Main Conference Day Two - GMT (Greenwich Mean Time, GMTZ)
Alternative data is no longer an edge – it’s an expectation. From satellite imagery to payment flows and ESG sentiment, the challenge has sifted from sourcing to signal extraction, integration, and regulatory defensibility. How are quant teams turning noise into scalable alpha across asset classes?
Exclusive guest speaker address
- Mahdi Anvari - Head of Equity Derivatives Quantitative Analysis, Millennium
- Stability and accuracy of fd solution: myths, facts and orthodoxy
- Discrete consistency: backwards, forwards, dupire and monte carlo
- Dividends, stochastic volatility, jumps
- Monte-carlo simulation, likelihood ratio tricks and adjoint differentiation
- Jesper Andreasen - Head of Quantitative Analytics, Verition Fund Management
How containers enable real-time speed
Breaking latency with modular design
From edge computing to execution
- Alexandre Antonov - Quanitative Research and Development Lead, ADIA
- Fabrizio Anfuso - Senior Technical Specialist, Bank of England
Volatility clustering meets capital thresholds
- Andrey Itkin - Adjunct Professor, New York University
Black Scholes, local volatility, and stochastic models
From prototype to production safely
- Youssef Elouerkhaoui - Managing Director, Global Head of Markets Quantitative Analysis, Citigroup
Interpreting models under Basel III
Applying annealing in non-convex spaces
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 - Cofounder, Turnleaf Analytics
Extracting alpha from analyst tone
When bid offer becomes portfolio drag
From copulas to contagion simulation
How synchrony breaks diversification logic
Developments and innovation
Combining the best aspects of entropy and volatility with respect to option pricing
- Mahdi Anvari - Head of Equity Derivatives Quantitative Analysis, Millennium
Latency, memory, and compilation trade-offs
Hardware choices for model acceleration
From notebook to deployment fast
From priors to robust allocation
Adjusting exposure to volatility shocks
Scaling allocation across correlated trees
Exposure rises with credit deterioration
This is actually an increasingly acute issue since the COVID market crash, where the hedging of large quantities of short term Equity options, sold by banks to the retail market, has had a large and paradoxical impact on the equity market itself, with the consequence of reinforcing any market drawdowns. This phenomenon was before COVID limited to a few albeit recurrent market crashes and ONLY wholesale market, which was actually the focus of my Ph. D (2006) and academic and practitioners papers (2012,2016,2017,2019) and presentations of previous business and academic conferences (QuantMinds 2016, and award of the Best investment presentation at the US Society of Actuary Conference in 2018).
- Aymeric Kalife - Associate Professor, Paris Dauphine University
Extracting forward credit risk signals
From structured notes to volatility-linked products, what's driving client demand and innovation?
How are teams evolving infrastructure to handle large-scale data, model governance, and MLOps?
What models, signals, or frameworks are helping navigate macro uncertainty and asset correlation shifts?
Where are the blind spots when models break under stress, and how can risk teams prepare?