Main Conference Day 2: Friday 2nd October
08.30 – 09.00: Morning Welcome Coffee
Morning Stream Chair:
Nikolai Nowaczyk:
Quantitative Analytics, Director, NatWest Group
Nikolai Nowaczyk:
Nikolai Nowaczyk: Quantitative Analytics, Director, NatWest Group
09.00 – 09.45: “Vibe Coding xVA?”
Nikolai Nowaczyk:
Quantitative Analytics, Director, NatWest Group
Nikolai Nowaczyk:
Nikolai Nowaczyk: Quantitative Analytics, Director, NatWest Group
09.45 – 10.30: Multimodal models for asset price evolution
Blanka Horvath:
Associate Professor in Mathematical and Computational Finance, University of Oxford
Blanka Horvath:
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.
10.30 – 11.00: Morning Break and Networking Opportunities
11.00 – 11.45: Breaking the Trend: How to Avoid Cherry-Picked Signals
Our empirical results show an impressive fit with the pretty complex theoretical Sharpe formula of a trend-following strategy depending on the parameter of the signal, which was derived by Grebenkov and Serror (2014). That empirical fit convinces us that a mean-Réversion process with only one time scale is enough to model, in a pret y precise way, the reality of the trend-following mechanism at the average scale of CTAs and as a consequence, using only one simple EMA, appears optimal to capture the trend. As a consequence, using a complex basket of different complex indicators as signal, do not seem to be so rational or optimal and exposes to the risk of cherry-picking.
Sébastien Valeyre:
Portfolio Manager, Machina Capital
Sébastien Valeyre:
Sébastien Valeyre: Portfolio Manager, Machina Capital
Sébastien Valeyre is the portfolio manager of Machina Capital’s systematic futures strategy, Machina Electron. Machina Capital is a Paris-based investment firm, founded by seasoned equity derivatives traders and quantitative researchers. The firm specializes in mid-frequency systematic strategies for equities and futures, aiming to generate absolute and uncorrelated returns.
Prior to joining Machina Capital, Sébastien was a partner at John Locke Investments, where he launched a statistical arbitrage fund, the John Locke Equity Market Neutral Fund. Sébastien successfully managed this equity strategy while also contributing research to the firm’s systematic CTA strategy. Before that, Sébastien was head of research at BPHI Capital, which employed a blended fundamental and quantitative approach. Sébastien began his career at France’s Authority of Nuclear Safety and Atomic Energy Commission.
Sébastien holds a PhD in Economics from Sorbonne Paris Cité University, a Master of Science from Imperial College, a Master of Science in Finance from Dauphine University, and a Master of Science in Physics from École Supérieure de Physique et Chimie Industrielles de Paris (ESPCI).
11.45 – 12.30: Self-Improving LLM-agents
Nicole Königstein:
Chief Data Scientist, Head of AI & Quant Research, Wyden Capital AG
Nicole Königstein:
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.
12.30 – 13.30: Lunch
Afternoon Stream Chair:
Christopher Kantos:
Managing Director and Head of Quantitative Research, Alexandria Technology
Christopher Kantos:
Christopher Kantos: Managing Director and Head of Quantitative Research, Alexandria Technology
Mr. Christopher Kantos is a Managing Director and Head of Quantitative Research at Alexandria Technology. In this role, he focuses on maintaining and growing new business in EMEA, and exploring ways in which natural language processing and machine learning can be applied in the financial domain. Prior, he spent 15 years working in financial risk at Northfield Information Services as a Director and Senior Equity Risk Analyst. Mr. Kantos earned a BS in computer engineering from Tufts University.
13.30 – 14.15: Time Series Foundation Models
This session covers the emerging landscape of time series foundation models — a new class of AI systems designed to understand and reason over sequential, temporal data at scale.
We’ll cover what they are, why they matter, and what drives their development over traditional task-specific forecasting approaches. We’ll look at real-world examples including Delphyne, a time series foundation model we trained from scratch, before closing with a look at where the field still falls short and where future research can go.
