Main Conference Day 1: Thursday 25th September
08.00 – 09.00: Registration and Morning Welcome Coffee
Morning 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.
09.00 – 09.45: “LLMs for Time Series: an Application for Single Stocks and Statistical Arbitrage”
Recently, LLMs (Large Language Models) have been adapted for time series prediction with significant success in pattern recognition. However, the common belief is that these models are not suitable for predicting financial market returns, which are known to be almost random. We aim to challenge this misconception through a counterexample. Specifically, we utilized the Chronos model from Ansari et al. (2024) and tested both pretrained configurations and fine-tuned supervised forecasts on the largest American single stocks using data from Guijarro-Ordonnez et al. (2022). We constructed a long/short portfolio, and the performance simulation indicates that LLMs can in reality handle time series that are nearly indistinguishable from noise, demonstrating an ability to identify inefficiencies amidst randomness and generate alpha. Finally, we compared these results with those of specialized models and smaller deep learning models, highlighting significant room for improvement in LLM performance to further enhance their predictive capabilities.
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).
09.45 – 10.30: “Navigating complex Risk Models with LLM Agents”
We present Generative AI solution based on Large Language Model (LLM) Agents for finding expert-knowledge in very large and complex Risk Model libraries. The talk explores the AI methodology of our Gen AI solution, concrete examples of use cases and results, and the implications of LLM-driven agents in model development, validation, and the use in a risk management and regulatory context.
Francois Bergeaud:
Lead Quantitative Analyst, BNP Paribas
Francois Bergeaud:
Francois Bergeaud: Lead Quantitative Analyst, BNP Paribas
Lead Quantitative Analyst / Data analytics at BNP Paribas (current)
Previously , Head of RBS XVA Quantitative Research, Head of Credit Quants Commerzbank and Dresdner Kleinwort, Director of financial engineering @ AppNet (now Refinitiv)
Quantitative Analysis / Data analytics / XVA, Credit Derivatives, Pricing, Risk, FRTB , Rates, Fixed Income, Inflation,
PhD Mathematics ECP / Courant Institute (New York University) ; Graduated from Ecole Centrale Paris
10.30 – 11.00: Morning Break and Networking Opportunities
11.00 – 11.45: Pricing Derivatives with a Generative Adversarial Network Approach.
- Motivation: Deep Learning Approximations with Generative ML
- Taxonomy of Generative Methods
- An Introduction to GANs, Conditional GANs and Regression GANs
- Theoretical Principles for Training GANs
- Wasserstein GANs and the Earth-Mover Distance
- Applications
Youssef Elouerkhaoui:
Managing Director, Global Head of Markets Quantitative Analysis, Citi
Youssef Elouerkhaoui:
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.
11.45 – 12.30: Large Language Models as Co‑Analysts: Transforming Investment Research Workflows
Key talking points:
- Fine‑tuning vs. retrieval‑augmented generation (RAG) for financial documents
- Automating earnings‑call Q&A and thematic mapping across transcripts
- Chain‑of‑thought prompting for hypothesis generation and validation
- Human‑in‑the‑loop controls, hallucination detection, and compliance logging
- Productivity metrics from pilot deployments at buy‑side desks
Large Language Models (LLMs) are shifting from novelty to necessity in fundamental research. I will dissect practical architectures that couple finance‑specific RAG with guard‑railed prompting to turn LLMs into reliable “junior analysts.” Real‑world case studies show 40–60 % time savings in transcript review, consensus modelling, and sector‑theme scouting. Attendees will learn how to structure unstructured data pipelines, quantify hallucination risk, and meet stringent record‑keeping requirements under SEC and ESMA rules. The session concludes with a roadmap for integrating LLM insights into existing portfolio construction and risk systems.
David Mascio
Clinical Associate Professor, Executive Director of the FinTech Research Centre and the Faculty Director of the MS in Finance & Technology, University of Florida.
David Mascio
Dr. David A. Mascio: Clinical Associate Professor, Executive Director of the FinTech Research Centre and the Faculty Director of the MS in Finance & Technology, University of Florida.
Dr. David A. Mascio is a seasoned financial economist, an academic and quantitative strategist with over twenty-five years of experience designing and managing sophisticated investment solutions bridging rigorous financial-economic research with real-world portfolio management. David is the Founder and Managing Partner of Della Parola, where he has developed systematic multi-asset strategies leveraging advanced statistical techniques and sentiment analysis to deliver consistent, risk-adjusted returns for institutional investors, family offices, and high-net-worth individuals. Earlier in his career, he served as the Chief Investment Officer at a major trust bank, where he pioneered quantitative overlay programs and established robust risk-management frameworks.
