World Business StrategiesServing the Global Financial Community since 2000

Main Conference Day 2: Friday 27th September

08.30 – 09.00: Morning Welcome Coffee

Morning Stream Chair: TBC

Generative AI / Large Language Models / Machine Learning Stream

09.00 – 09.45: Generative Machine Learning in Quantitative Finance

Recent advances in modeling of financial markets have focused on understanding deep statistical dependencies among a large number of financial assets and their characteristics. We model joint dynamics of thousands of companies along with hundreds of their financial data fields, e.g. market prices, fundamentals and technical indicators; and when we don’t have sufficient historical data we use generative AI to produce synthetic data. The usage of machine learning to generate synthetic data has grown with the advent large language models that learn the complex multi-variate distribution of the underlying data. We show that generative AI has a broad range of applications in finance, including generating realistic financial time-series, volatility and correlation estimation and portfolio optimization. We will demonstrate applications in Data Imputation and Now-casting particularly for deducing geographical, climate and ESG exposures of companies that fail to report on these metrics. We also show applications of generative modeling for general asset and derivatives pricing/hedging use cases, with a special treatment on pricing illiquid instruments which don’t have much market data.

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.

Generative AI / Large Language Models / Machine Learning Stream

09.45 – 10.30: Generative methods in Quant Finance

  • Generative Methods – an overview
  • Synthetic Data
  • GMMR (Gaussian Mixture Regression) and Data Generation
  • Examples

Jörg Kienitz:

Quantitative Finance and Machine Learning, Acadiasoft

Jörg Kienitz: Quantitative Finance and Machine Learning (Acadiasoft), Partner (Quaternion), Adjunct Prof (UCT), Assistant Prof (BUW)

Jörg Kienitz is a partner at Quaternion, Acadia’s Quant Services division. He owns the finciraptor.de website – an educational platform for Quantitative Finance and Machine Learning. Jörg consults on the development, implementation, and validation of quantitative models. He is an Assistant Professor at the University of Wuppertal and an Adjunct Associate Professor in AIFMRM at the University of Cape Town. He regularly addresses major conferences, including Quant Minds, RISK or the WBS Quant Conference. Jörg has authored four books, Monte Carlo Frameworks (with Daniel J. Duffy), Financial Modelling (with Daniel Wetterau), and Interest Rate Derivatives Explained I and II (with Peter Caspers). He also co-authored research articles that appeared in leading journals like Quantitative Finance, RISK or Mathematics in Industry.

10.30 – 11.00: Morning Break and Networking Opportunities

Generative AI / Large Language Models / Machine Learning Stream

11.00 – 11.45: Topic and Presenter to be confirmed.

Generative AI / Large Language Models / Machine Learning Stream

11.45 – 12.30: Variational Deep Pricing

  • Motivation: Deep Learning Approximations with Generative ML
  • Generative ML vs Discriminative ML
  • An Introduction to Variational Inference and KL Divergence
  • Conditional and Regression Variational Auto-Encoder
  • Numerical Implementation
  • Applications

Youssef Elouerkhaoui:

Managing Director, Global Head of Markets Quantitative Analysis, Citi

Youssef Elouerkhaoui: Managing Director, Global Head of Markets Quantitative Analysis, Citi

Youssed Elouerkhaoui is the global Head of Credit Quantitive Analysis at Citi. His group supports all aspects of modelling and product development across desks, thais includes: Flow Credit Trading, Correlation Trading, CDOs, Exotics and Emering Markets.

He also supports CVA, Funding and Regulatory Capital for Credit Markets. Prior to this, he was a Director in the Fixed Income Derivatives Quantitative Research Group at UBS, where he was in charge of developing and implementing models for the Structured Credit Desk. Before joining UBS, Youssef was a Quantitative Research Analyst at Credit Lyonnais supporting the Interest Rates Exotics business. He has also worked as a Senior Consultant in the Risk Analytics and Research Group at Ernst & Young. He is a graduate of Ecole Centrale Paris and he holds a PhD in Mathematics from Paris-Dauphine University.