Achintya Gopal:
AI Engineer, Millennium
Achintya Gopal:
Achintya Gopal: AI Engineer, Millennium
Achintya Gopal is an AI Engineer at Millennium, where he works on applying machine learning and AI to quantitative modeling in finance. Prior to that, he was a Machine Learning Quant Researcher at Bloomberg, working on a wide range of machine learning techniques for finance. His work includes developing foundation models for financial time series, generative modeling and factor modeling of equities with machine learning, causal inference, differential privacy, interpretability of LLMs, and developing novel models for uncertainty modeling using normalizing flows and novel methods to evaluate statistical models with model uncertainty.
14.15 – 15.00: Model Risk in the Age of Agentic AI
- From function validation to policy validation
- Fragility under perturbations and distribution shift
- Specification and objective misalignment
- Adversarial, generative validation frameworks
- Continuous assurance of non-stationary systems
Harsh Prasad:
Harsh Prasad:
Harsh Prasad: Principal & CEO, Qxplain
Harsh is the CEO of Qxplain, where he is working to help clients in the financial institutions develop and adopt more trustworthy AI models. He specializes in model risk management, AI/ML applications in finance, and GenAI product development. Prior to starting Qxplain, Harsh has over 20 years of experience in model development and validation where he led groundbreaking work in applying machine learning to the financial services industry. He has worked with Morgan Stanley, Nomura, EY and provided consultancy to GE Capital, Mubadala, Citi, BNP Paribas, London Stock Exchange Group and several other banks, funds, asset managers and family offices. He has taught at various universities, is the chair of CQF industry working group for data science and machine learning and conducts advanced trainings for machine learning in finance. He is an active researcher, thought leader and contributor in shaping the industry and regulatory best practice of model risk management for AI/ML models.
15.00 – 15.30: Afternoon Break and Networking Opportunities
15.30 – 16.15: “Sports Trading as a Quantitative Market: Games, Signals & Equilibria”
- Market microstructure, pricing inefficiencies, liquidity dynamics, and parallels with traditional financial markets.
- Machine Learning for Prediction & Pricing – Forecasting outcomes, probability estimation, feature engineering, and turning predictive signals into trading decisions.
- AI & LLMs for Research and Trading Operations – Automated analysis, information extraction, trader productivity, and decision-support systems.
- Building Scalable Trading Systems – Data infrastructure, model deployment, execution, monitoring, and risk management.
- The Future of Quantitative Sports Trading – Agentic AI, real-time decision-making, reinforcement learning, and emerging business opportunities.
Maurizio Garro:
CFO and Head of Business Development, My Alpha investment FZCO
Maurizio Garro:
Maurizio Garro: CFO and Head of Business Development, My Alpha investment FZCO
Maurizio Garro works as a CFO and Head of Business Development at My Alpha investment FZCO. Previusly he was the senior Lead BA for the IBOR Transition programme at Lloyds Banking Group, where he lead the delivery of the changes required for models, curves and products for the transition to the alternative risk-free rates for the Front and Back book. His background is in quantitative risk management, Model Risk, Market Risk, Counterparty Credit Risk, Pricing, Liquidity and Stress Testing.
He has a long-standing experience as an internal auditor, consultant and banker in model risk management and previously worked in the Development and Validation teams of top-tier financial institutions in Europe, U.S., and the U.K. for over 15 years.
Maurizio is a frequent speaker on various topics in risk management, a member of the Institute of Internal Auditor and the Director of the Global Association of Risk Professional (GARP) London Chapter.
Maurizio Garro received his Master Degree in Economics from the Bocconi University of Milano and a certificate in Financial Risk Management (FRM) from GARP.
08.30 – 09.00: Morning Welcome Coffee
Morning Stream Chair:
To be confirmed
09.00 – 09.45: “Stretching Volatility Parametrizations with Random Coefficients”
- Implied volatility parametrizations are enhanced by randomizing the coefficients
- New parametrizations are semi-analytical and powerful enough to fit almost all market regimes
It is a market practice to express market-implied volatilities in some parametric form (SABR, SVI). These representations indirectly impose a model-specific volatility structure on observable market quotes. When the market’s volatility does not follow the parametric model regime, the calibration procedure will fail or lead to extreme parameters, indicating inconsistency. In this talk we propose an arbitrage-free framework for letting the parameters from the parametric implied volatility formula be random. The method enhances the existing parametrizations and enables a significant widening of the spectrum of permissible shapes of implied volatilities while preserving analyticity. We demonstrate the effectiveness of the novel method on real data from short-term index and equity options, where the standard parametrizations fail to capture market dynamics. Our results show that the proposed method is particularly powerful in modeling the implied volatility curves of short expiry options preceding an earnings announcement, when the risk-neutral probability density function exhibits a bimodal form.