David is Clinical Associate Professor and the Executive Director of the FinTech Research Center at the University of Florida. He participates in cutting-edge research in AI-driven investment models and mentors emerging financial analyst as the Faculty Director of the Masters of Science in Finance and Technology program in the Warrington College of Business. He is co-authoring two textbooks in Natural Language Processing & Gen AI and Financial Modeling. He is an active contributor to peer-reviewed academic journals, and is a frequent speaker, moderator and panelist at industry conferences. He holds a PhD in Finance, an MBA, and a BA in Economics.
12.30 – 13.45: Lunch
Afternoon Stream Chair:
David Mascio
Clinical Associate Professor, Executive Director of the FinTech Research Centre and the Faculty Director of the MS in Finance & Technology, University of Florida.
David Mascio
Dr. David A. Mascio: Clinical Associate Professor, Executive Director of the FinTech Research Centre and the Faculty Director of the MS in Finance & Technology, University of Florida.
Dr. David A. Mascio is a seasoned financial economist, an academic and quantitative strategist with over twenty-five years of experience designing and managing sophisticated investment solutions bridging rigorous financial-economic research with real-world portfolio management. David is the Founder and Managing Partner of Della Parola, where he has developed systematic multi-asset strategies leveraging advanced statistical techniques and sentiment analysis to deliver consistent, risk-adjusted returns for institutional investors, family offices, and high-net-worth individuals. Earlier in his career, he served as the Chief Investment Officer at a major trust bank, where he pioneered quantitative overlay programs and established robust risk-management frameworks.
David is Clinical Associate Professor and the Executive Director of the FinTech Research Center at the University of Florida. He participates in cutting-edge research in AI-driven investment models and mentors emerging financial analyst as the Faculty Director of the Masters of Science in Finance and Technology program in the Warrington College of Business. He is co-authoring two textbooks in Natural Language Processing & Gen AI and Financial Modeling. He is an active contributor to peer-reviewed academic journals, and is a frequent speaker, moderator and panelist at industry conferences. He holds a PhD in Finance, an MBA, and a BA in Economics.
13.45 – 14.30: Filtered not Mixed: Stochastic Filtering-Based Online Gating for Mixture of Large Language Models
We propose MoE-F – a formalized mechanism for combining N pre-trained Large Language Models (LLMs) for online time-series prediction by adaptively forecasting the best weighting of LLM predictions at every time step. Our mechanism leverages the conditional information in each expert’s running performance to forecast the best combination of LLMs for predicting the time series in its next step. Diverging from static (learned) Mixture of Experts (MoE) methods, our approach employs time-adaptive stochastic filtering techniques to combine experts. By framing the expert selection problem as a finite state-space, continuous-time Hidden Markov model (HMM), we can leverage the Wohman-Shiryaev filter. Our approach first constructs N parallel filters corresponding to each of the N individual LLMs. Each filter proposes its best combination of LLMs, given the information that they have access to. Subsequently, the N filter outputs are optimally aggregated to maximize their robust predictive power, and this update is computed efficiently via a closed-form expression, generating our ensemble predictor.
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.
14.30 – 15.15: Distributed Agentic Investment Committees: A Framework for Autonomous Portfolio Management Using Large Language Models and Model Context Protocol
Miquel Noguer Alonso:
Co – Founder and Chief Science Officer, Artificial Intelligence Finance Institute – AIFI
Miquel Noguer Alonso:
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).
15.15 – 15.45: Afternoon Break and Networking Opportunities
15.45 – 16.30: At the Crossroads between Modern AI and Classical Numerical Methods
Machine Learning today
- We are still at the dawn of AI and ML in finance.
- Striking results have been achieved – alongside an inevitable wave of hype.
- Modern & “traditional” numerical methods – now rebranded into Machine Learning?
Where ML fits — and where it doesn’t
- How to judge when ML adds real value.
- Practical challenges in calibration, evaluation, validation and usage inside large organisations.
- The “black box” issue: a problem of transparency, explainability and tractability.
Examples of model classes
- Models for unknown complex dependencies
- Models for unknown simple dependencies
- Models for known dependencies
Use cases in quant finance
- From LLMs and predictors to stand-alone pricing and risk engines.
- Concrete applications: XVA, IMM capital, PFE, pricing ladders, FRTB-IMA, SA-CVA, exotic calibration, and fast intraday exotic quotations.
