Preconference Day: Summits & WorkshopsMonday, 16 November 2026 - GMT (Greenwich Mean Time, GMTZ)

A big picture look at the technology stack powering modern quant finance. How are firms leveraging machine learning, quantum computing, GPU acceleration, and cloud infrastructure to navigate complexity, scale operations, and stay ahead in an increasingly data-driven landscape?

Traditional fixed-income risk models often rely on historical scenarios, linear factor assumptions, or manually specified shocks. These approaches can struggle when markets enter regimes that are poorly represented in the historical record, particularly when correlations shift, curves twist nonlinearly, or tail events propagate across markets in unexpected ways.

This session introduces YieldGAN, a generative modelling framework for simulating realistic forward-looking yield curve scenarios. The model learns the joint dynamics of sovereign yield curves and generates full distributions of plausible future paths, capturing nonlinear dependencies, curve-shape changes, volatility clustering, and regime-sensitive correlation structures.

We will discuss how this approach can support a range of quant workflows, including regime-break analysis, PCA/factor-structure forecasting, covariance and tail-risk estimation, stress testing, scenario generation and hedging. The session will also cover empirical validation of generated curve behaviour, including out-of-sample tests of distributional properties and correlation dynamics.

The goal is to show how synthetic market data can become a practical tool for fixed-income quants: not as a replacement for traditional models, but as a new layer for testing portfolios against plausible market environments that have not yet occurred.

• Brief introduction to RAG systems
• Metrics and automated evaluation frameworks
• Application to a use case
• Threshold calibration and monitoring
• Key conclusions