Nicola Zaugg:
Quantitative Researcher, LGT Private Banking
Nicola Zaugg:
Nicola Zaugg: Quantitative Researcher, LGT Private Banking
Nicola Zaugg is a quantitative researcher in fixed income and derivatives at LGT Private Banking in Zurich. Alongside his industry role, he conducts academic research in financial mathematics in collaboration with Utrecht University in the Netherlands, contributing to research on derivatives pricing and volatility modeling. Prior to joining LGT, Nicola worked as a quantitative researcher at Rabobank in the Netherlands and at swissQuant in Zurich, Switzerland.
09.45 – 10.30: Convex Volatility Interpolation (CVI), an arbitrage-free volatility surface fitting methodology
- Arbitrage-free implied volatility surface fitting posed as a convex quadratic program in variance space
- Calendar spread no-arbitrage constraints are linear, butterfly no-arbitrage constraints are linearized
- Model-free, bid-ask-aware, no hyperparameter tuning (consistent across underlyings)
- Convexity guarantees a unique global optimum, eliminating the calibration fragility of traditional parametric models
- All expiries fitted jointly. Fit S&P 500 in a fraction of a second
Fabrice Deschâtres:
Founder and CEO, Volptima
Fabrice Deschâtres:
Fabrice Deschâtres: Founder and CEO, Volptima
Fabrice Deschâtres is the founder and CEO of Volptima, a Swiss fintech company commercialising Convex Volatility Interpolation (CVI), a high-performance, arbitrage-free volatility surface fitting methodology published in Risk.net’s Cutting Edge section in February 2026. Before founding Volptima, Fabrice held quantitative roles in derivatives pricing at Goldman Sachs, Millennium and Flow Traders. He is a graduate of the École Normale Supérieure (Ulm).
10.30 – 11.00: Morning Break and Networking Opportunities
11.00 – 11.45: Smooth Local Vol Construction: The case of American options on stocks with discrete dividends.
Leif Andersen:
Global Co-Head Of Quantitative Strategies Group, Bank of America
Leif Andersen:
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.
11.45 – 12.30: Unbiased Monte Carlo Greeks for Discontinuous Payoffs — No Smoothing Required
Pathwise differentiation is the standard method for computing Monte Carlo Greeks, but it fails at discontinuities: barriers, autocall triggers, digital coupons, and knock-out conditions all produce zero or biased sensitivities. The industry workaround — manual insertion of smoothing functions — requires per-product calibration, introduces systematic bias, and is a persistent source of model risk.
We present a correction method that restores unbiased Greeks without any smoothing. For each discontinuity indicator, a one-dimensional Newton root-finding locates the boundary in the normal-random space, and a local payoff jump is combined with the standard normal density to produce an exact correction term. The method is model independent (GBM, Heston, Hull-White, LMM), product-independent (any payoff expressible as a composition of indicator functions), and fully automatic — no manual parameter tuning is needed.
Benchmarks on multi-asset autocallables with weekly observations show that all Greeks match bump-and-revalue within Monte Carlo noise, at a cost of 6–12 additional kernel replaysCper indicator per path. We demonstrate convergence on barrier options (vs analytic), Phoenix autocallables, and two-asset worst-of structures.
Evgeny Lakshtanov:
Evgeny Lakshtanov:
Evgeny Lakshtanov: Traded Risk Model Validation Validator, Standard Chartered
Evgeny spent many years in academia working on mathematical research and Electric Impedance Tomography. In 2018 Evgeny joined Dmitry Goloubentsev to co-found Matlogica where he focused on AAD and a new paradigm for programming and parallel computations. In 2022 he left academia to work on Traded Risk Model Validation at Standard Chartered Bank.
