Main Conference Day Three - GMT (Greenwich Mean Time, GMTZ)
- Vincent Denoiseux - Managing Director, Head of Product Innovation and Research, iShares EMEA, BlackRock
The SABR model is a cornerstone of interest rate volatility modeling, but its practical application relies heavily on the analytical approximation by Hagan et al., whose accuracy deteriorates for high volatility, long maturities, and out-of-the-money options, admitting arbitrage. While machine learning approaches have been proposed to overcome these limitations, they have often been limited by simplified SABR dynamics or a lack of systematic validation against the full spectrum of market conditions. We develop a novel SABR DNN, a specialized Artificial Deep Neural Network (DNN) architecture that learns the true SABR stochastic dynamics using an unprecedented large training dataset (more than 200 million points) of interest rate Cap/Floor volatility surfaces, including very long maturities (30Y) and extreme strikes consistently with market quotations. Our dataset is obtained via high-precision unbiased Monte Carlo simulation of a special scaled shifted-SABR stochastic dynamics, which allows dimensional reduction without any loss of generality. Our SABR DNN provides arbitrage-free calibration of real market volatility surfaces and Cap/Floor prices for any maturity and strike with negligible computational effort and without retraining across business dates. Our results fully address the gaps in the previous machine learning SABR literature in a systematic and self-consistent way, and can be extended to cover any interest rate European options in different rate tenors and currencies, thus establishing a comprehensive functional SABR framework that can be adopted for daily trading and risk management activities.
- Marco Bianchetti - Head of Market and Counterparty Risk IMA Methodologies, Intesa Sanpaolo
The state of play in quantum computing
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
- Wim Schoutens - Professor Of Financial Engineering, University of Leuven
- Ruben Kerkhofs - PhD Student, University of Leuven
We look at XVA through the lens that underlies deep hedging, viewing it as the result of hedging under a utility function. This reveals a generalisation of standard XVAs, generating the classic CVA and FVA, along with HVA (hedging costs) and a number of non-classical contributions that arise from the realities of a real-world hedging strategy. The full framework leads to formulae that can be solved by approximation or via a deep learning approach.
- Benedict Burnett - Director, XVA Quant Lead, Barclays
- Florian Bourgey - Quantitative Researcher, Bloomberg LP