World Business StrategiesServing the Global Financial Community since 2000

Main Conference Day 1: Thursday 28th September

08.00 – 09.00: Registration and Morning Welcome Coffee

Morning Stream Chair:

Ioana Boier:

Ioana Boier:

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

Generative AI / Large Language Models Stream

09.00 – 09.45: Training and Taming LLMs for Quant Finance

Ioana Boier:

Ioana Boier:

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

Generative AI / Large Language Models Stream

09.45 – 10.30: An Attention-like Mechanism for Model-Agnostic Pricing of Path-Dependent Options

Blanka Horvath:

Associate Professor in Mathematical and Computational Finance, University of Oxford

Blanka Horvath: Associate Professor in Mathematical and Computational Finance, University of Oxford and Researcher, The Alan Turing Institute

Blanka research interests are in the area of Stochastic Analysis and Mathematical Finance.

Including 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.

Zacharia Issa:

Associate at BNY Mellon | PhD Candidate, King’s College London

Zacharia Issa: Associate, BNY Mellon | PhD Candidate, King’s College London

10.30 – 11.00: Morning Break and Networking Opportunities

Generative AI / Large Language Models Stream

11.00 – 11.45: Customizing Large Language Models for Quant Finance Applications

Due to the highly specialized subject matter, the leading commercial (GPT-4) and open source (Llama 2, Code Llama) LLMs are unable to provide specialized comprehension and generation suitable for quant finance applications out of the box.

In this presentation, I will describe fine-tuning and prompt engineering techniques that convert stock LLMs into specialized tools that can assist with the following model governance functions, subject to final sign-off by human analysts:

  1. Validation of trade capture using LLM comprehension of trade confirmations
  2. Validation of model documentation using LLM comprehension of source code
  3. LLM generation of model documentation and release note drafts

Alexander Sokol:

Executive Chairman and Head of Quant Research, CompatibL

Alexander Sokol: Executive Chairman and Head of Quant Research, CompatibL

Alexander Sokol is the founder, Executive Chairman, and Head of Quant Research at CompatibL, a trading and risk technology company. He is also the co-founder of Numerix, where he served as CTO from 1996 to 2003, and the co-founder of Duality Group, where he served as CTO from 2017 to 2020.

Alexander won the Quant of the Year Award in 2018 together with Leif Andersen and Michael Pykhtin, for their joint work revealing the true scale of the settlement gap risk that remains even in the presence of initial margin. Alexander’s other notable research contributions include systemic wrong-way risk (with Michael Pykhtin, Risk Magazine), joint measure models, and the local price of risk (with John Hull and Alan White, Risk Magazine), and mean reversion skew (Risk Books, 2014).

Alexander earned his BA from the Moscow Institute of Physics and Technology at the age of 18, and a PhD from the L. D. Landau Institute for Theoretical Physics at the age of 22. He was the winner of the USSR Academy of Sciences Medal for Best Student Research of the Year in 1988.

Generative AI / Large Language Models Stream

11.45 – 12.30: Documenting Models and Algorithms.

Summary: As a quant, one of the most impactful ways of mitigating model risk is by creating accurate and consistent documentation. In the present talk, I discuss how qualitative and quantitative techniques can be used to enhance the quality and effectiveness of documentation.

Agenda:

  • Why documentation matters
  • Challenges
  • Architecture of documentation
  • Technology
  • Use cases

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.

12.30 – 13.45: Lunch

Afternoon Stream Chair:

Andrey Chirikhin:

Head of Structured Credit QA, Barclays Investment Bank

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

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

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

Generative AI / Large Language Models Stream

13.45 – 14.30: Generative Models for Time Series in Finance

Miquel Noguer Alonso:

Co – Founder and Chief Science Officer, Artificial Intelligence Finance Institute – AIFI

Miquel Noguer Alonso: Co – Founder and Chief Science Officer, Artificial Intelligence Finance Institute – AIFI

Miquel Noguer is a financial markets practitioner with more than 20 years of experience in asset management, he is currently Head of Development at Global AI ( Big Data Artificial Intelligence in Finance company ) and Head on Innovation and Technology at IEF.

He worked for UBS AG (Switzerland) as Executive Director.for the last 10 years. He worked as a Chief Investment Office and CIO for Andbank from 2000 to 2006.

He is professor of Big Data in Finace at ESADE and Adjunct Professor at Columbia University teaching Asset Allocation, Big Data in Finance and Fintech. He received an MBA and a Degree in business administration and economics in ESADE in 1993. In 2010 he earned a PhD in quantitative finance with a Summa Cum Laude distinction (UNED – Madrid Spain).