12.30 – 13.30: Lunch
Afternoon Stream Chair:
To be confirmed
13.30 – 14.15: Hysteretic Stochastic Volatility
Julien Guyon:
Professor, ENPC, Institut Polytechnique de Paris & Visiting Associate Professor, NYU Tandon
Julien Guyon:
Julien Guyon: Professor, ENPC, Institut Polytechnique de Paris & Visiting Associate Professor, NYU Tandon
Julien is a former senior quantitative analyst in the Quantitative Research group at Bloomberg L.P., New York. He is also an adjunct professor in the Department of Mathematics at Columbia University and at the Courant Institute of Mathematical Sciences, NYU. Before joining Bloomberg, Julien worked in the Global Markets Quantitative Research team at Societe Generale in Paris for six years (2006-2012), and was an adjunct professor at Universite Paris 7 and Ecole des ponts. He co-authored the book Nonlinear Option Pricing (Chapman & Hall, CRC Financial Mathematics Series, 2014) with Pierre Henry-Labordere. His main research interests include nonlinear option pricing, volatility and correlation modeling, and numerical probabilistic methods. Julien holds a Ph.D. in Probability Theory and Statistics from Ecole des ponts. He graduated from Ecole Polytechnique (Paris), Universite Paris 6, and Ecole des ponts. A big football fan, Julien has also developed a strong interest in sports analytics, and has published several articles on the FIFA World Cup, the UEFA Champions League, and the UEFA Euro in top-tier newspapers such as The New York Times, Le Monde, and El Pais, including a new, fairer draw method for the FIFA World Cup.
14.15 – 15.00: Arbitrage-Free Volatility in Delta Space: Abel ODEs and Surface Interpolation
In this talk, we present recent results on the construction and interpolation of arbitrage-free implied volatility surfaces.
The first part focuses on a characterization of differentiable, arbitrage-free implied volatility slices. Using Fukasawa’s normalizing volatility transforms (NVTs), we show that every such slice satisfies a first-order Abel ODE. We then provide numerical examples illustrating how this characterization can be used to construct and interpolate implied volatility smiles across strikes.
In the second part, we study the interpolation of volatility slices across expiries. We establish a connection between calendar and strike arbitrage in NVT coordinates and discuss how this relationship can be used to construct arbitrage-free implied volatility surfaces.
Vladimir Lucic
Head of Quants at Marex Solutions, Visiting Professor at Imperial College London
Vladimir Lucic
Vladimir Lucic: Head of Quants, Marex Solutions, Visiting Professor, Imperial College London
Vladimir Lucic is a Visiting Professor at Imperial College London and Head of Quants at Marex Solutions, London. During his 25+ career as a quant Vladimir focused on various aspects of quant modelling and volatility investment strategies. He has published in premier academic and practitioner’s journals. Vladimir is the recipient of the Risk.net Quant of the Year award for 2025.
15.00 – 15.30: Afternoon Break and Networking Opportunities
15.30 – 16.15: “Sports Trading as a Quantitative Market: Games, Signals & Equilibria”
- Market microstructure, pricing inefficiencies, liquidity dynamics, and parallels with traditional financial markets.
- Machine Learning for Prediction & Pricing – Forecasting outcomes, probability estimation, feature engineering, and turning predictive signals into trading decisions.
- AI & LLMs for Research and Trading Operations – Automated analysis, information extraction, trader productivity, and decision-support systems.
- Building Scalable Trading Systems – Data infrastructure, model deployment, execution, monitoring, and risk management.
- The Future of Quantitative Sports Trading – Agentic AI, real-time decision-making, reinforcement learning, and emerging business opportunities.
Maurizio Garro:
CFO and Head of Business Development, My Alpha investment FZCO
Maurizio Garro:
Maurizio Garro: CFO and Head of Business Development, My Alpha investment FZCO
Maurizio Garro works as a CFO and Head of Business Development at My Alpha investment FZCO. Previusly he was the senior Lead BA for the IBOR Transition programme at Lloyds Banking Group, where he lead the delivery of the changes required for models, curves and products for the transition to the alternative risk-free rates for the Front and Back book. His background is in quantitative risk management, Model Risk, Market Risk, Counterparty Credit Risk, Pricing, Liquidity and Stress Testing.
