We are excited to be organising CUATS Quant Conference 2026 – our fifth annual quant conference! 

This event will feature six presentations by experts discussing the latest research in the world of quantitative finance covering topics including:

• Order Flow and Market Impact
• Factor Models for Option Risk
• Stochastic Factors in Statistical Arbitrage
• Information Risk in Foreign Exchange Markets
• Deep Hedging Derivatives with Generative Models
• Replication Crisis in Finance

This is a great opportunity to hear some fascinating talks and to meet some of today’s leaders in the quant finance industry as well as to mingle with like-minded students and practitioners active in this field. 

Please scroll down to the Agenda, which details speakers’ biographies, titles, and abstracts of their talks.

The conference will take place in the William Mong Hall at Sidney Sussex College, Cambridge, 10:00-17:00, with a drinks and canapés reception to follow. 

Lunch and refreshments will be provided during the conference. 

Ticket bookings may be made here:

https://cuats_quant_conference_2026.eventbrite.co.uk  (Student Admission)

https://cuats_quant_conference1_2026.eventbrite.co.uk   (General Admission)

We look forward to seeing you at the CUATS Quant Conference 2026 in Cambridge on Friday, 6 March, 2026!

Featuring speakers from the following companies and academic institutions

Event Agenda

Time	Topic	Speaker
10:00 – 10:30	Registration	Registration
10:30 – 10:45	Opening Remarks	Farouk Hadeed President, CUATS
10:45 – 11:30	Learning to Trade: From Market Simulation to Hedging We discuss the use of modern ML/AI methods for trading derivatives, a broad departure from the traditional derivatives risk management paradigm. Instead of focusing on solvable low-parametric models we build market simulators which capture broad features of the market, and the learn to trade and risk manage in the simulated environments with reinforcement learning techniques. We present the broad outline of the approach, applications, and challenges in implementation.	Hans Buehler, Visiting Professor, University of Oxford Prof. Dr. Hans Buehler is a leading quantitative finance executive and researcher known for pioneering work in machine-learning-based derivatives hedging. He led the Equity Derivatives Quant Research at Deutsche Bank before joining J.P. Morgan in 2008, where he became Global Head of Equities Quantitative Research and later co-lead the Markets’ Analytics, Automation & Optimization “AI” effort. At J.P. Morgan, he spearheaded the “deep hedging” framework, applying neural networks to trading and risk management—work that earned him Risk.net’s 2022 Quant of the Year award. In 2022 Hans joined XTX Markets as Deputy CEO and later Co-CEO. He has served as a visiting professor at TU Munich and is now a visiting professor at the Mathematical Institute of the University of Oxford. Hans holds a PhD in Financial Mathematics from TU Berlin.
11:30 – 12:15	Market Making and Information Risk in Foreign Exchange Markets Foreign exchange is a highly fragmented, de-localised marketplace that combines significant OTC and open market segments. Dealers provide bid and ask prices to their clients at which they are happy to buy and sell, respectively. To manage the inventory risk, dealers can skew their quotes and hedge in the interbank market. In addition, dealers have to mitigate risks directly or indirectly related to the information they reveal to their clients and to the whole market through their actions, the risks associated with the possible informational advantage of their clients, as well as the latency risks. In this talk, we are going to focus on the optimal control approach to OTC market making, specifically targeting response to risks via pricing, execution, and possibly rejection of client requests. Reputation feedback arising from endogenous modulation of client trading intensity by the dealer’s own past actions will also be discussed.	Alexander Barzykin, Senior Quantitative Researcher, HSBC   Alexander joined HSBC FX eRisk as a director in 2015 and is currently leading the design and development of spot FX execution algorithms. He was previously building the algorithmic execution suite at RBS, in equities and fixed income. Alexander moved to finance in 2007 from the field of theoretical chemical physics. He received his PhD in physics and mathematics from the Moscow Institute for Physics and Technology in 1989 and held senior research positions at the Institute of Chemical Physics, Russian Academy of Sciences and the National Institute of Advanced Industrial Science and Technology, Japan.
12:15 – 13:30	Lunch in Sidney Sussex College	Lunch in Sidney Sussex College
