like FRTB.

Shengyao Zhu:

Senior Quantitative Analyst, XVA Trading Desk, Nordea

Shengyao Zhu: Senior Quantitative Analyst, XVA Trading Desk, Nordea

Shengyao currently works as a senior quantitative analyst at Nordea XVA trading desk. Before this, Shengyao worked in different banks in Europe and Asia as quantitative analyst for market risk and counterparty credit risk.  Shengyao hold a master degree of mathematical modelling from Technical University of Denmark and a Bachelor degree from Central University of Finance and Economics, Beijing China.

12.45 - 14.00
Lunch
14.00 - 14.45
Machine Learning & Quantum Computing Techniques Stream
Identification and Forecast of Market Regimes using Machine Learning
  • Applying Hidden Markov Models (HMM) to identify market regimes (bull/bear/range etc)
  • Specification and estimation of HMMs using Unsupervised Learning
  • Forecasting of likelihoods of regimes at different horizons
  • Applications to systematic trading strategies

(To be confirmed)

Artur Sepp:

Head of Research, Quantica Capital AG

Artur Sepp: Head of Research, Quantica Capital AG

Artur Sepp is Head of Research at Quantica Capital AG in Zurich focusing on systematic data-driven trading strategies. Artur has extensive experience working as a Quantitative Strategist in leading roles since 2006. Prior to joining Quantica, Artur worked at Julius Baer in Zurich developing algorithmic solutions and strategies for the wealth management and portfolio advisory. Before, Artur worked as a front office quant strategist for equity and credit derivatives trading at Bank of America Merrill Lynch in London and Merrill Lynch in New York.  Artur has a PhD in Statistics, an MSc in Industrial Engineering from Northwestern University, and a BA in Mathematical Economics. Artur’s research area and expertise are on econometric data analysis, machine learning, and computational methods with their applications for quantitative trading strategies and asset allocation. He is the author and co-author of several research articles on quantitative finance published in leading journals and he is known for his contributions to stochastic volatility and credit risk modelling. Artur is a member of the editorial board of the Journal of Computational Finance.

14.45 - 15.30
Machine Learning & Quantum Computing Techniques Stream
Topic to be confirmed

Tony Guida:

Executive Director – Senior Quant Research, RAM Active Investments

Tony Guida: Executive Director – Senior Quant Research, RAM Active Investments

Tony Guida is Executive Director – Senior Quant Research at RAM Active Investments. Before this, Tony was a Senior Investment Manager in quantitative equity at the investment manager of a major UK pension fund in London, where he managed multifactor systematic equity portfolios. During his career, he held such positions as senior consultant for smart beta and risk allocation at EDHEC RISK Scientific Beta and senior research and investment committee for Minimum Variance Strategies, where he led the factor investing research group for institutional clients, and a regular speaker at quant conferences.

15.30 - 16.00
Afternoon Break and Networking Opportunities
16.00 - 16.45
Machine Learning & Quantum Computing Techniques Stream
Topic to be confirmed

Presenter to be confirmed

16.45 - 17.30
Machine Learning & Quantum Computing Techniques Stream
Deep Learning Volatility

We present a consistent neural network based calibration method for a number of volatility models-including the rough volatility family-that performs the calibration task within a few milliseconds for the full implied volatility surface.
The aim of neural networks in this work is an off-line approximation of complex pricing functions, which are difficult to represent or time-consuming to evaluate by other means. We highlight how this perspective opens new horizons for quantitative modelling: The calibration bottleneck posed by a slow pricing of derivative contracts is lifted. This brings several model families (such as rough volatility models) within the scope of applicability in industry practice. As customary for machine learning, the form in which information from available data is extracted and stored is crucial for network performance. With this in mind we discuss how our approach addresses the usual challenges of machine learning solutions in a financial context (availability of training data, interpretability of results for regulators, control over generalisation errors). We present specific architectures for price approximation and calibration and optimize these with respect different objectives regarding accuracy, speed and robustness. We also find that including the intermediate step of learning pricing functions of (classical or rough) models before calibration significantly improves network performance compared to direct calibration to data.