12.30 – 13.30: Lunch

Afternoon Stream Chair: TBC

Generative AI / Large Language Models / Machine Learning Stream

13.30 – 14.15: Topic and Presenter to be confirmed.

Generative AI / Large Language Models / Machine Learning Stream

14.15 – 15.00: Foundation NLP Models for information extraction

  • How to specialize generalized Foundation models
  • Putting the Human back in the Loop
  • Think step by step…

Robert Dargavel Smith:

Director of Machine Learning Engineering , Clarity AI

Robert Dargavel Smith: Director of Machine Learning Engineering , Clarity AI

“Robert Smith is the Director of Machine Learning Engineering at Clarity AI. Previously he was Head of Data Science at IHS Markit (now part of S&P Global). He has worked in capital markets for over 25 years in Banco Santander and ABN Amro, holding various positions from Head of CVA Desk to Global Head of Quantitative Analysis.”

15.00 – 15.30: Afternoon Break and Networking Opportunities

All Streams: Closing talk

15.30 – 16.00: Topic and Presenter to be confirmed

08.30 – 09.00: Morning Welcome Coffee

Morning Stream Chair: TBC

Volatility / Options / Trading Stream

09.00 – 09.45: Beyond affine models: On inclusion of random parameters in pricing models

In this talk, we challenge the traditional reliance on Affine (Jump) Diffusion (AD) models in financial pricing through the introduction of Randomized AD (RAnD) models. By integrating exogenous stochasticity into model parameters, RAnD extends beyond the limitations of affine models, offering enhanced flexibility and precision in option pricing. This approach not only overcomes the linearity constraints of AD models but also maintains the benefits of quick calibration and efficient Monte Carlo simulations. We explore the theoretical foundations and practical implementations of RAnD, including the derivation of characteristic functions, simulation techniques, and sensitivity analysis. Specifically, we demonstrate the superiority of randomized stochastic volatility models through the consistent pricing of options on the S&P 500 and VIX. Furthermore, we extend our investigation to short-rate models within the Heath-Jarrow-Morton framework, applying RAnD to achieve controlled implied volatility and improved calibration quality. The randomized Hull-White model exemplifies the potential of RAnD in producing local volatility dynamics and achieving near-perfect calibration to swaption implied volatilities. This talk underscores the significance of incorporating random parameters into pricing models, marking a departure from traditional affine models towards a more nuanced and practical modelling approach in financial markets.

Lech Grzelak:

Quantitative Analyst, Rabobank and Assistant Professor, TUDelft

Lech Grzelak: Quantitative Analyst, Rabobank and Assistant Professor, TUDelft

Volatility / Options / Trading Stream

09.45 – 10.30: A Local Volatility Model for Counterparty Risk Computation of Equity Products

In the context of counterparty risk exposure computation, a local volatility model is suggested for equity spot price simulation. For the spot simulation model to have stylised features (such as fat tails, mean-reverting and positive volatility levels), the starting point is the Heston stochastic volatility model. The local volatility model is then built to “replicate” the Heston model through Gyongy theorem (i.e. as a weak solution). Model calibration is performed based on historical time series of equity spot and variance swap prices. A set of numerical tests are presented to compare the characteristics of the suggested model with GBM. In particular, the suggested model leads to more realistic results for volatility and variance swaps as well as for far-OTM put options. Finally, the impacts in terms of EEPE and PFE are presented.

Antoine Collas:

Market & Counterparty Risk Quantitative Analyst, BNP Paribas

Antoine Collas: Market & Counterparty Risk Quantitative Analyst, BNP Paribas

10.30 – 11.00: Morning Break and Networking Opportunities

Volatility / Options / Trading Stream

11.00 – 11.45: Denoised Monte Carlo for Option Pricing and Greeks estimation

Andrzej Daniluk:

Traded Risk Model Validation, Director, Standard Chartered Bank

Andrzej Daniluk: Traded Risk Model Validation, Director, Standard Chartered Bank

Volatility / Options / Trading Stream

11.45 – 12.30: High-Performance Option pricing with Discrete Dividends

We consider the problem of efficiently pricing options in the presence of lumpy dividends. We aim for a high-performance algorithm (fast, very accurate) that can support almost arbitrarily complicated dependency between the discrete dividend policy and the stock price. We also allow for usage of continuous dividend yield, which may double as a model for borrow costs. The algorithm can handle European, American and Bermudan options, and we also discuss more advanced dividend models where the payment policy is noisy and/or depends on the history of stock prices.