He has a long-standing experience as an internal auditor, consultant and banker in model risk management and previously worked in the Development and Validation teams of top-tier financial institutions in Europe, U.S., and the U.K. for over 15 years.
Maurizio is a frequent speaker on various topics in risk management, a member of the Institute of Internal Auditor and the Director of the Global Association of Risk Professional (GARP) London Chapter.
Maurizio Garro received his Master Degree in Economics from the Bocconi University of Milano and a certificate in Financial Risk Management (FRM) from GARP.
08.30 – 09.00: Morning Welcome Coffee
Morning Stream Chair:
Marco Bianchetti:
Head of Market Risk Methodologies, Intesa Sanpaolo
Marco Bianchetti:
Marco Bianchetti: Head of Market Risk Methodologies, Intesa Sanpaolo
Marco holds a M.Sc. in theoretical nuclear physics (1995) and a Ph.D. in theoretical condensed matter physics (2000) from Università degli Studi di Milano. In 2000 he joined the Financial Engineering team of Banca Caboto (now IMI CIB Division of Intesa Sanpaolo), developing pricing models and applications for trading desks. In 2008 he moved to the Financial and Market Risk Management area of Intesa Sanpaolo. In 2015 he was appointed head of Fair Value Policy, developing the global fair/prudent/IPV policies and the valuation risk management framework of Intesa Sanpaolo Group. In 2021 he was appointed head of IMA Market Risk, in charge of regulatory market risk models and RWAs under Basel 2.5 and FRTB. Since Sept. 2024 he is head of Market and Counterparty Risk IMA Methodologies for Intesa Sanpaolo Group.
His work covers pricing and risk management of financial instruments, market risk, valuation risk, interest rates, XVAs, quasi-Monte Carlo, financial bubbles and portfolio optimization. He is the author of a few research papers, adjunct professor at Università di Bologna (2015-present) and at Università di Torino (2018-2023), member of Conference/Ph.D/Master Advisory Boards, and a frequent speaker at international conferences.
See also the LinkedIn profile.
09.00 – 09.45: Equity backfilling for the future
Eduardo Epperlein:
MD, Senior Technical Advisor: Nomura International PLC
Eduardo Epperlein:
Eduardo Epperlein has 30 years’ experience in the financial industry. Prior to joining Nomura, Eduardo held various roles in risk methodology at Citigroup, including model validation. Eduardo holds a PhD in Plasma Physics from Imperial College, London, and spent 10 years as a research scientist prior to joining the financial industry.
09.45 – 10.30: Charging for Liquidity
Liquidity has moved from a background constraint to a front-line driver of derivatives pricing and risk management. As LCR and related requirements bite, banks need a clear way to charge for liquidity alongside capital, funding and margin costs.
This talk explains how liquidity requirements are calculated, why they matter for derivatives desks, and how liquidity costs can be framed as a valuation adjustment linked to FVA and MVA.
Matthias Arnsdorf:
MD, Global Head of Counterparty Credit, Market Risk & Treasury Modelling, JPMorgan
Matthias Arnsdorf:
Matthias Arnsdorf, Managing Director, JPMorgan
Global Head of Counterparty Credit, Market Risk & Treasury Modelling
Matthias Arnsdorf leads the Counterparty Credit, Market Risk & Markets Treasury Quant teams at JPMorgan. He is responsible for the development of J.P. Morgan’s suite of XVA, VaR, margin & balance sheet models which are used for valuation, risk management as well as credit, market & liquidity risk capital.
Matthias started his career in finance in 2002 working in credit derivatives quant research. Prior to this he spent two years as a post-doctoral researcher at the Niels Bohr Institute in Copenhagen. Matthias holds a PhD in Quantum Gravity from Imperial College London.