Blanka Horvath:

Honorary Lecturer, Department of Mathematics, Imperial College London

Blanka Horvath: Honorary Lecturer, Department of Mathematics, Imperial College London

Blanka is a Honorary Lecturer in the Department of Mathematics at Imperial College London and a Lecturer at King’s College London. Her research interests are in the area of Stochastic Analysis and Mathematical Finance.

Her interests include asymptotic and numerical methods for option pricing, smile asymptotics for local- and stochastic volatility models (the SABR model and fractional volatility models in particular), Laplace methods on Wiener space and heat kernel expansions.

Blanka completed her PhD in Financial Mathematics at ETHZürich with Josef Teichmann and Johannes Muhle-Karbe. She holds a Diploma in Mathematics from the University of Bonn and an MSc in Economics from the University of Hong Kong.

20.00 -
Gala Dinner

The Gala Dinner is complimentary for all conference delegates.

Location to be confirmed.

Thursday 17th October

08.00 - 09.00
Registration and Morning Welcome Coffee
09.00 - 09.45
All Streams
Keynote: The Perils of Parameterization
  • Market-makers adopt parametric forms. How consistent is it?
  • The geometry of arbitrage. Separating today from tomorrow’s manifold
  • The problem with recalibration. Arbitrage in Black-Scholes and Heston models
  • Does the FX market know that high strike implied variance should never increase?

Abstract:
Automation, risk management and taste for Markov models lead markets to adopt parametric forms, for volatility for instance. It means that in the space of asset price vectors, the possibles states at a future date lie on a low dimensional manifold that sometimes can be separated from the current price vector by a hyperplane, creating an arbitrage. We illustrate this principle with several situations (European type profiles, sticky strike assumption, term structure parameterization, recalibration issues with Black-Scholes, Heston and SABR models). We show that if every day the implied variance, defined as the square of implied volatility times the residual maturity, converges as strikes go to infinity (common assumption in FX options), this level can never go up. In the case of a market that uses a Black-Scholes model every day (flat volatility surface every day but its level may change from one day to the next), we construct explicitely a portfolio of options that gains in value whenever the volatility level has changed, at any time before the first maturity, for any spot price.

Bruno Dupire:

Head of Quantitative Research, Bloomberg L.P.

Bruno Dupire: Head of Quantitative Research, Bloomberg L.P.

Bruno Dupire is head of Quantitative Research at Bloomberg L.P., which he joined in 2004. Prior to this assignment in New York, he has headed the Derivatives Research teams at Société Générale, Paribas Capital Markets and Nikko Financial Products where he was a Managing Director. He is best known for having pioneered the widely used Local Volatility model (simplest extension of the Black-Scholes-Merton model to fit all option prices) in 1993 and the Functional Itô Calculus (framework for path dependency) in 2009. He is a Fellow and Adjunct Professor at NYU and he is in the Risk magazine “Hall of Fame”. He is the recipient of the 2006 “Cutting edge research” award of Wilmott Magazine and of the Risk Magazine “Lifetime Achievement” award for 2008.

09.45 - 10.45
All Streams
Machine Learning & AI in Quantitative Finance Panel

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

Bruno Dupire:

Head of Quantitative Research, Bloomberg L.P.

Bruno Dupire: Head of Quantitative Research, Bloomberg L.P.

Bruno Dupire is head of Quantitative Research at Bloomberg L.P., which he joined in 2004. Prior to this assignment in New York, he has headed the Derivatives Research teams at Société Générale, Paribas Capital Markets and Nikko Financial Products where he was a Managing Director. He is best known for having pioneered the widely used Local Volatility model (simplest extension of the Black-Scholes-Merton model to fit all option prices) in 1993 and the Functional Itô Calculus (framework for path dependency) in 2009. He is a Fellow and Adjunct Professor at NYU and he is in the Risk magazine “Hall of Fame”. He is the recipient of the 2006 “Cutting edge research” award of Wilmott Magazine and of the Risk Magazine “Lifetime Achievement” award for 2008.