Generative AI / Large Language Models Stream

14.30 – 15.15: Autoencoders and Arbitrage Free Dynamics

  • The mathematics of neural networks: approximation theorems.
  • Training neural networks: Adjoint differentiation and stochastic gradient decent.
  • Making and training your own neural networks in C++.
  • Auto encoders and yield curve dynamics: how many factors?
  • Arbitrage free dynamics and supporting stochastic processes.
  • Extensions to other asset classes.

Jesper Andreasen: 

Kwantfather! Global Head of Quantitative Research, Saxo Bank

Jesper Andreasen (Kwantfather): 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.

15.15 – 15.45: Afternoon Break and Networking Opportunities

Generative AI / Large Language Models Stream

15.45 – 16.30: AEN-VAR-AEN

Description: We show how apply traditional VAR time series methodology to the now famous data set of the IR swap rates, and how to naturally extend it by applying autoencoders both as autoregressors and dimension reduction technique for the residuals in the regular VAR setting.

Andrey Chirikhin:

Head of Structured Credit QA, Barclays Investment Bank

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

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

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

Generative AI / Large Language Models Stream

16.30 – 17.15: Ethical and Responsible Large Language Models: Challenges and Best Practices

Nicole Königstein:

Chief Data Scientist, Head of AI & Quant Research, Wyden Capital AG

Nicole Königstein: Chief Data Scientist, Head of AI & Quant Research, Wyden Capital AG

Nicole Königstein is a distinguished Data Scientist and Quantitative Researcher, currently working as Data Science and Technology Lead at impactvise, an ESG analytics company, and as Head of AI and Quantitative Research at Quantmate, an innovative FinTech startup focused on alternative data in predictive modeling. Alongside her roles in these organizations, she serves as an AI consultant across diverse industries, leading workshops and guiding companies from the conceptual stages of AI implementation through to final deployment.

As a guest lecturer, Nicole shares her expertise in Python, machine learning, and deep learning at various universities. She is a regular speaker at renowned AI and Data Science conferences, where she conducts workshops and educational sessions. In addition, she is an influential voice in the data science community, regularly reviewing books in her field and offering her insights and critiques. Nicole is also the author of the well-received online course, “Math for Machine Learning.

All Streams

17.15 – 18.00: QFC Panel

Large Language Models (LLM) in Quant Finance – A Gimmick or a Game Changer?

  • Will LLLMs help us read derivatives term sheets, UCITS documentation, and securitization rules
  • Will LLLMs help us document our models for the regulators
  • Will LLLMs help us find bugs in our pricing models
  • GPT-4 or LLAMA 2?

ML Models for Valuation, XVA, and Risk

  • How to build trustworthy ML quant models that auditors and regulators will approve
  • Due to the lack of sufficient training data, is ML in quant models truly learning or only providing a better way to interpolate

Moderator:

Alexander Sokol:

Executive Chairman and Head of Quant Research, CompatibL

Alexander Sokol: Executive Chairman and Head of Quant Research, CompatibL

Alexander Sokol is the founder, Executive Chairman, and Head of Quant Research at CompatibL, a trading and risk technology company. He is also the co-founder of Numerix, where he served as CTO from 1996 to 2003, and the co-founder of Duality Group, where he served as CTO from 2017 to 2020.

Alexander won the Quant of the Year Award in 2018 together with Leif Andersen and Michael Pykhtin, for their joint work revealing the true scale of the settlement gap risk that remains even in the presence of initial margin. Alexander’s other notable research contributions include systemic wrong-way risk (with Michael Pykhtin, Risk Magazine), joint measure models, and the local price of risk (with John Hull and Alan White, Risk Magazine), and mean reversion skew (Risk Books, 2014).

Alexander earned his BA from the Moscow Institute of Physics and Technology at the age of 18, and a PhD from the L. D. Landau Institute for Theoretical Physics at the age of 22. He was the winner of the USSR Academy of Sciences Medal for Best Student Research of the Year in 1988.

Ioana Boier:

Ioana Boier:

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

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

Ignacio Ruiz:

MoCaX Intelligence

Ignacio Ruiz: MoCaX Intelligence

Ignacio Ruiz has been the Head of Counterparty Credit Risk Measurement and Analytics, Scotiabank, the head strategist for Counterparty Credit Risk, exposure measurement, for Credit Suisse, as well as the Head of Risk Methodology, equities, for BNP Paribas. In 2010, Ignacio set up iRuiz Consulting as an independent advisory business in this field. In 2014, Ignacio founded iRuiz Technologies to develop and commercialise MoCaX Intelligence.