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.

12.30 – 13.30: Lunch

Afternoon Stream Chair: TBC

Volatility / Options / Trading Stream

13.30 – 14.15: Shadow Hedging and Statistical Arbitrage

Mihail Turlakov:

Mihail Turlakov: Senior Advisor, Terra Quantum AG

Mihail Turlakov: Senior Advisor, Terra Quantum AG

Volatility / Options / Trading Stream

14.15 – 15.00: ‘Why Citadel has become the largest hedge fund ever trading Commodities’

Helyette Geman:

Ralph O’Connors Sustainable Energy Institute, Johns Hopkins University

Helyette Geman: PhD, PhD: Ralph O’Connors Sustainable Energy Institute, Johns Hopkins University, Visiting Professor, Inland Norway University of Applied Sciences

Director, Commodity Finance Centre, Birkbeck-University of London

Ralph O’Connors Sustainable Energy Institute, Johns Hopkins University

Hélyette Geman is a Graduate of Ecole Normale Superieure in Mathematics and holds PhDs in Probability and Finance and a Masters’ degree in Theoretical Physics.

She has published more than 100 papers in Quantitative Finance; her book ‘Commodities and Commodity Derivatives’ is the reference in the field.

Hélyette Geman has taught in a number of prestigious Institutions worldwide and consulted for trading entities such as Louis Dreyfus, EDF Trading or Total Gas & Power.

Named ‘Financial Engineer of the Year 2022’ by the International Association for Quantitative Finance.

15.00 – 15.30: Afternoon Break and Networking Opportunities

All Streams: Closing talk

15.30 – 16.00: Topic and Presenter to be confirmed.

08.30 – 09.00: Morning Welcome Coffee

Morning Stream Chair: TBC

Machine Learning & Alt Data Stream

09.00 – 09.45: Time series Transformers – Challenges and Opportunities in Long-Term Financial Time Series Forecasting

 

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.

Machine Learning & Alt Data Stream

09.45 – 10.30: Approaches to Learning Basis Functions for Least-Squares Monte Carlo Methods

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.

10.30 – 11.00: Morning Break and Networking Opportunities

Machine Learning & Alt Data Stream

11.00 – 11.45: Modelling Diversity in Structured Ensemble Prediction and Applications to Forward-looking Portfolio Theory

  • A closed-form solution for structured time-series prediction, from an ensemble of learners in which diversity can be controlled parametrically, is presented. The method is called Structured Radial Basis Function Network (Rodriguez-Dominguez et al., 2023).
  • The connection between diversity in structured ensemble prediction and forward-looking portfolio diversification is described.
  • The new forward-looking portfolio theory is explained and contrasted with existing frameworks.
  • Applications and experiments are discussed, including main competitive advantages of this framework with respect to others.

Rodriguez Dominguez A. and Shahzad M. and Hong X. (2023). Structured Radial Basis Function Network: Modelling Diversity for Multiple Hypotheses Prediction },2309.00781, arXiv, cs.LG

Alejandro Rodríguez Domínguez:

Head of Quantitative Research & Analysis, Miraltabank

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

Alejandro Rodriguez Dominguez is Head of Quantitative Analysis at Miralta Bank since 2018, a Spanish bank with a focus on private and institutional investments, and more than 1 Bn of AUM. His team is responsible for the R&D of data-driven and AI-based solutions across the institution. His research focuses mainly on the applications of machine learning and statistics to portfolio risk diversification, correlation structures and causality, and developing risk management indicators. He is also a quant advisor at Inspiration-Q, working in the R&D of quantum-inspired systematic investment strategies.

Previously, Alejandro worked in London and Paris for several years in Société Générale, Nomura, BBVA and BNP Paribas since 2012, in trading and financial engineering roles. Alejandro pursues a PhD at University of Reading in Artificial Intelligence, and holds a M. Eng in Mining Engineering, a MSc in Financial Engineering from Imperial College London, a MSc in Computational Statistics, and a MSc in Artificial Intelligence from Cork Institute of Technology.