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.

Tony Guida:

Executive Director – Senior Quant Research, RAM Active Investments

Tony Guida: Executive Director – Senior Quant Research, RAM Active Investments

Tony Guida is Executive Director – Senior Quant Research at RAM Active Investments. Before this, Tony was a Senior Investment Manager in quantitative equity at the investment manager of a major UK pension fund in London, where he managed multifactor systematic equity portfolios. During his career, he held such positions as senior consultant for smart beta and risk allocation at EDHEC RISK Scientific Beta and senior research and investment committee for Minimum Variance Strategies, where he led the factor investing research group for institutional clients, and a regular speaker at quant conferences.

Artur Sepp:

Head of Research, Quantica Capital AG

Artur Sepp: Head of Research, Quantica Capital AG

Artur Sepp is Head of Research at Quantica Capital AG in Zurich focusing on systematic data-driven trading strategies. Artur has extensive experience working as a Quantitative Strategist in leading roles since 2006. Prior to joining Quantica, Artur worked at Julius Baer in Zurich developing algorithmic solutions and strategies for the wealth management and portfolio advisory. Before, Artur worked as a front office quant strategist for equity and credit derivatives trading at Bank of America Merrill Lynch in London and Merrill Lynch in New York.  Artur has a PhD in Statistics, an MSc in Industrial Engineering from Northwestern University, and a BA in Mathematical Economics. Artur’s research area and expertise are on econometric data analysis, machine learning, and computational methods with their applications for quantitative trading strategies and asset allocation. He is the author and co-author of several research articles on quantitative finance published in leading journals and he is known for his contributions to stochastic volatility and credit risk modelling. Artur is a member of the editorial board of the Journal of Computational Finance.

Blanka Horvath:

Honorary Lecturer, Department of Mathematics, Imperial College London

Blanka Horvath: Honorary Lecturer, Department of Mathematics, Imperial College London

Blanka is a Honorary Lecturer in the Department of Mathematics at Imperial College London and a Lecturer at King’s College London. Her research interests are in the area of Stochastic Analysis and Mathematical Finance.

Her interests include asymptotic and numerical methods for option pricing, smile asymptotics for local- and stochastic volatility models (the SABR model and fractional volatility models in particular), Laplace methods on Wiener space and heat kernel expansions.

Blanka completed her PhD in Financial Mathematics at ETHZürich with Josef Teichmann and Johannes Muhle-Karbe. She holds a Diploma in Mathematics from the University of Bonn and an MSc in Economics from the University of Hong Kong.

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.

10.45 - 11.15
Morning Break and Networking Opportunities
Stream Chair: To be confirmed

Chair

11.15 - 12.00
Volatility & Modelling Techniques Stream
Optimal Investment Strategy in Stochastic and Local Volatility Models
  • We revisit the classical Merton optimal allocation problem
  • We consider local and stochastic volatility models
  • Significant corrections to the Merton ratio arise from hard to observe behaviour of the variance process around zero
  • Adjustment to the myopic Merton ratio can be largely deduced from observed option prices
  • Deep learning as an approach to determine model-free optimal investment strategy

Vladimir Piterbarg:

MD, Head of Quantitative Analytics and Quantitative Development, NatWest Markets

Vladimir Piterbarg: MD, Head of Quantitative Analytics and Quantitative Development at NatWest Markets

12.00 - 12.45
Volatility & Modelling Techniques Stream
Topic to be confirmed

Adolfo Montoro:

Global Head of Market Data Strategy & Analytics, Deutsche Bank

Adolfo Montoro: Director, Global Head of Market Data Strategy & Analytics, Market Valuation Risk Management Deutsche Bank