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

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

Peter Jaeckel:

Independent financial mathematics and analytics consultant. OTC Analytics

Peter Jaeckel: Independent financial mathematics and analytics consultant. OTC Analytics

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.

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.

Gala Dinner: Marina Restaurante 

The main building of the Beach Club is home to the Marina Restaurant, a circular restaurant with spacious windows overlooking Las Arenas Playa. The terrace extends towards the sea, a space full of charm for those who want to have a meal “al fresco” with amazing sea views.

Marina Restaurante
Calle Marina Real Juan Carlos I
46011 Valencia
Spain

Tel: +34 961 150 007
Website

Directions from SH Valencia Palace to Marina Restaurante.

08.00 – 09.00: Registration and Morning Welcome Coffee

Morning Stream Chair:

Andrew McClelland: 

Director, Quantitative Research, Numerix

Andrew McClelland: Director, Quantitative Research, Numerix

Andrew McClelland’s work at Numerix focuses on counterparty credit risk issues including valuation adjustments and counterparty exposure production for structured products. He also works on numerical methods for efficient production of risk profiles under real-world measures.

Andrew received his Ph.D. in finance at the Queensland University of Technology in financial econometrics. His research involved markets exhibiting crash feedback, option pricing, and parameter estimation using particle filtering methods. His work has been published in the Journal of Banking and Finance, the Journal of Econometrics, and the Journal of Business and Economic Statistics.

Volatility, Pricing & Modelling Stream

09.00 – 09.45: Looking Beyond SA-CCR

Michael Pykhtin:

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

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

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

Volatility, Pricing & Modelling Stream

09.45 – 10.30: Intraday TAA meets Systematic Global Macro

Blaž Žličar:

Quantitative Portfolio Mgmt & Machine Learning Research, Vice President, Deutsche Bank

Blaž Žličar: Quantitative Portfolio Mgmt & Machine Learning Research, Vice President, Deutsche Bank

Blaz Zlicar specialises in systematic alpha research across asset classes and the application of quantitative methods to short-term pattern detection problems. Prior to joining Deutsche Bank he worked as a quantitative researcher and has acquired industry experience on both the buy- and the sell-side. Blaz holds a BSc in Economics, several MSc degrees in Finance (U. of Ljubljana, U. of Amsterdam and UCL), as well as a PhD in Financial Computing and Machine Learning from the University College London.

10.30 – 11.00: Morning Break and Networking Opportunities

Volatility, Pricing & Modelling Stream

11.00 – 11.45: Comparing AAD Techniques & Performance

In this presentation, Stephan Bosch and Dmitri Goloubentsev will present a review of Bump & Revalue, Tape-Based AAD and Code Generation AAD approaches for CVA calculations and cover the following:

  • Performance gains of introducing AAD, and scalability for large portfolios in terms of speed and memory use;
  • Comparative performance of traditional tape-based vs. code generation AAD approaches for stress testing protocols and analysis;
  • How adopting AAD to the evaluation of higher-order sensitivities can deliver linear scalability with respect to the number of cross-gamma entries.

Dmitri Goloubentsev:

CTO, Head of Automatic Adjoint Differentiation, Matlogica

Dmitri Goloubentsev: CTO, Head of Automatic Adjoint Differentiation, Matlogica

Dmitri has 15 years of combined experience in model development working on C++ quant libraries. He worked as a Senior Quant Analyst in interest rate derivatives and played a leading role in delivering XVA solution at a major Canadian bank. Prior to focusing on AAD, he was responsible for construction of SIMM/MVA model. Dmitri earned his degree in Maths and Applied Maths from the Moscow State University.

Stephan Bosch:

Quantitative Developer, ING

Stephan Bosch: Quantitative Developer, ING

Volatility, Pricing & Modelling Stream

11.45 – 12.30: Projecting Exposures and Margin: Getting Risk Models and Pricing Models to Play Nice

  • The problem of projecting exposures and margin requirements off a pricing model (continuous-time, arbitrage free etc.) is well understood
  • We have a range of numerical techniques (e.g. LSMC) to do this efficiently when closed-form representations are not available
  • But what if we are simulating risk scenarios off a dedicated risk model (discrete-time PCA, GARCH etc.)?
  • Translating between the risk model and the pricing model on a per-scenario basis is complex, good candidate for fitting the inverse map via ML?

Andrew McClelland: 

Director, Quantitative Research, Numerix

Andrew McClelland: Director, Quantitative Research, Numerix

Andrew McClelland’s work at Numerix focuses on counterparty credit risk issues including valuation adjustments and counterparty exposure production for structured products. He also works on numerical methods for efficient production of risk profiles under real-world measures.