Machine Learning & Alt Data Stream

11.45 – 12.30: Exploring the relationship between inflation, growth and macro assets

In this presentation, we will more broadly explore the relationship between inflation, growth and macro assets. We’ll also be examining more broadly the relationship between inflation and growth across multiple countries. We’ll look at how forecasts for inflation and growth can be used to create systematic trading rules for macro assets such as G10 FX, EMFX, etc.

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.

12.30 – 13.30: Lunch

Afternoon Stream Chair: TBC

Latest Modelling Techniques

13.30 – 14.15: The return of the JEDi: Jumps, Elasticity and Diffusion for Libor and Alternate Reference Rates

  • Has the switch to ARR changed universal regimes for rates?
  • Volatility (diffusion) dependence on rate levels
  • Jump dependence on rate levels
  • Are short rates special? Central Bank rates as a part of the model
  • Micro and macro structure of the regimes
  • 1-day vs 10-days returns: FRTB angle
  • What about inflation?

Maria Makarova:

Assistant Vice President Quantitative Analyst, BNP Paribas

Maria Makarova: Assistant Vice President Quantitative Analyst, BNP Paribas

Maria Makarova has been a Risk Methodology Quantitative Analyst at BNP Paribas since 2018. She splits her time between performing research of interest rate modelling, and delivering improvements to the Market and Counterparty Risk methodologies. Maria has previously worked for Barclays, developing Market Risk models and helping to adapt the bank’s framework to FRTB requirements. Before starting her career in Financial Markets, she has briefly worked as a managements consultant with McKinsey. Maria holds a Master degree in Applied Maths from Moscow Institute of Physics and Technology.

Vladimir Chorniy:

Managing Director, Head of Risk Model Fundamentals and Research Lab, Senior Technical Lead, BNP Paribas

Vladimir Chorniy: Managing Director, Head of Risk Model Fundamentals and Research Lab, Senior Technical Lead, BNP Paribas

Vladimir Chorniy started his career in finance as a founding member and later led Credit Risk Analytics team in Barclays Capital. Later he headed Risk Methodology and Analytics team in BNP Paribas responsible for methodologies covering counterparty risk (EE/PFE models), market risk (VAR, IRC, CRM), credit value adjustment, capital calculations and exotic derivative treatment. Later Vladimir has assumed a new role to determine long term strategy of risk modelling in BNP Paribas as Head of Risk Modelling Strategy and Senior Technical Lead. In 2022 whilst retaining his lead role in model development Vladimir founded and became the head of Risk Model Fundamentals and Research Lab to reflect evolving role and understanding of model risk. Vladimir holds a Ph.D. in Physics from Cambridge University.

Latest Modelling Techniques

14.15 – 15.00: Sharpening Market Risk Allocation: from Basel 2.5 to FRTB

Contents:

  • The problem of Risk allocation
  • Euler, Shapley, and beyond
  • Toy examples, computational challenged
  • Application to real portfolios: Basel 2.5 and FRTB

Abstract

Risk Allocation is a common problem in Risk Management, consisting in the decomposition of a risk measure into the contributions of its components, like portfolios or underlying risk factors, which contribute to the measure itself. This task is not straightforward, since risk measures are indeed typically not additive because of interactions among the risk sources. In this work we review various risk allocation principles, outlining their advantages and shortcomings. In particular, we focus on Shapley Allocation, unpacking its theoretical properties and examining its implications in real-world applications. Moreover, we investigate computational challenges that often accompany its implementation, discussing possible strategies to overcome these hurdles. Finally, we propose some practical applications of market risk allocation to realistic trading portfolios under different regulations, i.e. Basel 2.5 and FRTB.

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.

15.00 – 15.30: Afternoon Break and Networking Opportunities

All Streams: Closing talk

15.30 – 16.00: Topic and Presenter to be confirmed.

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    15% until 19th July 2024

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    10% until 23rd August 2024

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    When two colleagues attend the 3rd goes free!

  • 70% Academic Discount
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