Ignacio Ruiz:
Ignacio Ruiz:
Ignacio Ruiz: Founder, 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.
16.30 – 17.15: The Psychology of LLMs
- AI and humans perform surprisingly alike in classic behavioral psychology experiments
- These experiments show that AI shares many cognitive biases of humans and is prone to human-like errors of logical reasoning and recall
- We will examine the reasons for this surprising and perhaps even shocking finding, some superficial and some profound
- Our analysis will lead to concrete, practical techniques for avoiding these biases and increasing the reliability of AI in business settings
Alexander Sokol:
Executive Chairman and Head of Quant Research, CompatibL
Alexander Sokol:
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.
17.15 – 17.20: The Journal of FinTech (JFT) – QFC Awards
The upcoming edition of the journal will feature key trends in developments, displayed at the WBS 21st Quantitative Finance Conference – Short announcement about the awards by Blanka Horvath
17.20 – 18.00: Panel – Are We Ready for AI Colleagues?
Recently, AI agents have advanced from acting under the direct guidance of individual users to full team member roles with their own Outlook, Teams, and GitHub accounts. These new capabilities make interacting with AI similar to interacting with human team members, with profound implications for the team dynamics and morale.
- Is the transition from personal assistant to team member an incremental improvement or a seismic shift in how we interact with AI?
- Do you see AI colleagues participating in meetings and speaking up without being directly prompted?
- When this capability becomes available to you as a manager, will you allow AI to have the same degree of independence as you would a human subordinate?
- What tasks would you assign to AI, and do you expect it to require more supervision than a human employee in performing these tasks?
- In a recent study, experienced software engineers reported productivity gains from AI-enabled coding assistants while objective measures of their productivity fell. Can AI colleagues contribute to team productivity, or will the cost of instructing and supervising them be greater than the benefit?
- Are we ready for this change?
Moderator:
Alexander Sokol:
Executive Chairman and Head of Quant Research, CompatibL
Alexander Sokol:
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.
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.
Ioana Boier:
Ioana Boier:
Ioana Boier: Senior Principal Solutions Architect, NVIDIA
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.
Ignacio Ruiz:
Ignacio Ruiz:
Ignacio Ruiz: Founder, 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.
Miquel Noguer Alonso:
Co – Founder and Chief Science Officer, Artificial Intelligence Finance Institute – AIFI
Miquel Noguer Alonso:
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).
Gala Dinner: Thursday 25th September, 20.00 til late.
Lo Stand Florio Restaurant – The Gala Dinner is complimentary for all conference delegates.
The Florio Stand, commissioned by Vincenzo Florio Jr. to Ernesto Basile in the early 1900s, was built during the Belle Epoque, a fine example of Art Nouveau architecture with incursions into Moorish art. The original idea was to create a large space for sports, entertainment, entertainment and socializing.
The Gala Dinner is complimentary for all conference delegates.
Menu:
Primo Piatto – First Course
Busiate alla Norma con melanzane fritte, salsa di pomodoro, ricotta salata e basilico
Busiate alla Norma with fried aubergines, tomato sauce, salted ricotta cheese and basil
Secondo Piatto – Second Course
Filetto di spigola al pan di agrumi siciliani, patate mascotte al rosmarino e pomodoro gratinato
Seabass fillet with Sicilian citrus breading, rosemary-scented mascotte potatoes and gratinated tomato
Dessert
Cannolo con ricotta home made
Cannolo with ricotta cheese home made
Bevande – Beverage
Mineral and sparkling water
Vino Tasca D’Almerita, Sallier de la Tour, Grillo
Amaro Florio
Contact Information:
- via Messina Marine, 40 – 90123 Palermo
- +39 091 730 8360
- info@standflorio.it
- Location Map
08.00 – 09.00: Registration and Morning Welcome Coffee
Morning Stream Chair:
Peter Jaeckel:
Independent financial mathematics and analytics consultant. OTC Analytics
Peter Jaeckel:
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.
09.00 – 09.45: Pricing with Passion: The Local Occupied Volatility (LOV) Model
Volatility becomes occupied when it is function of the spot price and occupation flow, tracking the time spent by the underlying at arbitrary level. In this talk, we shed light on the Local Occupied Volatility (LOV) model, sitting conveniently between Dupire’s local volatility and fully path-dependent models. By design, the LOV model is automatically calibrated to European vanilla options and preserves market completeness. Moreover, it offers additional flexibility to fit other instruments such as American put options on non-dividend-paying stocks. This is achieved this by tuning the occupation sensitivity function, which quantifies the effect of path-dependent shocks (through the occupation flow) on volatility. We reveal this sensitivity function through the neural calibration of a general occupied volatility model, together with automatic differentiation.