Adolfo Montoro FRM, is a Director within Deutsche Bank’s Market Risk Management & Risk Methodology department in London. He currently leads the Market Data Strategy and Analytics team and represents DB in the Industry FRTB Working Group supporting elements of the FRTB implementation and advocacy for the Bank over the last five years. Previously he has been in charge of the Strategic implementation of Full Revaluation-based suite of VaR model ensuring as well the adequacy of quantitative methodologies used for market risk management and regulatory purposes (Pillar I and II). He has earned an MSc in Risk Management from Bocconi University, Italy, and graduated with a degree in economics (with honours) from Universita’ della Calabria, Italy. He has earned his Financial Risk Manager (FRM) certification in 2005. Adolfo is currently affiliated with the Global Association of Risk Professionals, where he serves both as a Regional Director for the UK Chapter as well as member of the FRM Committee.

12.45 - 14.00
Lunch
14.00 - 14.45
Volatility & Modelling Techniques Stream
Payoff Scripting Languages: Sung and Unsung Glories and Next Generation
  • Knowledge: There is (i). what you know, (ii). what you know you don’t know, and (iii). what you don’t know you don’t know
  • Scripting languages and exotic derivatives
  • Scripting languages and XVA
  • Scripting languages and AAD and regulatory capital
  • Scripting languages and transactions, trade life cycle, back-office, and anti-money laundering

Jesper Andreasen: 

Kwant Daddy! Global Head of Quantitative Research, Saxo Bank

Jesper Andreasen (Kwant Daddy): 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.

14.45 - 15.30
Volatility & Modelling Techniques Stream
Quantifying Model Performance
  • Introduction: known issues with models
  • Payoff replication quality as an objective measure of the hedging performance of a model
  • Hedging quality criteria and its numerical expression via regression
  • Numerical experiments
    • Take the real-world 3Y path of USD/EUR
    • Create different hedging strategies (Heston, BS etc.) for both European and exotic options
    • Compare them using our model criteria and the standard P&L analysis

Conclusion: a new efficient model performance criteria for the back-testing

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.

15.30 - 16.00
Afternoon Break and Networking Opportunities
16.00 - 16.45
Volatility & Modelling Techniques Stream
Topic to be confirmed

Presenter to be confirmed

16.45 - 17.30
Volatility & Modelling Techniques Stream
Analytical Composite Option Valuation with Full Smiles for FX and Primary Underlying

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

The Gala Dinner is complimentary for all conference delegates.

Location to be confirmed.

Thursday 17th October

08.00 - 09.00
Registration and Morning Welcome Coffee
09.00 - 09.45
All Streams
Keynote: The Perils of Parameterization
  • Market-makers adopt parametric forms. How consistent is it?
  • The geometry of arbitrage. Separating today from tomorrow’s manifold
  • The problem with recalibration. Arbitrage in Black-Scholes and Heston models
  • Does the FX market know that high strike implied variance should never increase?

Abstract:
Automation, risk management and taste for Markov models lead markets to adopt parametric forms, for volatility for instance. It means that in the space of asset price vectors, the possibles states at a future date lie on a low dimensional manifold that sometimes can be separated from the current price vector by a hyperplane, creating an arbitrage. We illustrate this principle with several situations (European type profiles, sticky strike assumption, term structure parameterization, recalibration issues with Black-Scholes, Heston and SABR models). We show that if every day the implied variance, defined as the square of implied volatility times the residual maturity, converges as strikes go to infinity (common assumption in FX options), this level can never go up. In the case of a market that uses a Black-Scholes model every day (flat volatility surface every day but its level may change from one day to the next), we construct explicitely a portfolio of options that gains in value whenever the volatility level has changed, at any time before the first maturity, for any spot price.

Bruno Dupire:

Head of Quantitative Research, Bloomberg L.P.

Bruno Dupire: Head of Quantitative Research, Bloomberg L.P.

