Main Conference Day One - GMT (Greenwich Mean Time, GMTZ)
The widespread adoption of AI and machine learning in finance is having a major impact on quantitative finance and the nature of quant careers in finance. What is the impact of AI on the quant profession and its implication for the training of future quants?
Buyside perspectives on quant funds and allocation trends
How are quants adapting these two key markets to their investments?
- Bruno Dupire - Head Of Quantitative Research, Bloomberg L.P.
Managing investments and trading for quant finance alongside regulatory requirements and reputational risk.
- Alexander Sokol - Executive Chairman, CompatibL
How can neural networks improve fast calibration and pricing?
Old school FFT, new era enhancements
Beyond the well-charted territory of equity portfolio construction lies the complex challenge of fixed income – a domain where traditional quantitative approaches have struggled. While equity markets benefit from simple asset properties and good data access, fixed income presents formidable obstacles. The few quantitative methods in fixed income – notably stratified sampling for passive strategies – merely scratch the surface. These techniques are incomplete workarounds rather than solutions to the fundamental challenge: the absence of high-quality fixed income risk models.
Our research tackles this through sophisticated interest rate and granular issuer spread curves, coupled with systematic factor models similar to equity approaches. We'll explore theoretical aspects of model construction, including "risk entities" that capture fixed income's multidimensional nature, along with the unique challenges of implementing these models in portfolio optimization frameworks.
Join us to discover research breakthroughs enabling truly quantitative fixed income portfolio construction and optimization.
- Alan Langworthy - Head of Fixed Income and Multi-Asset Class Analytics Research, SimCorp
- Adrian Zymolka - Managing Director, Axioma Client Experience, SimCorp
- Hamza Bahaji - Head of Financial Engineering and Investment Solutions, Amundi ETF, Indexing & Smart Beta,, Amundi
Integration of ML in credit risk
- The quadratic rough Heston (QRH) model
- Simulating SPX and VIX under QRH
- The skew-stickiness ratio (SSR)
- The SSR under QRH
- Joint fits and the SSR on one day in history
- Why does QRH fit so well?
- Jim Gatheral - Presidential Professor of Mathematics, Baruch College, CUNY
Best practice approaches
Bridging the gap with NLP
Quantitative methods for optimising equity portfolios.
Stress testing masterclass session following 2025 with an outlook for 2026
- Daniel Mayenberger - Head of Quants Markets Solutions – Digital Products, J.P. Morgan
What are the best practices for scaling quant research, managing codebases, and deploying models?
How do you validate alpha over time, stress-test your edge, and manage crowding effects?
What’s changing in how firms assess, measure, and respond to risk across portfolios?