Valentin Tissot-Daguette:
Quantitative Researcher, Bloomberg
Valentin Tissot-Daguette:
Valentin Tissot-Daguette: Quantitative Researcher, Bloomberg
Valentin Tissot-Daguette is a quantitative researcher in the CTO office at Bloomberg. He earned his PhD in financial mathematics from Princeton University in 2024, under the supervision of Prof. Mete Soner and Bruno Dupire. His doctoral research was recognized with the 2024 Nicola Bruti Liberati Prize for “Best PhD Thesis in Quantitative Finance,” awarded by the Bachelier Finance Society and the Department of Mathematics of the Politecnico di Milano. His research interests include exotic derivatives, volatility modeling, and stochastic control.
09.45 – 10.30: Next Gen Finite Difference Grids
Stability and accuracy of fd solution: myths, facts and orthodoxy
Discrete consistency: backwards, forwards, Dupire fd solution, monte carlo.
- local volatility and absence of arbitrage
Dividends.
- stochastic volatility
- jumps
- american options
- bids and offers of listed options
- monte-carlo simulation, likelihood ratio tricks and adjoint differentiation
Jesper Andreasen:
Head of Quantitative Analytics, Verition Fund Management LLC
Jesper Andreasen:
Jesper Andreasen: Head of Quantitative Analytics, Verition Fund Management LLC
Jesper Andreasen is head of Quantitative Analytics at Verition Fund Management LLC. Jesper has previously held senior positions in the quantitative research departments of Saxo Bank, 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.
10.30 – 11.00: Morning Break and Networking Opportunities
11.00 – 11.45: The Arbitrage Free Autoencoder
Brian Norsk Huge:
Head of Financial Modeling, Trafigura
Brian Norsk Huge:
Brian Norsk Huge: Head of Financial Modeling, Trafigura
Brian Huge is working as the head of financial modeling at Trafigura. Before joining Trafigura, Brian was head of quants at Saxo Bank and previously as a quant for 20 years at 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.
11.45 – 12.30: “A General Approach to Statistical Arbitrage”
Bruno Dupire:
Head of Quantitative Research, Bloomberg
Bruno Dupire:
Bruno Dupire: Head of Quantitative Research, Bloomberg
Bruno Dupire is the Global Head of Quantitative Research, CTO Office at Bloomberg, which he joined in 2004. Prior to this assignment in New York, he has headed the Derivatives Research teams at Société Générale, Paribas Capital Markets and Nikko Financial Products where he was a Managing Director. He is best known for having pioneered the widely used Local Volatility model (simplest extension of the Black-Scholes-Merton model to fit all option prices) in 1993 and the Functional Itô Calculus (framework for path dependency) in 2009. He is a Fellow and Adjunct Professor at NYU and he is in the Risk magazine “Hall of Fame”. He is the recipient of the 2006 “Cutting edge research” award of Wilmott Magazine and of the Risk Magazine “Lifetime Achievement” award for 2008. He runs and organizes the Bloomberg Quant (BBQ) seminar, the largest monthly event of this kind.
12.30 – 13.45: Lunch
Afternoon Stream Chair:
Nikolai Nowaczyk:
Quantitative Analytics, Director, NatWest Group
Nikolai Nowaczyk:
Nikolai Nowaczyk: Quantitative Analytics, Director, NatWest Group
13.45 – 14.30: Robust Lower Bound for a Bermudan Option
- We derive a sharp model-independent lower bound for any twice-exercisable Bermudan
- We characterise it as a dual to the optimal sub-hedge of a generalized spread type
- We show that there exists a common model (measure) under which all Bermudans with the same exercise dates are priced at their individual lower bounds
Vladimir Piterbarg:
MD, Head of Quantitative Analytics and Quantitative Development, NatWest Markets
Vladimir Piterbarg:
Vladimir Piterbarg: MD, Head of Quantitative Analytics and Quantitative Development at NatWest Markets
14.30 – 15.15: Discovering fundamental financial laws: Universal regimes across full rates spectrum
- Universal regimes for nominal, real and inflation rates
- Universal regimes across economic periods and across markets
- Fundamentals behind the universal regimes. Critical role of Central Banks. Tenor dimension
- Universal regimes across P and Q universes
- Expanding Fisher equation from rate levels to its volatilities: is knowing two out of three enough?