Bruno Dupire is head of Quantitative Research at Bloomberg L.P., which he joined in 2004. Prior to this assignment in New York, he has headed the Derivatives Research teams at Société Générale, Paribas Capital Markets and Nikko Financial Products where he was a Managing Director. He is best known for having pioneered the widely used Local Volatility model (simplest extension of the Black-Scholes-Merton model to fit all option prices) in 1993 and the Functional Itô Calculus (framework for path dependency) in 2009. He is a Fellow and Adjunct Professor at NYU and he is in the Risk magazine “Hall of Fame”. He is the recipient of the 2006 “Cutting edge research” award of Wilmott Magazine and of the Risk Magazine “Lifetime Achievement” award for 2008.

09.45 - 10.45
All Streams
Machine Learning & AI in Quantitative Finance Panel

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

Bruno Dupire:

Head of Quantitative Research, Bloomberg L.P.

Bruno Dupire: Head of Quantitative Research, Bloomberg L.P.

Bruno Dupire is head of Quantitative Research at Bloomberg L.P., which he joined in 2004. Prior to this assignment in New York, he has headed the Derivatives Research teams at Société Générale, Paribas Capital Markets and Nikko Financial Products where he was a Managing Director. He is best known for having pioneered the widely used Local Volatility model (simplest extension of the Black-Scholes-Merton model to fit all option prices) in 1993 and the Functional Itô Calculus (framework for path dependency) in 2009. He is a Fellow and Adjunct Professor at NYU and he is in the Risk magazine “Hall of Fame”. He is the recipient of the 2006 “Cutting edge research” award of Wilmott Magazine and of the Risk Magazine “Lifetime Achievement” award for 2008.

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.

Tony Guida:

Executive Director – Senior Quant Research, RAM Active Investments

Tony Guida: Executive Director – Senior Quant Research, RAM Active Investments

Tony Guida is Executive Director – Senior Quant Research at RAM Active Investments. Before this, Tony was a Senior Investment Manager in quantitative equity at the investment manager of a major UK pension fund in London, where he managed multifactor systematic equity portfolios. During his career, he held such positions as senior consultant for smart beta and risk allocation at EDHEC RISK Scientific Beta and senior research and investment committee for Minimum Variance Strategies, where he led the factor investing research group for institutional clients, and a regular speaker at quant conferences.

Artur Sepp:

Head of Research, Quantica Capital AG

Artur Sepp: Head of Research, Quantica Capital AG

Artur Sepp is Head of Research at Quantica Capital AG in Zurich focusing on systematic data-driven trading strategies. Artur has extensive experience working as a Quantitative Strategist in leading roles since 2006. Prior to joining Quantica, Artur worked at Julius Baer in Zurich developing algorithmic solutions and strategies for the wealth management and portfolio advisory. Before, Artur worked as a front office quant strategist for equity and credit derivatives trading at Bank of America Merrill Lynch in London and Merrill Lynch in New York.  Artur has a PhD in Statistics, an MSc in Industrial Engineering from Northwestern University, and a BA in Mathematical Economics. Artur’s research area and expertise are on econometric data analysis, machine learning, and computational methods with their applications for quantitative trading strategies and asset allocation. He is the author and co-author of several research articles on quantitative finance published in leading journals and he is known for his contributions to stochastic volatility and credit risk modelling. Artur is a member of the editorial board of the Journal of Computational Finance.

Blanka Horvath:

Honorary Lecturer, Department of Mathematics, Imperial College London

Blanka Horvath: Honorary Lecturer, Department of Mathematics, Imperial College London

Blanka is a Honorary Lecturer in the Department of Mathematics at Imperial College London and a Lecturer at King’s College London. Her research interests are in the area of Stochastic Analysis and Mathematical Finance.

Her interests include asymptotic and numerical methods for option pricing, smile asymptotics for local- and stochastic volatility models (the SABR model and fractional volatility models in particular), Laplace methods on Wiener space and heat kernel expansions.

Blanka completed her PhD in Financial Mathematics at ETHZürich with Josef Teichmann and Johannes Muhle-Karbe. She holds a Diploma in Mathematics from the University of Bonn and an MSc in Economics from the University of Hong Kong.

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.

