Main Conference Day ThreeThursday, 20 November 2025 - GMT (Greenwich Mean Time, GMTZ)

Where are we now? Where are we headed? What are the current practical applications?

How do we expect quantum ML to play a sharper role in finance that modern ML?

Although recent advances have made it feasible to estimate dynamic covariance matrices in high dimensions, portfolio managers face significant model risk when selecting a single forecast approach. We propose several forecast combination methods to mitigate model uncertainty and the risk of relative underperformance. These include distance-based approaches that penalize models producing forecasts that diverge excessively from others, as well as two optimization-based methods, which balance variance reduction with portfolio stability relative to individual models. Using daily U.S. stock data from 1980 to 2022, we evaluate ten individual covariance forecasting models and several combination strategies, constructing long-only minimum variance portfolios for universes of up to 1000 stocks. Forecast combinations--particularly simple ones--reduce model risk and deliver realized volatility close to the best individual models. However, optimization-based combinations often introduce additional turnover, potentially offsetting their benefits after transaction costs. Our results highlight the trade-off between improved risk forecasts and higher turnover, suggesting turnover-aware forecast combination as a direction for future research.

We reveal a geometric structure underlying both hedging and investment products. The structure follows from a simple formula expressing investment risks in terms of returns. This informs optimal product designs. Optimal pure hedging (including cost-optimal products) and hybrid hedging (where a partial hedge is built into an optimal investment product) are considered. Duality between hedging and investment is demonstrated with applications to optimal risk recycling. A geometric interpretation of rationality is presented.

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.

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).

Most counterparties do not have traded CDS instruments. This poses a challenge for calculating CVA which relies on risk-neutral default probabilities. Here we present a new model for the estimation of credit spreads using equity market data. In contrast to traditional equity-credit models, we take an empirical approach in order to determine a simple functional relationship that can be used in practice for CVA risk management. We find that our approach out-performs models that rely solely on credit data as well as alternative equity-credit models in the literature.

Bridging signals with real fills

• Tasche model
• Stochastic variance approach
• LGD driver model
• Calibration strategy

Balancing beta drift and exposure

From tight spreads to tail risk

Forward MtM (FMtM) Pricers are crucial whenever we need to evaluate a future MtM inside a simulated market for the purpose of computing a final t0 price. For instance, XVA pricing has relied on either analytical pricers for simple payoffs or regression pricers for more complicated ones. Evaluating the performance of these pricers is of utmost importance whenever the final t0 price has a heavy dependence on the distribution of FMtMs. To that end, we propose a framework based on Monte Carlo Branching Simulation to compute the Squared Error between an FMtM that we would like to assess, and the true reference FMtM which we don’t necessarily know. The Squared Error has the advantage of accounting for differences across the entire distribution of FMtMs. It can be used both as a selection criteria from a pool of available FMtM pricers, and as a tool to measure the performance of a single FMtM pricer. While it is one of the best ways to achieve that, we recognize that in some applications such as XVA this could be too strict as only the average positive part of FMtMs is needed. We thus propose a moment based approach also using Branching Simulation to evaluate the ability of an FMtM pricer to accurately calculate the average of a function of FMtMs under certain conditions. This effectively loosens the constrains of testing the entire FMtM distribution via the Squared Error.

As a numerical application, we use Regression Pricers in the context of XVA.

Global regulatory factors and considerations for credit risk modelling

When models learn hedge slippage