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?
    • Are we becoming more software engineers than quants?
    • 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?
    • Is there a way to make it more explainable?
  • Where do you think alternative data fits in with the vogue for machine learning?
    • Have you used alternative data?
    • Is it more for buy side or sell side.
  • 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.

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.

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.

10.45 - 11.15
Morning Break and Networking Opportunities
11.15 - 12.00
Machine Learning & Quantum Computing Techniques Stream
Quantum Computing and Quantum Machine Learning: Quant Finance Perspective
  • Gate model and analog quantum computing
  • Quantum Neural Networks
  • Boltzmann Machines and Born Machines

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
"Machine Learning + Chebyshev techniques for risk calculations: boosting each other"

The computation of risk metrics poses a huge computational challenge to banks. Many different techniques have been developed and implemented in the last few years to try and tackle the problem. We focus on Chebyshev tensors enhanced by machine learning, showing why they are such powerful pricing approximators  in risk calculations. We show how the presented unique mix of techniques can be applied in different calculations.

We illustrate with Numerical results obtained in a tier-1 bank internal systems the computational gains these techniques bring to FRTB IMA

In particular, We will give special attention on how to side-step the curse of dimensionality and how machine learning techniques can be used to boost Chebyshev tensors.

Mariano Zeron:

Head of Research and Development: MoCaX Intelligence

Mariano Zeron: Head of Research and Development: MoCaX Intelligence

Mariano leads our Research & Development work. He has vast experience in Chebyshev Spectral Decomposition, machine-learning and related disciplines, and their application to quantitative problems in the financial markets. Mariano holds a Ph.D. in Mathematics from Cambridge University.

12.45 - 14.00
Lunch
14.00 - 14.45
Machine Learning & Quantum Computing Techniques Stream
QUANTAMENTAL FACTOR INVESTING USING ALTERNATIVE DATA AND MACHINE LEARNING

To gain an edge in the markets, quantitative hedge fund managers require automated processing to quickly extract actionable information from unstructured and, increasingly, non-traditional sources of data. The nature of these “alternative data” sources presents challenges that are comfortably addressed through machine learning techniques. We illustrate the use of AI and ML techniques that help extract derived signals that have significant alpha or risk premium, which can lead to more profitable trading strategies.

This session will cover the following topics:

  • The broad application of machine learning in finance
  • Extracting sentiment from textual data — such as news stories and social media content — using machine learning algorithms
  • Generated automated data-driven insights/alerts for short-term market prediction
  • The construction of scoring models and factors from complex data sets such as supply chain graphs, options (implied volatility skew, term structure) and ESG (Environmental, Social and Governance) data
  • Use of Alternative data such as extreme weather (Cyclone, Snowfall) to quantify impact on companies that own retail stores and factories.

Arun Verma:

Quantitative Research Solutions, Bloomberg, LP

Arun Verma: Quantitative Research Solutions, Bloomberg, LP

Dr. Arun Verma joined the Bloomberg Quantitative Research group in 2003. Prior to that, he earned his Ph.D from Cornell University in the areas of computer science & applied mathematics. At Bloomberg, Mr. Verma’s work initially focused on Stochastic Volatility Models for Derivatives & Exotics pricing and hedging. More recently, he has enjoyed working at the intersection of diverse areas such as data science (for structured & unstructured data), innovative quantitative & machine learning methods and finally interactive visualizations to help reveal embedded signals in financial data.

14.45 - 15.30
Machine Learning & Quantum Computing Techniques Stream
A leap of Faith? Interpretability of Deep Learning models for stock selection

Over the last five years, Machine learning has become an intriguing yet interesting topic in quantitative investment. Amongst the numerous families and flavors of algorithms, Deep Learning stands as the one which is as fascinating as it is complex. However, the obsession from researchers for this topic is continuously increasing and results show promising territory for empirical asset pricing and stock selection purposes. Despite the significant promises of neural nets in finance, the pros of those algos seemed clouded and sometimes shadowed by the inherent complexity and the resulting lack of embedded transparency that they exhibit. Naturally, this lack of inherent “interpretability” led to the inflated “Machine Learning is a black box” myth, which turned to be a limit in the trust and then the adoption of those algos in production.