Andrew received his Ph.D. in finance at the Queensland University of Technology in financial econometrics. His research involved markets exhibiting crash feedback, option pricing, and parameter estimation using particle filtering methods. His work has been published in the Journal of Banking and Finance, the Journal of Econometrics, and the Journal of Business and Economic Statistics.

12.30 – 13.45: Lunch

Afternoon Stream Chair:

Peter Jaeckel:

Independent financial mathematics and analytics consultant. OTC Analytics

Peter Jaeckel: Independent financial mathematics and analytics consultant. OTC Analytics

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.

Volatility, Pricing & Modelling Stream

13.45 – 14.30: Learning volatility – now with mean reversion

Brian Norsk Huge:

Head of Quant, Saxo Bank

Brian Norsk Huge: Head of Quant, Saxo Bank

Brian Huge is working as the head of Quant at Saxo Bank. Before joining Saxo Bank Brian worked as a quant for 20 years in Danske Bank. Brian has a Ph.D. in Mathematical Finance from University of Copenhagen. In 2012 he was awarded Quant of the Year for his work on Volatility Interpolation and Random Grids.

Volatility, Pricing & Modelling Stream

14.30 – 15.15: Efficient Simulation of FRTB CVA Capital Costs

Sascha Geier:

Director, Head of Counterparty Risk and xVA Analytics, Commerzbank AG

Sascha Geier: Director, Head of Counterparty Risk and xVA Analytics, Commerzbank AG

15.15 – 15.45: Afternoon Break and Networking Opportunities

Volatility, Pricing & Modelling Stream

15.45 – 16.30: Algorithmic Adjoint Differentiation (AAD) for tail risk, model risk and stress testing

  • AAD is currently used in quant finance for fast greeks and valuation adjustments calculations.
  • It turns out that many other computationally intensive problems related to risk measurement, stress testing and model risk can benefit from AAD precision and speed.
  • In this presentation, we develop several such business cases and show how AAD can contribute to tail risk estimation, model risk measurement and stress testing in various framework such as climate risk.

 

Svetlana Borovkova:

Head of Quantitative Modelling, Probability & Partners. Associate Prof, Vrije Universiteit Amsterdam

Svetlana Borovkova: Head of Quantitative Modelling, Probability & Partners and Associate Professor, Vrije Universiteit Amsterdam

Dr Svetlana Borovkova is the partner and Head of Quant Modelling of risk management consulting firm Probability and Partners and an Associate Professor of Quantitative Finance and Risk Management at the  Vrije Universiteit Amsterdam. She is the author of over 60 academic and professional publications and a frequent speaker at conferences such as RiskMinds and QuantMinds. Her work encompasses a wide range of topics, ranging from derivatives pricing and risk modelling to sentiment analysis for quant investing and machine learning in quant finance. Find her work at SSRN and her columns on various finance topics in Financial Investigator.

Volatility, Pricing & Modelling Stream

16.30 – 17.15: Advanced Numerical Methods for Option Pricing

Leif Andersen:

Global Co-Head Of Quantitative Strategies Group, Bank of America

Leif Andersen: Global Co-Head Of Quantitative Strategies Group, Bank of America

Leif B. G. Andersen is the Global Co-Head of The Quantitative Strategies & Data Group at Bank of America, and is an adjunct professor at NYU’s Courant Institute of Mathematical Sciences and at CMU’s Tepper School of Business. He holds MSc’s in Electrical and Mechanical Engineering from the Technical University of Denmark, an MBA from University of California at Berkeley, and a PhD in Finance from Aarhus Business School. He was the co-recipient of Risk Magazine’s 2001 and 2018 Quant of the Year Awards, and has worked for 30 years as a quantitative researcher in the global markets area. He has authored influential research papers and books in all areas of quantitative finance, and is an Associate Editor of Journal of Computational Finance and Mathematical Finance.

All Streams

17.15 – 18.00: QFC Panel

Large Language Models (LLM) in Quant Finance – A Gimmick or a Game Changer?

  • Will LLLMs help us read derivatives term sheets, UCITS documentation, and securitization rules
  • Will LLLMs help us document our models for the regulators
  • Will LLLMs help us find bugs in our pricing models
  • GPT-4 or LLAMA 2?

