Preconference Day: Summits & Workshops - GMT (Greenwich Mean Time, GMTZ)
- Aous Labbane - Quant Investment Professional, Independent
A big picture look across asset classes. How are allocators and managers adapting to market shifts, evolving risk dynamics, and the rise of quant integration in multi-asset portfolios?
Key updates in global regulation. What are the compliance and structuring considerations shaping investment decisions today?
From climate data to social scoring, how are ESG metrics being woven into quant strategies? What works, what doesn’t – and how are regulations driving innovation?
How are quant managers navigating current volatility? A deep dive into instruments, hedging techniques, and risk modelling approaches.
Insights into how banks and managers are building new alpha sources and tools for the buyside.
Understanding the evolution of BMR and its implications for index-based products and quant compliance.
How are quants adapting to tail risk in a year of sharp market moves? Lessons in stress testing and model resilience.
More insights into tactical and strategic quant innovations, with a focus on execution and implementation.
A closer look at vol markets, 0DTE options, and how exchange products are being used in short-term quant strategies.
A practical exploration of how firms are constructing portfolios across asset classes while optimising for risk, cost, and return.
From frontier markets to new data sources – how are quants expanding the investable universe?
A look into how digital assets are being used integrated into institutional quant portfolios – and where the next opportunities lie.
How are systematic ETF strategies being used for liquidity, factor exposure, and tactical tilts?
- Aous Labbane - Quant Investment Professional, Independent
How infrastructure, computation, and AI are redefining the edge
- Federico Fontana - Chief Technology Officer, XAI Asset Management
Optimising data flow from tick to trade
Balancing model autonomy and oversight
From open source to enterprise integration, what does the tech buy-side want?
How research, dev, and ops work as one
From fragmented tools to integrated workflows
Balancing community and compliance need
Resilience and reliability in quant systems
Closing the gap between model and execution
Deploying models, not just training them
Protecting code, data, and IP
Predictions across infrastructure, people, and process
- Blanka Horvath - Associate Professor in Mathematical and Computational Finance, University of Oxford
- Mikko Pakkanen - Reader in Data Science and Quantitative Finance, Imperial College
An overview of deep neural networks, activation functions, loss functions and training algorithms. Practical implementation in Pytorch.
- Blanka Horvath - Associate Professor in Mathematical and Computational Finance, University of Oxford
- Mikko Pakkanen - Reader in Data Science and Quantitative Finance, Imperial College
Unsupervised hedging and pricing of derivatives, high-frequency asset return prediction.
- Blanka Horvath - Associate Professor in Mathematical and Computational Finance, University of Oxford
- Mikko Pakkanen - Reader in Data Science and Quantitative Finance, Imperial College
Deep learning volatility and calibration of (rough) stochastic volatility models.
- Blanka Horvath - Associate Professor in Mathematical and Computational Finance, University of Oxford
- Mikko Pakkanen - Reader in Data Science and Quantitative Finance, Imperial College
Market generators and generative AI.
- Blanka Horvath - Associate Professor in Mathematical and Computational Finance, University of Oxford
- Mikko Pakkanen - Reader in Data Science and Quantitative Finance, Imperial College
- Blanka Horvath - Associate Professor in Mathematical and Computational Finance, University of Oxford
- Mikko Pakkanen - Reader in Data Science and Quantitative Finance, Imperial College
- Jim Gatheral - Presidential Professor of Mathematics, Baruch College, CUNY
- Shape of the volatility surface
- Scaling of implied volatility smiles
- Monofractal scaling of realized variance
- Estimation of H
- Realized variance forecasting
- Jim Gatheral - Presidential Professor of Mathematics, Baruch College, CUNY
- The forward variance curve
- Change of measure
- The rough Bergomi model
- The rough Heston model
- The quadratic rough Heston model
- Financial meaning of parameters
- Jim Gatheral - Presidential Professor of Mathematics, Baruch College, CUNY
- Affine forward intensity models
- Affine forward volatility models
- Microstructural foundation of the QRH model
- Diamonds and the exponentiation theorem
- The leverage swap
- Moment computations
- Jim Gatheral - Presidential Professor of Mathematics, Baruch College, CUNY
- Rational approximation of rough Heston
- The HQE scheme for affine models
- The QRH scheme for quadratic rough Heston
- Parameter sensitivities
- Joint fitting of SPX and VIX smiles
- Computing the skew-stickiness ratio (SSR)
- Jim Gatheral - Presidential Professor of Mathematics, Baruch College, CUNY
- Jim Gatheral - Presidential Professor of Mathematics, Baruch College, CUNY
Markets remain volatile, regulation are tightening, and alpha is harder to source. How are peers adapting strategy, risk budgets, and execution tools across asset classes?
How do emerging mandates – from sustainability to tokenisation – fit into the quant playbook? What does this mean for signal integration and portfolio allocation? Become future proof in a fast-moving world.
How are priorities aligned with workflows and tools between research and retch teams in fast-paced environments?
What deserves in-house investment versus vendor outsourcing in today’s quant stack?