This presentation will review the following points

  • Recap the basic notions of Machine learning and their related specificities for ML in finance
  • summarising what are the notions of explainability and interpretability in the field of deep learning models for stock selection.
  • Introducing a taxonomy of methods for interpretability
  • Equity US stocks universe use case to explain the different techniques and results for:
    • global models
    • Local models
    • Partial dependence
  • Expanding the use case by focussing on two deep learning models for stock selection
    • Multi-Layer Perceptron (MLP)
    • Convolutional Neural nets (CNN)

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
Making Python Parallel with Large Datasets

Python is a great language for data science. When working with large datasets which don’t fit entirely in memory, we may need to use some different approaches. In this talk we will discuss various Python libraries which are ideal for working with large time series datasets in a pandas-like way, including dask and vaex. We shall also explore how to make computation parallel in Python, talking about the differences between threading and multiprocessing, and wrappers like concurrent futures. We shall also talk about using the very powerful celery to distribute tasks. We shall illustrate the talk with a Jupyter notebook, including examples from finance (such as using FX tick datasets).

 

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.

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.

Brillo Restaurant

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?
    • Are we becoming more software engineers than quants?
    • 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?
    • Is there a way to make it more explainable?
  • Where do you think alternative data fits in with the vogue for machine learning?
    • Have you used alternative data?
    • Is it more for buy side or sell side.
  • 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.

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.

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.

10.45 - 11.15
Morning Break and Networking Opportunities
Stream Chair:

Alessandro Gnoatto:

Professor of Mathematical Finance, Università degli Studi di Verona

Alessandro Gnoatto: Professor of Mathematical Finance, Università degli Studi di Verona

Alessandro Gnoatto is a quantitative analyst with international academic experience (PhD and Post-Doc). I have a keen interest in the use of mathematical tools for the description of financial markets. I am an experienced model developer in the context of FX and Interest Rates.

I offer a good combination of mathematical background and programming expertise, which allows me to manage projects in the domain of quantitative finance from the definition of a model up to its software implementation.

Work Experience

  • 03.2018 – present: Associate Professor of Mathematical Finance – UniVr – Verona
  • 09.2015 – 02.2018: Specialist Counterparty Credit Risk and CVA Trading – BayernLB – Munich
  • 03.2012 – 08.2015: Post-Doc researcher at Mathematics Institute – LMU University – Munich
  • 09.2011 – 02.2012: Junior Analyst Risk Management at Prometeia SpA – Bologna
  • 03.2008 – 08.2008: Internship as Derivative Analyst – Fondiaria Sai SpA – Milano
  • 06.2006 – 09.2006: Internship as Business Consultant – Studio System – Bassano del Grappa
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
"Pricing Commodity Swing Options"
  • Modelling futures with different delivery periods.
  • Implying volatility smiles.
  • Numerical investigations with swing option contracts.

Andrea Pallavicini: 

Head of Equity, FX and Commodity Models, BANCA IMI

Andrea Pallavicini: Head of Equity, FX and Commodity Models, BANCA IMI

Andrea Pallavicini is the head of equity, FX and commodity models at Banca IMI, Milan, and visiting professor at the Department of Mathematics of Imperial College, London. He holds a Ph.D. in Theoretical and Mathematical Physics from the University of Pavia for his research activity at CERN. Over the years he published several papers in financial modelling, theoretical physics and astrophysics. He is the author of the books “Credit Models and the Crisis: a journey into CDOs, copulas, correlations and dynamic models”, Wiley (2010), and “Counterparty Credit Risk, Collateral and Funding with pricing cases for all asset classes”, Wiley (2013).

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
A critical (re-)view on Interest Rate Modelling for Portfolio Simulations, Derivative Valuation, Risk Minimization, (Deep) Hedging and ALM

Portfolio simulations and valuations (e.g., xVAs) require a high dimension risk factor simulation (“market simulation”, “world simulation”).

Another area, where high dimensional risk factor simulations are required, are hedge strategies in general risk minimization problems (where “deep hedging” is an appealing method) and ALM simulations.

For such applications, interest rates are very often modelled with short rate models with low (Markov-)dimension (e.g., affine term structure models with or without stochastic volatility). In the context of xVA short rate models have seen a renaissance.

While this is mainly due to computational efficiency – reducing the amount of memory required to represent the interest rate curve (e.g. to just a few (or one) Markovian state variable) –, it comes as a surprise from a modelling perspective:

It is known that such low dimension models lead to unrealistic interest rate curve modelling and inappropriate risk management of complex derivatives (c.f. Piterbarg, Filipovic, F., etc. (at least 2003, 2007)).