ML Models for Valuation, XVA, and Risk

  • How to build trustworthy ML quant models that auditors and regulators will approve
  • Due to the lack of sufficient training data, is ML in quant models truly learning or only providing a better way to interpolate

Moderator:

Alexander Sokol:

Executive Chairman and Head of Quant Research, CompatibL

Alexander Sokol: Executive Chairman and Head of Quant Research, CompatibL

Alexander Sokol is the founder, Executive Chairman, and Head of Quant Research at CompatibL, a trading and risk technology company. He is also the co-founder of Numerix, where he served as CTO from 1996 to 2003, and the co-founder of Duality Group, where he served as CTO from 2017 to 2020.

Alexander won the Quant of the Year Award in 2018 together with Leif Andersen and Michael Pykhtin, for their joint work revealing the true scale of the settlement gap risk that remains even in the presence of initial margin. Alexander’s other notable research contributions include systemic wrong-way risk (with Michael Pykhtin, Risk Magazine), joint measure models, and the local price of risk (with John Hull and Alan White, Risk Magazine), and mean reversion skew (Risk Books, 2014).

Alexander earned his BA from the Moscow Institute of Physics and Technology at the age of 18, and a PhD from the L. D. Landau Institute for Theoretical Physics at the age of 22. He was the winner of the USSR Academy of Sciences Medal for Best Student Research of the Year in 1988.

Ioana Boier:

Ioana Boier:

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

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

Ignacio Ruiz:

MoCaX Intelligence

Ignacio Ruiz: MoCaX Intelligence

Ignacio Ruiz has been the Head of Counterparty Credit Risk Measurement and Analytics, Scotiabank, the head strategist for Counterparty Credit Risk, exposure measurement, for Credit Suisse, as well as the Head of Risk Methodology, equities, for BNP Paribas. In 2010, Ignacio set up iRuiz Consulting as an independent advisory business in this field. In 2014, Ignacio founded iRuiz Technologies to develop and commercialise MoCaX Intelligence.

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

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

Peter Jaeckel:

Independent financial mathematics and analytics consultant. OTC Analytics

Peter Jaeckel: Independent financial mathematics and analytics consultant. OTC Analytics

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.

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.

Gala Dinner: Marina Restaurante 

The main building of the Beach Club is home to the Marina Restaurant, a circular restaurant with spacious windows overlooking Las Arenas Playa. The terrace extends towards the sea, a space full of charm for those who want to have a meal “al fresco” with amazing sea views.

Marina Restaurante
Calle Marina Real Juan Carlos I
46011 Valencia
Spain

Tel: +34 961 150 007
Website

Directions from SH Valencia Palace to Marina Restaurante.

08.00 – 09.00: Registration and Morning Welcome Coffee

Morning Stream Chair:

Saeed Amen

Turnleaf Analytics / Cuemacro / Visiting Lecturer at QMUL

Saeed Amen: Turnleaf Analytics / Cuemacro / Visiting Lecturer at QMUL

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.

Machine Learning, Alt Data & Deep Learning Stream

09.00 – 09.45: Unlocking Efficiency in Risk Calculations through Machine Learning

The realm of risk calculations grapples with a recurring challenge – the portfolio revaluation dilemma.

From XVA and PFE to IMM, IMA-FRTB, VaR, and Stress-testing, the need for a high number of derivative valuations exacts a toll on both computational resources and operational speed.

Enter Machine Learning methods, a potent solution to this challenge. When harnessed effectively, Machine Learning methodologies hold the promise of significantly enhancing computational efficiency, all while maintaining accuracy.

In this presentation, Ignacio shares his insights and experience in this field. He will cover a spectrum of topics:

  • Various categories of learning methods
  • Navigating the intricate challenge of dimensionality
  • Techniques for dimensionality reduction
  • Unveiling the potency of Chebyshev Tensors
  • Optimal use of Deep Neural Networks
  • Tensors Train formats for optimal outcomes
  • Chebyshev Sliders to fine-tune results
  • Real-world numerical illustrations, including
    • Vanilla Swaps
    • Black-Scholes Options
    • Barrier Options
    • Bermuda Swaptions
    • TARFs (Target Redemption Forwards)
    • Autocallable Options
    • Simulation of Sensitivities and SIMM (Standard Initial Margin Model)

Ignacio Ruiz:

MoCaX Intelligence

Ignacio Ruiz: MoCaX Intelligence

Ignacio Ruiz has been the Head of Counterparty Credit Risk Measurement and Analytics, Scotiabank, the head strategist for Counterparty Credit Risk, exposure measurement, for Credit Suisse, as well as the Head of Risk Methodology, equities, for BNP Paribas. In 2010, Ignacio set up iRuiz Consulting as an independent advisory business in this field. In 2014, Ignacio founded iRuiz Technologies to develop and commercialise MoCaX Intelligence.