Maria Makarova:
Assistant Vice President Quantitative Analyst, BNP Paribas
Maria Makarova:
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:
15.15 – 15.45: Afternoon Break and Networking Opportunities
15.45 – 16.30: Decomposing Hedge Backtesting Results: Insights for Model Validation & Model Optimization
- Hedge backtesting is widely used in assessing models for pricing and hedging
- The combination of cloud computing and coding assistants can support more rapid iteration
- The literature on hedge backtesting as a formal tool for model validation is a little sparse
- Though the material there suggests a route to formalizing the analysis and bolstering heuristics
- We’ll pick up there and try to systematize things with the aid of some representative use cases
- We’ll also touch on tangential issues of model under-specification and historical calibration
Andrew McClelland:
Director, Quantitative Research, Numerix
Andrew McClelland:
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.
16.30 – 17.15: A Hair for A Square
Subtitle: Standard Error and Goodness of Fit in American Monte Carlo
Authors: Andrew Greene, Nikolai Nowaczyk & Anas Bakkali
– review statistical framework for model validation of regression pricing
– explore advanced metrics using hairy simulation
Nikolai Nowaczyk:
Quantitative Analytics, Director, NatWest Group
Nikolai Nowaczyk:
Nikolai Nowaczyk: Quantitative Analytics, Director, NatWest Group
Andrew Greene:
Andrew Greene:
Andrew Greene: Director, CVA Quantitative Analyst, NatWest Markets
Andrew gained a degree in Physics and Theoretical Physics at Cambridge University, followed by a PhD degree in experimental High Energy Physics at Imperial College London. This was followed by a transition from FORTRAN to C++ in the computer game industry, which set him up for a move into quantitative finance. After a stint at JPMorgan working for the commodity exotics desk, he moved to the then RBS, now NatWest Markets, and has been developing the xVA model and systems at NatWest Markets ever since.
17.15 – 17.20: The Journal of FinTech (JFT) – QFC Awards
The upcoming edition of the journal will feature key trends in developments, displayed at the WBS 21st Quantitative Finance Conference – Short announcement about the awards by Blanka Horvath
17.20 – 18.00: Panel – Are We Ready for AI Colleagues?
Recently, AI agents have advanced from acting under the direct guidance of individual users to full team member roles with their own Outlook, Teams, and GitHub accounts. These new capabilities make interacting with AI similar to interacting with human team members, with profound implications for the team dynamics and morale.
- Is the transition from personal assistant to team member an incremental improvement or a seismic shift in how we interact with AI?
- Do you see AI colleagues participating in meetings and speaking up without being directly prompted?
- When this capability becomes available to you as a manager, will you allow AI to have the same degree of independence as you would a human subordinate?
- What tasks would you assign to AI, and do you expect it to require more supervision than a human employee in performing these tasks?
- In a recent study, experienced software engineers reported productivity gains from AI-enabled coding assistants while objective measures of their productivity fell. Can AI colleagues contribute to team productivity, or will the cost of instructing and supervising them be greater than the benefit?
- Are we ready for this change?
Moderator:
Alexander Sokol:
Executive Chairman and Head of Quant Research, CompatibL
Alexander Sokol:
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.
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.
Ioana Boier:
Ioana Boier:
Ioana Boier: Senior Principal Solutions Architect, NVIDIA
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.
Ignacio Ruiz:
Ignacio Ruiz:
Ignacio Ruiz: Founder, 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.
Miquel Noguer Alonso:
Co – Founder and Chief Science Officer, Artificial Intelligence Finance Institute – AIFI
Miquel Noguer Alonso:
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).
Gala Dinner: Thursday 25th September, 20.00 til late.
Lo Stand Florio Restaurant – The Gala Dinner is complimentary for all conference delegates.
The Florio Stand, commissioned by Vincenzo Florio Jr. to Ernesto Basile in the early 1900s, was built during the Belle Epoque, a fine example of Art Nouveau architecture with incursions into Moorish art. The original idea was to create a large space for sports, entertainment, entertainment and socializing.
The Gala Dinner is complimentary for all conference delegates.
Menu:
Primo Piatto – First Course
Busiate alla Norma con melanzane fritte, salsa di pomodoro, ricotta salata e basilico
Busiate alla Norma with fried aubergines, tomato sauce, salted ricotta cheese and basil
Secondo Piatto – Second Course
Filetto di spigola al pan di agrumi siciliani, patate mascotte al rosmarino e pomodoro gratinato
Seabass fillet with Sicilian citrus breading, rosemary-scented mascotte potatoes and gratinated tomato
Dessert
Cannolo con ricotta home made
Cannolo with ricotta cheese home made
Bevande – Beverage
Mineral and sparkling water
Vino Tasca D’Almerita, Sallier de la Tour, Grillo
Amaro Florio
Contact Information:
- via Messina Marine, 40 – 90123 Palermo
- +39 091 730 8360
- info@standflorio.it
- Location Map
08.00 – 09.00: Registration and Morning Welcome Coffee
Morning Stream Chair:
Saeed Amen
Saeed Amen
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.