10.30 – 11.00: Morning Break and Networking Opportunities
11.00 – 11.45: Graphical Representation for Structured Finance and Payoffs
Jörg Kienitz:
Quant Finance and Machine Learning, Adjunct Prof (UCT), Assistant Prof (BUW), Naturfotograf
Jörg Kienitz:
Jörg Kienitz: Quant Finance and Machine Learning, Adjunct Prof (UCT), Assistant Prof (BUW), Naturfotograf
Jörg Kienitz is Director of Quantitative Methods, Olaf Dreyer and Ken Lichtner are Principle Consultants for Quantitative Methods at mrig – a Frankfurt based consultancy firm specialized on Quantitative Finance. Before joining mrig all three worked for different consultancy companies, banks or financial infrastructure providers. They cumulate decades of experience in the financial markets sector and are active in the academic research as well.
Ken Lichtner:
Principle Consultants for Quantitative Methods, m|rig GmbH
Ken Lichtner:
Ken Lichtner: Principle Consultants for Quantitative Methods, m|rig GmbH
Jörg Kienitz is Director of Quantitative Methods, Olaf Dreyer and Ken Lichtner are Principle Consultants for Quantitative Methods at mrig – a Frankfurt based consultancy firm specialized on Quantitative Finance. Before joining mrig all three worked for different consultancy companies, banks or financial infrastructure providers. They cumulate decades of experience in the financial markets sector and are active in the academic research as well.
Olaf Dreyer:
Principle Consultants for Quantitative Methods, m|rig GmbH
Olaf Dreyer:
Olaf Dreyer: Principle Consultants for Quantitative Methods, m|rig GmbH
Jörg Kienitz is Director of Quantitative Methods, Olaf Dreyer and Ken Lichtner are Principle Consultants for Quantitative Methods at mrig – a Frankfurt based consultancy firm specialized on Quantitative Finance. Before joining mrig all three worked for different consultancy companies, banks or financial infrastructure providers. They cumulate decades of experience in the financial markets sector and are active in the academic research as well.
11.45 – 12.30: Learning the Exact SABR Model
- The pricing model calibration bottleneck
- Theoretical framework: why SABR ?
- Learning SABR with DNNs
- Conclusions and perspectives
The SABR model is a cornerstone of interest rate volatility modeling, but its practical application relies heavily on the analytical approximation by Hagan et al., whose accuracy deteriorates for high volatility, long maturities, and out-of-the-money options, admitting arbitrage. While machine learning approaches have been proposed to overcome these limitations, they have often been limited by simplified SABR dynamics or a lack of systematic validation against the full spectrum of market conditions.
We develop a DNN SABR, a specialized Deep Neural Network (DNN) architecture that learns the true SABR stochastic dynamics using an very large training dataset (more than 200 million points) of interest rate Cap/Floor volatility surfaces, including very long maturities (30Y) and extreme strikes consistently with market quotations. Our dataset is obtained via high-precision unbiased Monte Carlo simulation of a special scaled shifted-SABR stochastic dynamics, which allows dimensional reduction without any loss of generality. Our SABR DNN provides arbitrage-free calibration of real market volatility surfaces and Cap/Floor prices for any maturity and strike with negligible computational effort and without retraining across business dates. Our results fully address the gaps in the previous machine learning SABR literature in a systematic and self-consistent way, and can be extended to cover any interest rate European options in different rate tenors and currencies, thus establishing a comprehensive functional SABR framework that can be adopted for daily trading and risk management activities.
Paper: arxiv.org/abs/2510.10343
Marco Bianchetti:
Head of Market Risk Methodologies, Intesa Sanpaolo
Marco Bianchetti:
Marco Bianchetti: Head of Market Risk Methodologies, Intesa Sanpaolo
Marco holds a M.Sc. in theoretical nuclear physics (1995) and a Ph.D. in theoretical condensed matter physics (2000) from Università degli Studi di Milano. In 2000 he joined the Financial Engineering team of Banca Caboto (now IMI CIB Division of Intesa Sanpaolo), developing pricing models and applications for trading desks. In 2008 he moved to the Financial and Market Risk Management area of Intesa Sanpaolo. In 2015 he was appointed head of Fair Value Policy, developing the global fair/prudent/IPV policies and the valuation risk management framework of Intesa Sanpaolo Group. In 2021 he was appointed head of IMA Market Risk, in charge of regulatory market risk models and RWAs under Basel 2.5 and FRTB. Since Sept. 2024 he is head of Market and Counterparty Risk IMA Methodologies for Intesa Sanpaolo Group.