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

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

Machine Learning, Alt Data & Deep Learning Stream

09.45 – 10.30: Causal Discovery in Reinforcement Learning and its Applications in Finance

Topic and Presenter to be confirmed.

Leila Korbosli:

Quantitative Analyst, UBS

Leila Korbosli: Quantitative Analyst, UBS

Leila is currently a quantitative analyst at UBS where she is part of the firm’s Advanced Cloud-based Quantitative Analytics ACQA Platform, in charge of building and delivering cutting edge trading tools. She started her career in Quantitative Research at Lehman Brothers in 2007 focusing on IR exotics and hybrids and then built an extensive cross-asset modelling experience on the sell-side across different asset classes as a quant and a trader in Rates/FX, Credit and XVA. Leila holds an engineering degree in Applied Mathematics and Computer Science from ENSIMAG (2006) and a masters in Probability and Finance from Paris VI-Ecole Polytechnique (2007). She is the co-author of the book “Global Derivatives: Products, Theory and Practice”, World Scientific.

10.30 – 11.00: Morning Break and Networking Opportunities

Machine Learning, Alt Data & Deep Learning Stream

11.00 – 11.45: Forecasting Inflation with Machine Learning and alt data

Inflation has become a key topic in recent months. In our talk, we discuss how to approach the topic from a machine learning perspective, and also how to incorporate alternative data in the process. We will also discuss some specific use cases for inflation forecasting, in particular a systematic trading rule for FX.

Saeed Amen

Turnleaf Analytics / Cuemacro / Visiting Lecturer at QMUL

Saeed Amen: Turnleaf Analytics / Cuemacro / Visiting Lecturer at QMUL

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.

Machine Learning, Alt Data & Deep Learning Stream

11.45 – 12.30: An Inflation-Based Asset Allocation Strategy With Sentiment Data

Abstract: We develop a general approach to perform a multi-asset allocation strategy, which depends on inflation nowcasts enhanced with RavenPack sentiment analytics. Specifically, we build a systematic inflation hedging strategy that maximizes the multiperiod mean-variance criterion of the portfolio to invest in three financial assets (equity, commodity, bond), by exploiting inflation nowcasts (headline and core inflation). We test the robustness of the strategy via backtest, by investing in the US equity market, the US bond with 10 years maturity and six conventional commodity indexes. We also extend the proposed strategy to a volatility targeting approach, which searches for risk tolerance parameters of the risky assets given a risk preference of the investor in terms of volatility.

Anmar Al-Wakil:

Head of Quantitative Strategies, RavenPack

Anmar Al-Wakil: Head of Quantitative Strategies, RavenPack

Anmar is the Head of Quantitative Strategies at RavenPack. Before joining RavenPack in 2021, he worked as a quantitative researcher at Natixis Investment Managers for nearly 8 years, where he developed systematic investment strategies within the technology platform. At RavenPack, Anmar excavates cutting-edge insights from news sentiment to elaborate alpha-generating strategies across equity, credit, and derivatives instruments. In addition, he advices some of the world’s top hedge funds and asset managers on the use of NLP-driven analytics in finance.

He holds a PhD in Quantitative Finance from the University of Paris Dauphine-PSL along with a Master’s degree in Mathematical Finance. Anmar has written articles in portfolio selection and machine learning that were presented in multiple conferences. His article about asset pricing won the Best Doctoral Paper of the Multinational Finance Society. He is also a part-time Associate Professor at the University of Paris-Est where he heads the MSc in Portfolio Management.

12.30 – 13.45: Lunch

Afternoon Stream Chair:

Jason Charlesworth:

NAG and Founder of Zettamatics

Jason Charlesworth: NAG and Founder of Zettamatics

Jason Charlesworth holds a PhD in theoretical physics from Cambridge University. He has worked in academic research in London and Cornell as well as industrial research in speech recognition, natural language processing, and machine learning. With over 20 years leading front-office quant and quant dev groups, he is an expert in applied computational mathematics and high-performance computing with a particular focus on novel hardware.

Machine Learning, Alt Data & Deep Learning Stream

13.45 – 14.30: Mathematical Finance in the Exascale Era

Historically quant analytics have largely ignored the hardware resulting in an algorithm Lottery. Many widely used algorithms and approaches are very poorly performing on modern hardware. Exacerbated by the rapidity of the banking business cycle opportunities for vastly faster quant analytics have been missed.