09.00 -09.45: The Paradigm Shift in Yield Curve Modeling
Andrei Lyashenko:
Head of Market Risk and Pricing Models, Quantitative Risk Management (QRM), Inc.
Andrei Lyashenko:
Andrei Lyashenko: Head of Market Risk and Pricing Models, Quantitative Risk Management (QRM), Inc.
Andrei Lyashenko is the head of Market Risk and Pricing Models at the Quantitative Risk Management (QRM), Inc. in Chicago. His team is responsible for research, implementation and support of pricing and risk models across multiple asset classes. In November 2019, he was awarded the prestigious Quant of the Year award, jointly with Fabio Mercurio from Bloomberg, L.P., for their Risk Magazine paper on modeling backward-looking rates.
Andrei is also adjunct professor at the Illinois Institute of Technology. Before joining the QRM in 1997, Andrei was on the mathematical faculty at the University of Illinois at Chicago and Iowa State University. Prior to coming to the US, he conducted academic research in applied math in Russia, Japan and Italy and published numerous research papers in the area of fluid stability in major mathematical journals. He holds a BSc in Mathematics from the Novosibirsk State University, Russia and a PhD in Mathematics from the Russian Academy of Science.
09.45 -10.30: Interest rate manifolds and mean reversion skew
- The fact that the yield curve can be accurately represented by a small number of state variables is well-established and widely used for Q- and P-measure model construction
- An interest rate manifold is a geometric representation of such low-dimensional subspace within the high-dimensional space of interest rates across all maturities
- While most model frameworks use linear representations (manifolds), there is a growing body of evidence (e.g., Kondratyev, Sokol, Andreasen, and others) that nonlinear manifolds provide more accurate representation of market data
- In a recent paper, Lyashenko, Mercurio and Sokol (LMS) developed a model framework for the evolution of interest rates along such nonlinear manifolds
- The objective of this talk is to demonstrate the profound connection between nonlinear manifolds and the mean reversion skew in conventional interest rate models
- We will show that conventional, rate-level-independent mean reversion speed assumed by nearly all interest rate models gives rise to linear manifolds while rate-level-dependent mean reversion speed gives rise to nonlinear manifolds
- We will review evidence for the presence of mean reversion skew in both Q- and P-measure, including previously unpublished results by Kondratyev and Sokol
- Adding rate-level-dependent mean reversion to conventional Q-measure models provides a pathway to modelling curve evolution along nonlinear manifolds while remaining close to the original model formulation
Alexander Sokol:
Executive Chairman and Head of Quant Research, CompatibL
Alexander Sokol:
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.
10.30 – 11.00: Morning Break and Networking Opportunities
11.00 – 11.45: Bridging Risk Parity and Variance Optimization: A Schur Complement Approach to Recursive Asset Allocation
We present a method for recursive asset allocation in portfolio construction that extends the concept of the Schur complement to connect hierarchical risk parity (HRP) with Markowitz’s minimum variance optimization (MVO). The resulting method retains the robustness of HRP while taking a step toward variance optimization and incorporating more information from the covariance matrix of returns. The Schur-based method allows the investor direct control over the amount and structure of the data being incorporated, generating a continuum of portfolio construction strategies with HRP and MVO as its boundary cases.
In addition to its practical implementation, we empirically test the method on both historical and synthetic data generated via Monte Carlo simulations. We show that appropriate parameter selection can significantly reduce portfolio volatility, while adaptive parameter selection enables substantially higher returns at equal or lower risk levels compared to both reference methods. Beyond its clear practical potential, the Schur method offers a conceptual contribution to portfolio theory by shifting the focus from choosing an “optimal method” to optimizing input parameters—making the method itself the outcome of that optimization process.
Petra Posedel Šimović:
Assistant Professor in Mathematics and Financial Mathematics, University of Zagreb
Petra Posedel Šimović:
Petra Posedel Šimović: Assistant Professor in Mathematics and Financial Mathematics, University of Zagreb, Faculty of Agriculture and Faculty of Electrical Engineering and Computing; Founder of FinQuant.