10.45 - 11.15
Morning Break and Networking Opportunities
Stream Chair:

Marc Henrard:

Managing Partner muRisQ Advisory and Visiting Professor, University College London

Marc Henrard: Managing Partner muRisQ Advisory and Visiting Professor, University College London

Over the last 20 years, Marc has worked in various areas of quantitative finance. Marc’s career includes Head of Quantitative Research at OpenGamma, Global Head of Interest Rate Modeling for Dexia Group, Head of Quantitative Research and Deputy Head of Interest Rate Trading at the Bank for International Settlements (BIS) and Deputy Head of Treasury Risk also at BIS.

Marc’s research focuses on interest rate modeling and risk management. More recently he focused his attention to market infrastructure (CCP and bilateral margin, exchange traded product design, regulatory costs). He publishes on a regular basis in international finance journals, and is a frequent speaker at academic and practitioner conferences. He recently authored two books: The multi-curve framework: foundation, evolution, implementation and Algorithmic Differentiation in Finance Explained.

Marc holds a PhD in Mathematics from the University of Louvain, Belgium. He has been research scientist and university lecturer in Belgium, Italy, Chile and the United Kingdom.

11.15 - 12.00
Interest Rate Reform Stream
A Quant Perspective on LIBOR Fallback
  • The current status on fallback
  • Potential difficulties with the proposed options
  • Value transfer in the fallback
  • The RFR term rates

Marc Henrard:

Managing Partner muRisQ Advisory and Visiting Professor, University College London

Marc Henrard: Managing Partner muRisQ Advisory and Visiting Professor, University College London

Over the last 20 years, Marc has worked in various areas of quantitative finance. Marc’s career includes Head of Quantitative Research at OpenGamma, Global Head of Interest Rate Modeling for Dexia Group, Head of Quantitative Research and Deputy Head of Interest Rate Trading at the Bank for International Settlements (BIS) and Deputy Head of Treasury Risk also at BIS.

Marc’s research focuses on interest rate modeling and risk management. More recently he focused his attention to market infrastructure (CCP and bilateral margin, exchange traded product design, regulatory costs). He publishes on a regular basis in international finance journals, and is a frequent speaker at academic and practitioner conferences. He recently authored two books: The multi-curve framework: foundation, evolution, implementation and Algorithmic Differentiation in Finance Explained.

Marc holds a PhD in Mathematics from the University of Louvain, Belgium. He has been research scientist and university lecturer in Belgium, Italy, Chile and the United Kingdom.

12.00 - 12.45
Interest Rate Reform Stream
Title: New Interest Rate Benchmarks: Valuation and Risk Management Issues
  • Classic vs Modern Benchmark Rates: EONIA, ESTER, EURIBOR and co.
  • Pricing and risk management with past, present and future interest rates
  • Focus on XVAs
  • Bye-Bye multi-curves?

Abstract

Once upon a time there was a classic financial world where all the interest rates were equal and considered a good proxy of the ideal risk-free rate required as basic building block of no-arbitrage pricing theory. In the present financial world after the credit crunch, multiple yield curves and volatility cubes are required to price financial instruments.

The current global reform of interest rate benchmarks is radically changing the scenario, adding more and more interest rates, with important consequences for pricing and risk management of financial instruments, but could also lead us back to a future financial world based again on a classic single-curve, few-volatility framework.

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.

Marco Scaringi:

Quant Risk Analyst, Fair Value Policy Office, Intesa Sanpaolo

Marco Scaringi: Quant Risk Analyst, Fair Value Policy Office, Intesa Sanpaolo

Marco Scaringi joined the Financial and Market Risk Management area of Intesa Sanpaolo in 2017 as quantitative analyst in the Fair Value Policy Office. His work focuses on interest rate models, XVAs, financial bubble analysis and portfolio optimization.

He holds a M.Sc. in theoretical physics from University of Milan, with a thesis on advanced statistical mechanics techniques applied to the description and detection of financial bubbles through optimization heuristics. He also holds a post lauream degree Executive Course of Quantitative Finance from MIP, Graduate School of Business, Polytechnic of Milan, with a thesis concerning interest rate and XVAs modelling.