In the first part of this presentation, we will discuss this issue, recalling and reviewing some known, but possibly forgotten aspects, taking risk-management of complex derivatives as an example.

In the second part, we propose an alternative based on a quasi- time-homogenous discrete term-structure model (LMM like model).

Agenda:

  • Motivation
    • Portfolio Simulations
    • Derivative Valuation
    • Bermudan Swaption Risk Management
  • Interest Rate Modelling – a review
    • -HJM, Short Rate Models / Affine Term-Structure Models, Discrete Tenor Models
  • Time-Homogenous Interest Rate Tenor Modelling
  • A quasi Time-Homogenous Discrete Term Structure Model
  • Open Source Reference Implementation
  • Numerical Results

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

15.30 - 16.00
Afternoon Break and Networking Opportunities
16.00 - 16.45
Volatility & Modelling Techniques Stream
On the Forward Smile

Abstract

Using short-time expansion techniques, we obtain analytic implied volatilities for European and forward starting options for a family of stochastic volatility models with arbitrary local volatility component and time dependent (piecewise constant) parameters. The formulas can be used to efficiently calibrate the model to European options at two expiries and to calculate the spanning forward starting option price.

Thomas Roos:

Consulting Partner, Quantitative Financial

Thomas Roos: Consulting Partner, Quantitative Financial

Thomas is an independent consultant specialising in derivatives, based in Italy. He has 20 years experience in the financial industry, including as Head of Interest Rate and Emerging Markets Quantitative Analytics at Barclays and as European Head of Interest Rate Modelling at Credit Suisse.

Thomas holds a PhD in Theoretical Physics from Cornell University.

16.45 - 17.30
Volatility & Modelling Techniques Stream
Industry-grade Function Approximation in Financial Applications
  • All maths has to be reduced to simple additions. Even multiplications.
  • Simple methods: Taylor, Padé, Inverse Taylor, Householder
  • Chebyshev, Economization, Chebyshev-Padé, Linear (Maehly) vs Nonlinear (Clenshaw-Lord)
  • Remez, Remez I/II, and all that Voodoo
  • The Russian source: Remez’s weight function
  • Taking the weight function into the problem: Remez II B
  • Examples

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.

Brillo Restaurant

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?
    • Are we becoming more software engineers than quants?
    • 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?
    • Is there a way to make it more explainable?
  • Where do you think alternative data fits in with the vogue for machine learning?
    • Have you used alternative data?
    • Is it more for buy side or sell side.
  • 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.

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.

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.

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
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.

14.45 - 15.30
Interest Rate Reform Stream
Looking Forward to Backward-Looking Rates: Completing 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.

15.30 - 16.00
Afternoon Break and Networking Opportunities
16.00 - 16.45
Interest Rate Reform Stream
IBOR Transition and linkage to the Risk & Capital Framework

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.

16.45 - 17.30
Interest Rate Reform Stream
Practical Implications from the changes to €STR and what will happen to Euribor
  • Transitioning from EONIA to €STR
  • Relevance of EONIA swaps for pension funds
  • The switch to €STR discounting
  • Transitioning from EURIBOR to €STR
  • Most likely solution for an €STR term structure
  • Implications for traded contracts

Max will discuss the practical implications of the IBOR and EONIA transition for interest rate risk hedgers like pension funds. What should we do with legacy swap portfolios and how do we transition to instruments with the new benchmarks?

Max Verheijen:

Head of Financial Markets, Cardano

Max Verheijen: Head of Financial Markets, Cardano

Max is a Director of Cardano’s Financial Markets division covering structuring, trading, reporting and control activities in financial products for institutional clients. He has over 25 years’ experience in derivatives, both on the sell and buy side.

Before joining Cardano he was vice president at the Interest Rate Derivatives Department of Treasury and Sales at ING Bank Amsterdam.
Max is specialised in regulation of financial markets and documentation, pricing and trading of financial instruments including OTC derivatives (specifically rates, inflation, equity and FX) and cash instruments.

20.00 -
Gala Dinner

The Gala Dinner is complimentary for all conference delegates.

Brillo Restaurant

  • Discount Structure
  • 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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