Petra completed her PhD in Statistics at ‘Luigi Bocconi’ University with Friedrich Hubalek, https://fam.tuwien.ac.at/~fhubalek/ and Ole Barndorff-Nielsen. She holds a bachelor and master degree in statistics and computer science from the Faculty of Natural Science, University of Zagreb, and was a postdoctoral researcher at FAM, Technische Univesität Wien as a START-group member, https://people.math.ethz.ch/~jteichma/index.php?content=startgroup
Petra’s research interests are in the area of Mathematical Statistics and Data Science with a special focus on stochastic volatility models, risk modelling and business analytics.
Petra has over 15 years of experience in academia and was a program director of the Quantitative Finance MBA program at the Zagreb School of Economics and Management. She is the leader of Finquant, a consulting service on business deep analytics and financial risk management
11.45 – 12.30: Forecasting inflation and creating inflation based macro systematic trading strategies
In this talk we shall discuss an approach to forecasting inflation in both DM and EM using machine learning models, contrasting it to more traditional approaches, alongside a discussion of the types of data required. We shall also be discussing how such an approach has performed historically. Later, we discuss ways we can utilise such inflation forecasts within the systematic trading strategies for macro assets such as FX, and how they have performed in a live environment.
Saeed Amen
Saeed Amen
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 – 13.45: Lunch
Afternoon Stream Chair:
Marco Bianchetti:
Head of Market and Counterparty Risk IMA Methodologies, Intesa Sanpaolo
Marco Bianchetti:
Marco Bianchetti: Head of Market and Counterparty Risk IMA 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.
13.45 – 14.30: Advances in Quantum Machine Learning
Quantum Machine Learning (QML) is an exciting area of quantum computing research that promises to be the first to deliver tangible quantum advantage and quantum utility since many of its algorithms are resistant to some types of noise and do not require large fault-tolerant quantum computers. QML is ideally suited to conducting experiments on the Noisy Intermediate-Scale Quantum (NISQ) computers. We are investigating what is behind the power of QML models and present the latest generation of QML algorithms with applications in quantitative finance.
Alexei Kondratyev:
Research Fellow: ADIA Lab and Visiting Professor: Imperial College London
Alexei Kondratyev:
Alexei Kondratyev: Research Fellow: ADIA Lab and Visiting Professor: Imperial College London
Alexei Kondratyev is Quantitative Research and Development Lead at Abu Dhabi Investment Authority (ADIA). Prior to joining ADIA in July 2021, he held quantitative research and data analytics positions at Standard Chartered, Barclays Capital and Dresdner Bank. Alexei holds MSc in Theoretical Physics from Taras Shevchenko National University of Kiev and PhD in Mathematical Physics from the Institute for Mathematics, National Academy of Sciences of Ukraine. He was the recipient of 2019 Risk magazine Quant of the Year award.
14.30 – 15.15: Quantum-Inspired Algorithms for Enhanced Cross-Sectional Dispersion Trading
There are areas in Finance where computational power has traditionally been the limiting factor — cross-sectional dispersion (CSD) is one of them. Quantum-inspired algorithms offer a breakthrough: they solve complex combinatorial optimization problems efficiently on classical hardware, removing the need to wait for quantum computing advancements.
Financial assets and portfolios exhibit rich qualitative and quantitative structures, forming complex clusters and hierarchies. Previously, unsupervised learning techniques were used to approximate this complexity. Now, leveraging quantum-inspired methods developed over decades at the Centro Superior de Investigaciones Científicas (CSIC) in Spain by the founders of the startup Inspiration-Q, it is possible to fully capture and monitor the entire clustering and hierarchical structure across asset features.
This approach enables, for the first time, an exact solution for market cross-sectional dispersion — where previously only approximations were possible.
Alejandro Rodríguez Domínguez:
Head of Quantitative Research & Analysis, Miraltabank
Alejandro Rodríguez Domínguez:
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.
15.15 – 15.45: Afternoon Break and Networking Opportunities
15.45 – 16.30: Accurate Greeks for Non-smooth Payoffs through Smoothing Techniques Combined with AAD
Dmitri Goloubentsev:
CTO, Head of Automatic Adjoint Differentiation, Matlogica
Dmitri Goloubentsev:
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.