In this talk we will illustrate some of the problems and their solutions:

  1. Hardware specialisation: Superscalar/SIMD/GPU…
  2. AAD in calibration and risk
  3. Usable front-office Machine learning

Jason Charlesworth:

NAG and Founder of Zettamatics

Jason Charlesworth: NAG and Founder of Zettamatics

Jason Charlesworth holds a PhD in theoretical physics from Cambridge University. He has worked in academic research in London and Cornell as well as industrial research in speech recognition, natural language processing, and machine learning. With over 20 years leading front-office quant and quant dev groups, he is an expert in applied computational mathematics and high-performance computing with a particular focus on novel hardware.

Machine Learning, Alt Data & Deep Learning Stream

14.30 – 15.15: Learning Market Data Anomalies

  • Why anomaly data detection is crucial for market risk management
  • Techniques: from simple statistics to isolation forest and (variational) autoencoders
  • Comparing and reporting outputs
  • Operational framework

Abstract

Everyday market risk managers are required to check a huge amount of market data, scenarios and positions used to compute market risk measures. Some data may present anomalous values because of a wide range of reasons, e.g. bugs in the related production processes, sudden and severe market movements, etc. Hence, it is crucial to integrate the daily data quality process with automatic and statistically robust tools able to smartly analyse all the available information and identify possible anomalies (the “needles in the haystack”). To this purpose we combined both simple statistics and machine learning algorithms to build an anomaly detection framework which is general enough to cover different asset classes and data dimensionalities (multiple curves, surfaces and cubes). The results are encouraging but they strongly depend on assumptions and parameters, keeping crucial the human supervision.

Marco Bianchetti:

Head of Internal Model Market Risk, Intesa Sanpaolo

Marco Bianchetti: Head of Internal Model Market Risk, 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:

Quantitative Analyst, Risk Management, Intesa Sanpaolo

Marco Scaringi: Quantitative Analyst, Risk Management, 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.

 

15.15 – 15.45: Afternoon Break and Networking Opportunities

Machine Learning, Alt Data & Deep Learning Stream

15.45 – 16.30: Common Causal Manifolds for Portfolio Risk Management via Sensitivity Modelling with Market Data.

Mean Variance framework goal is a linear combination of assets (portfolio) with returns as a vector in a hyperplane in which portfolio constituents’ vectors returns, when projected I that hyperplane, have at best (optimal) opposite directions. In a way, a portfolio is a financial product in which constituents hedge each other up to a level (diversification). This setup while introducing the key concept of diversification has strong limitations known by any practitioner. Thinking of financial markets as a dynamical system consisting of all public and private information makes that hyperplane a useless abstraction. However, the concept of diversification allows to think in other, more realistic, manifolds, formed by data from the financial market’s dynamical system. If the markets are treated as a dynamical system, so are the constituents of a portfolio and the optimal portfolio itself. These systems can be modelled by PDEs and SPDEs, which most of the time are unknown but thanks to neural networks and automatic differentiation techniques their solution as well as their partial derivatives can be easily approximated from data. Just like in the Mean Variance setup, assets’ sensitivities can form manifolds in which these assets can be optimally projected for diversification. This time, diversification has a meaning defined by the PDE/SDE instead of an abstract hyperplane. Any manifold can be chosen by the portfolio manager depending on its preference, experience, or personal bias, differentiating from Bayesian methods in the structural non-linearity of PDE/SDE approximation. Our work focuses on causal manifolds. The infinite chain of events of causal dynamics in the markets makes it impossible to capture causal factors systematically, in contrast to what other practitioners believe, but physicist and engineers know this well, ending up with something that is too specific, abstract, and useless for general purposes. But if the focus is just on diversification, there is no need to care about causal dynamics, but about common causal dynamics of the portfolio constituents. For this, we have tools developed in the twentieth century, with which it is possible to approximate probabilistic common cause with correlation (Reichenbach Common Cause Principle, 1956). By finding the common cause optimal drivers of the portfolio constituents’ dynamics, we can approximate the sensitivities of the constituents with respect to them with neural networks, form the common causal manifold and find the optimal portfolio in terms of common causal diversification.

We will explain the methodology, including examples showing its impressive performance. Also, how portfolio managers can incorporate this toolkit as part of their investment process and in combination with other sources of alpha and beta. We will explain how by modelling sensitivities calibrated to market data allows modelling the trajectory of portfolio risk. Finally, we will show the connection with common causal factor models and some uses.