 

12.45 - 14.00
Lunch
14.00 - 14.45
Interest Rate Reform Stream
Tractable and Arbitrage Free smile Interpolator
  • Lamperti Transform for local volatility models
  • Drift reduction and measure change
  • Application to piecewise linear local volatility

Elias Daboussi:

Quantitative Analyst, Bank of America Merrill Lynch

Elias Daboussi: Quantitative Analyst, Bank of America Merrill Lynch

Elias Daboussi is a quantitative analyst at Bank of America since 2016. After graduating from University Paris-Diderot and Supelec in 2014, he has specialized in the Rates and Hybrids area, first in the Model Risk Management Group, and now as part of the Quantitative Strategies Group.

14.45 - 15.30
Interest Rate Reform Stream
Swaptions Modelling in a World Transitioning from IBOR References to SOFR Style Indices

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.

15.30 - 16.00
Afternoon Break and Networking Opportunities
16.00 - 16.45
Interest Rate Reform Stream
Looking Forward to Backward-Looking Rates: A Modeling Framework for Terms Rates Replacing LIBOR
  • A quick overview of the LIBOR transition
  • Introducing the concept of extended zero coupon bond
  • Defining and modeling in-arrears rates
  • Modeling both forward-looking and backward-looking forward rates
  • Modeling general forward-rate dynamics
  • Introducing the generalized Forward Market Model (FMM)
  • Differences between the FMM and the classic LMM
  • The valuation of vanilla derivatives in the FMM
  • Numerical examples

Fabio Mercurio: 

Head of Quant Analytics at Bloomberg L.P.

Fabio Mercurio: Head of Quant Analytics at Bloomberg L.P.

Fabio is global head of Quantitative Analytics at Bloomberg LP, New York. His team is responsible for the research on and implementation of cross-asset analytics for derivatives pricing, XVA valuations and credit and risk management. Fabio is also adjunct professor at NYU. He has jointly authored the book ‘Interest rate models: theory and practice’ and published extensively in books and international journals, including 16 cutting-edge articles in Risk Magazine. Fabio holds a BSc in Applied Mathematics from the University of Padua, Italy, and a PhD in Mathematical Finance from the Erasmus University of Rotterdam, The Netherlands.

16.45 - 17.30
Interest Rate Reform Stream
Looking Forward to Backward-Looking Rates: Local Extensions of the Forward Market Model
  • Generalized Forward Rate Model
  • Building zero-bond price curve evolution
  • Building local bank account process
  • Local stochastic extension with HJM
  • Local stochastic extension with Cheyette
  • Implying short rate process
  • Numerical examples

Andrei Lyashenko:

Head of Market Risk and Pricing Models, Quantitative Risk Management (QRM), Inc.

Andrei Lyashenko: Head of Market Risk and Pricing Models, Quantitative Risk Management (QRM), Inc.

Andrei Lyashenko is the head of Market Risk and Pricing Models at the Quantitative Risk Management (QRM), Inc. in Chicago.  His team is responsible for research, implementation and support of pricing and risk models across multiple asset classes.  Andrei is also adjunct professor at the Illinois Institute of Technology.  Before joining the QRM in 1997, Andrei was on the mathematical faculty at the University of Illinois at Chicago and Iowa State University.  Prior to coming to the US, he conducted academic research in applied math in Russia, Japan and Italy and published numerous research papers in the area of fluid stability in major mathematical journals.  He holds a BSc in Mathematics from the Novosibirsk State University, Russia and a PhD in Mathematics from the Russian Academy of Science.

20.00 -
Gala Dinner

The Gala Dinner is complimentary for all conference delegates.

Location to be confirmed.

  • Discount Structure
  • Early bird discount
    20% until August 2nd 2019

  • Early bird discount
    10% until September 20th 2019

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

  • Conference + Workshop
    £300 Discount

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

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