16.30 – 17.15: “Risk-Aware Trading Portfolio Optimization”
We investigate portfolio optimization in financial markets from a trading and risk management perspective. We term this task Risk-Aware Trading Portfolio Optimization (RATPO), formulate the corresponding optimization problem, and propose an efficient Risk-Aware Trading Swarm (RATS) algorithm to solve it. The key elements of RATPO are a generic initial portfolio P, a specific set of Unique Eligible Instruments (UEIs), their combination into an Eligible Optimization Strategy (EOS), an objective function, and a set of constraints. RATS searches for an optimal EOS that, added to P, improves the objective function repecting the constraints.
RATS is a specialized Particle Swarm Optimization method that leverages the parameterization of P in terms of UEIs, enables parallel computation with a large number of particles, and is fully general with respect to specific choices of the key elements, which can be customized to encode financial knowledge and needs of traders and risk managers.
We showcase two RATPO applications involving a real trading portfolio made of hundreds of different financial instruments, an objective function combining both market risk (VaR) and profit&loss measures, constrains on market sensitivities and UEIs trading costs. In the case of small-sized EOS, RATS successfully identifies the optimal solution and demonstrates robustness with respect to hyper-parameters tuning. In the case of large-sized EOS, RATS markedly improves the portfolio objective value, optimizing risk and capital charge while respecting risk limits and preserving expected profits.
Our work bridges the gap between the implementation of effective trading strategies and compliance with stringent regulatory and economic capital requirements, allowing a better alignment of business and risk management objectives.
Marco Bianchetti:
Head of Market and Counterparty Risk IMA Methodologies, Intesa Sanpaolo
Marco Bianchetti:
Marco Bianchetti: Head of Market and Counterparty Risk IMA 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.
17.15 – 17.20: The Journal of FinTech (JFT) – QFC Awards
The upcoming edition of the journal will feature key trends in developments, displayed at the WBS 21st Quantitative Finance Conference – Short announcement about the awards by Blanka Horvath
17.20 – 18.00: Panel – Are We Ready for AI Colleagues?
Recently, AI agents have advanced from acting under the direct guidance of individual users to full team member roles with their own Outlook, Teams, and GitHub accounts. These new capabilities make interacting with AI similar to interacting with human team members, with profound implications for the team dynamics and morale.
- Is the transition from personal assistant to team member an incremental improvement or a seismic shift in how we interact with AI?
- Do you see AI colleagues participating in meetings and speaking up without being directly prompted?
- When this capability becomes available to you as a manager, will you allow AI to have the same degree of independence as you would a human subordinate?
- What tasks would you assign to AI, and do you expect it to require more supervision than a human employee in performing these tasks?
- In a recent study, experienced software engineers reported productivity gains from AI-enabled coding assistants while objective measures of their productivity fell. Can AI colleagues contribute to team productivity, or will the cost of instructing and supervising them be greater than the benefit?
- Are we ready for this change?
Moderator:
Alexander Sokol:
Executive Chairman and Head of Quant Research, CompatibL
Alexander Sokol:
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.
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.
Ioana Boier:
Ioana Boier:
Ioana Boier: Senior Principal Solutions Architect, NVIDIA
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.
Ignacio Ruiz:
Ignacio Ruiz:
Ignacio Ruiz: Founder, 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.
Miquel Noguer Alonso:
Co – Founder and Chief Science Officer, Artificial Intelligence Finance Institute – AIFI
Miquel Noguer Alonso:
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).
Gala Dinner: Thursday 25th September, 20.00 til late.
Lo Stand Florio Restaurant – The Gala Dinner is complimentary for all conference delegates.
The Florio Stand, commissioned by Vincenzo Florio Jr. to Ernesto Basile in the early 1900s, was built during the Belle Epoque, a fine example of Art Nouveau architecture with incursions into Moorish art. The original idea was to create a large space for sports, entertainment, entertainment and socializing.
The Gala Dinner is complimentary for all conference delegates.
Menu:
Primo Piatto – First Course
Busiate alla Norma con melanzane fritte, salsa di pomodoro, ricotta salata e basilico
Busiate alla Norma with fried aubergines, tomato sauce, salted ricotta cheese and basil
Secondo Piatto – Second Course
Filetto di spigola al pan di agrumi siciliani, patate mascotte al rosmarino e pomodoro gratinato
Seabass fillet with Sicilian citrus breading, rosemary-scented mascotte potatoes and gratinated tomato
Dessert
Cannolo con ricotta home made
Cannolo with ricotta cheese home made
Bevande – Beverage
Mineral and sparkling water
Vino Tasca D’Almerita, Sallier de la Tour, Grillo
Amaro Florio
Contact Information:
- via Messina Marine, 40 – 90123 Palermo
- +39 091 730 8360
- info@standflorio.it
- Location Map
Download Event PDF