Alejandro Rodríguez Domínguez:

Head of Quantitative Research & Analysis, Miraltabank

Alejandro Rodríguez Domínguez: Head of Quantitative Research & Analysis, Miraltabank

Alejandro Rodríguez Domínguez is Head of Quantitative Research & Analysis at Miraltabank, which he joined in 2018. He is a PhD candidate from University of Reading in Information Geometry applied to Continual learning for Autonomous Driving Trajectory Prediction. Prior to that he worked in London as a Financial Engineer at Société Générale, Quant at Nomura and trader at BBVA. He holds a M.Eng Mining Engineering from Universidad Politecnica de Madrid, MSc Financial Engineering and Risk Management from Imperial College London, MSc Computational Statistics from Universidad Complutense de Madrid and MSc Artificial Intelligence from Munster Technological University.

Machine Learning, Alt Data & Deep Learning Stream

16.30 – 17.15: Prediction and Imputation of financial data using collaborative filtering and Machine Learning

Abstract: Quant traders and data scientists regularly use automated ML & AI technologies to extract a variety of information from large datasets, e.g. sentiment from news data, or scoring methods for complex data sets like Supply Chain and ESG.

ML methods are also being used for imputation of financial data as well as prediction of asset prices. This talk will provide a brief overview of the following topics:

  • The broad application of machine learning in finance: opportunities and challenges.

Machine Learning techniques for Imputation e.g. estimating granular Geographical Exposure of companies given partial & high-level disclosure from the company financial statement

  • Collaborative filtering techniques for illiquid asset pricing, use of data driven methods to inform price movements in a target instrument from observations on related liquid instruments

Arun Verma:

Head of Quantitative Research Solutions, Bloomberg

Arun Verma: Head of Quantitative Research Solutions, Bloomberg

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.

All Streams

17.15 – 18.00: QFC Panel

Large Language Models (LLM) in Quant Finance – A Gimmick or a Game Changer?

  • Will LLLMs help us read derivatives term sheets, UCITS documentation, and securitization rules
  • Will LLLMs help us document our models for the regulators
  • Will LLLMs help us find bugs in our pricing models
  • GPT-4 or LLAMA 2?

ML Models for Valuation, XVA, and Risk

  • How to build trustworthy ML quant models that auditors and regulators will approve
  • Due to the lack of sufficient training data, is ML in quant models truly learning or only providing a better way to interpolate

Moderator:

Alexander Sokol:

Executive Chairman and Head of Quant Research, CompatibL

Alexander Sokol: Executive Chairman and Head of Quant Research, CompatibL

Alexander Sokol is the founder, Executive Chairman, and Head of Quant Research at CompatibL, a trading and risk technology company. He is also the co-founder of Numerix, where he served as CTO from 1996 to 2003, and the co-founder of Duality Group, where he served as CTO from 2017 to 2020.

Alexander won the Quant of the Year Award in 2018 together with Leif Andersen and Michael Pykhtin, for their joint work revealing the true scale of the settlement gap risk that remains even in the presence of initial margin. Alexander’s other notable research contributions include systemic wrong-way risk (with Michael Pykhtin, Risk Magazine), joint measure models, and the local price of risk (with John Hull and Alan White, Risk Magazine), and mean reversion skew (Risk Books, 2014).

Alexander earned his BA from the Moscow Institute of Physics and Technology at the age of 18, and a PhD from the L. D. Landau Institute for Theoretical Physics at the age of 22. He was the winner of the USSR Academy of Sciences Medal for Best Student Research of the Year in 1988.

Ioana Boier:

Ioana Boier:

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

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

Ignacio Ruiz:

MoCaX Intelligence

Ignacio Ruiz: MoCaX Intelligence

Ignacio Ruiz has been the Head of Counterparty Credit Risk Measurement and Analytics, Scotiabank, the head strategist for Counterparty Credit Risk, exposure measurement, for Credit Suisse, as well as the Head of Risk Methodology, equities, for BNP Paribas. In 2010, Ignacio set up iRuiz Consulting as an independent advisory business in this field. In 2014, Ignacio founded iRuiz Technologies to develop and commercialise MoCaX Intelligence.

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

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

Peter Jaeckel:

Independent financial mathematics and analytics consultant. OTC Analytics

Peter Jaeckel: Independent financial mathematics and analytics consultant. OTC Analytics

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.

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.

Gala Dinner: Marina Restaurante 

The main building of the Beach Club is home to the Marina Restaurant, a circular restaurant with spacious windows overlooking Las Arenas Playa. The terrace extends towards the sea, a space full of charm for those who want to have a meal “al fresco” with amazing sea views.

Marina Restaurante
Calle Marina Real Juan Carlos I
46011 Valencia
Spain

Tel: +34 961 150 007
Website

Directions from SH Valencia Palace to Marina Restaurante.

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