Main Conference Day Three - GMT (Greenwich Mean Time, GMTZ)
From realised volatility to gamma
Joint dynamics for multi risk pricing
- The Quintic Ornstein-Uhlenbeck (OU) model: a new, tractable, and production-friendly stochastic volatility model
- Explicit expressions for the VIX
- Characteristic functions of the log spot and the affine structure
- Fitting the joint SPX/VIX volatility surface across time
- Consistent modelling of spot/vol dynamics via SSR (skew stikiness ratio)
Based on joint work with Eduardo Abi Jaber (Ecole Polytechnique), Xuyang Lin (Ecole Polytechnique), and Camille Illand (AXA Investment Managers).
- Shaun Li - Quantitative Strategist, Morgan Stanley
- Davide Venturelli - Associate Director - Quantum Computing & Research Scientist, USRA
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.
- Alexandre Rubesam - Associate Professor of Finance, IÉSEG School of Management
From credit spreads to FX
Unifying yield curves with momentum
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
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.
- Anas Bakkali - Vice President, XVA Quantitative Analytics, NatWest
Stress scenarios meet non-modellability
Capturing risk in volatile bursts
Separating shape from market beliefs
From theoretical alpha to P&L
Bridging signals with real fills
VAR vs ES with flawed inputs
Worst-case loss as hedge driver
From utility to payout calibration
From inventory to market impact
Encoding sequences for financial prediction
Failover, scaling, and cost routing
Privacy-preserving validation at scale
Balancing beta drift and exposure
From tight spreads to tail risk
Global regulatory factors and considerations for credit risk modelling
When models learn hedge slippage
Replicating beta using swaps
Structured payoffs with dynamic risk
From action-value to optimal spread
Index alpha with similarity search
State transitions inform allocation shifts
Creating edge from latent space
Crisis alpha from convex payoffs
From live ticks to red flags