His work covers pricing and risk management of financial instruments, market risk, valuation risk, interest rates, XVAs, quasi-Monte Carlo, financial bubbles and portfolio optimization. He is the author of a few research papers, adjunct professor at Università di Bologna (2015-present) and at Università di Torino (2018-2023), member of Conference/Ph.D/Master Advisory Boards, and a frequent speaker at international conferences.
See also the LinkedIn profile.
12.30 – 13.30: Lunch
Afternoon Stream Chair:
To be confirmed
13.30 – 14.15: Extending VaR modelling capability to extreme scenario generation. EVT and copula implications
Vladimir Chorniy:
Managing Director, Head of Risk Model Fundamentals and Research Lab, Senior Technical Lead, BNP Paribas
Vladimir Chorniy:
Dorinel Bastide:
Senior Quantitative Analyst, BNP Paribas
Dorinel Bastide:
Dorinel Bastide: Senior Quantitative Analyst, BNP Paribas
Dorinel Bastide is a 20-year experienced senior quantitative researcher in risk management at BNP Paribas, covering clearing, systemic, operational, market, credit and climate modelling risks for XVAs, Reserves, Stress Test, ICAAP & IFRS9 metrics. He is a also member of BNP Paribas Risk Model Fundamentals and Research Lab. Dorinel is the coordinator for BNP Paribas of the research Chair Stress Test with Applied Mathematics Lab of French Ecole Polytechnique since 2018, responsible for organizing research events, designing and mentoring PhD projects in applied mathematics. He is a lecturer at French École Polytechnique within the MSc&T Data Science and AI for Business track. Dorinel holds a PhD in Applied Mathematics from University Paris-Saclay.
14.15 – 15.00: Quantifying Narratives: Applications of Large Language Models for Fed Sentiment Drift Modeling
Ivan Saroka:
Senior Quantitative Analyst, Schonfeld
Ivan Saroka:
15.00 – 15.30: Afternoon Break and Networking Opportunities
15.30 – 16.15: “Sports Trading as a Quantitative Market: Games, Signals & Equilibria”
- Market microstructure, pricing inefficiencies, liquidity dynamics, and parallels with traditional financial markets.
- Machine Learning for Prediction & Pricing – Forecasting outcomes, probability estimation, feature engineering, and turning predictive signals into trading decisions.
- AI & LLMs for Research and Trading Operations – Automated analysis, information extraction, trader productivity, and decision-support systems.
- Building Scalable Trading Systems – Data infrastructure, model deployment, execution, monitoring, and risk management.
- The Future of Quantitative Sports Trading – Agentic AI, real-time decision-making, reinforcement learning, and emerging business opportunities.
Maurizio Garro:
CFO and Head of Business Development, My Alpha investment FZCO
Maurizio Garro:
Maurizio Garro: CFO and Head of Business Development, My Alpha investment FZCO
Maurizio Garro works as a CFO and Head of Business Development at My Alpha investment FZCO. Previusly he was the senior Lead BA for the IBOR Transition programme at Lloyds Banking Group, where he lead the delivery of the changes required for models, curves and products for the transition to the alternative risk-free rates for the Front and Back book. His background is in quantitative risk management, Model Risk, Market Risk, Counterparty Credit Risk, Pricing, Liquidity and Stress Testing.
He has a long-standing experience as an internal auditor, consultant and banker in model risk management and previously worked in the Development and Validation teams of top-tier financial institutions in Europe, U.S., and the U.K. for over 15 years.
Maurizio is a frequent speaker on various topics in risk management, a member of the Institute of Internal Auditor and the Director of the Global Association of Risk Professional (GARP) London Chapter.
Maurizio Garro received his Master Degree in Economics from the Bocconi University of Milano and a certificate in Financial Risk Management (FRM) from GARP.
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