13:30 – 14:15	A Unified Theory of Order Flow, Market Impact, and Volatility We propose a microstructural model for the order flow in financial markets that distinguishes between core orders and reaction flow, both modeled as Hawkes processes. This model has a natural scaling limit that reconciles a number of salient empirical properties: persistent signed order flow, rough trading volume and volatility, and power-law market impact. In our framework, all these quantities are pinned down by a single statistic H_0, which measures the persistence of the core flow. Specifically, the signed flow converges to the sum of a fractional process with Hurst index H_0 and a martingale, while the limiting traded volume is a rough process with Hurst index H_0-1/2. No-arbitrage constraints imply that volatility is rough, with Hurst parameter 2H_0-3/2, and that the price impact of trades follows a power law with exponent 2-2H_0.  The analysis of signed order flow data yields an estimate H_0 close to 3/4. This is not only consistent with the square-root law of market impact, but also turns out to match estimates for the roughness of traded volumes and volatilities remarkably well.	Mathieu Rosenbaum, Professor, École Polytechnique Mathieu Rosenbaum is a full-time professor at Ecole Polytechnique, where he holds the chair “Analytics and Models for Regulation”. He obtained his Ph.D from University Paris-Est in 2007. After being Assistant Professor at École Polytechnique, he became Professor at University Pierre et Marie Curie (Paris 6) in 2011. Mathieu Rosenbaum’s research mainly focuses on statistical finance problems, such as market microstructure modeling or designing statistical procedures for high frequency data and on regulatory issues, especially in the context of high frequency trading. He received the Europlace Award for Best Young Researcher in Finance in 2014 and the Louis Bachelier Prize in 2020. He is also the recipient, along with Jim Gatheral, of Risk.net‘s 2021 Quant of the Year award.
14:15 – 15:00	Stochastic Factors in Statistical Arbitrage: A Robust Perspective Modern statistical arbitrage strategies trade on residuals obtained from a basket of securities. The simplest of these is pairs trading, which will be main example in this talk, where the basket consists of two securities and the investor trades on their spread. Constructing such trading strategies requires statistical estimation of price dynamics and, often, obtaining stochastic non-tradeable factors to help inform investment decisions. Due to low signal-to-noise ratios present in financial data, only certain quantitates can be accurately estimated, while others are, explicitly or implicitly, assumed. To shed light on how this affects trading performance, we study a mathematical growth-optimization problem under ergodicity and model uncertainty designed with statistical arbitrage in mind. Our main result explicitly characterizes the robust optimal trading strategy under two regimes: (i) where the stochastic factor dynamics are known perfectly and (ii) where only the long run behaviour of the factors is known. These lead to new interpretable insights on the role of stochastic factors, which I will discuss through several explicit examples.	David Itkin, Assistant Professor, London School of Economics David Itkin is an Assistant Professor in the Probability in Finance and Insurance group within the Statistics Department at LSE. He is an affiliate of the Data Science Institute (DSI) at the LSE and an Honorary Lecturer at Imperial College London. Prior to this, he was a Chapman Fellow in the Mathematics Department at Imperial College London. David holds a PhD in Mathematical Sciences from Carnegie Mellon University and is the recipient of Risk.net’s 2026 Rising Star in Quant Finance Award.
15:00 – 15:30	Break with Refreshments	Break with Refreshments
15:30 – 16:15	A Data-Driven Factor Model for Option Risk Option pricing and risk management both rely on understanding how the volatility surface moves over time. Many models can fit a surface, but few give a realistic and simple description of how option prices co-move from one day to the next. Motivated by modelling the risk of complex option books, we take a data‑driven look at the correlations of option PnLs across strikes and maturities. Focusing on returns rather than prices lets us separate the problem of fitting the current volatility surface from the problem of modelling its dynamics. After a reparameterisation of moneyness and maturity, we find that option return correlations depend primarily on distance in this 2D coordinate space. PCA reveals an eigenstructure consistent with a heat‑type stochastic PDE acting on the surface. This gives a clean “physical” picture of how shocks propagate across the surface and puts familiar stylized facts into a compact mathematical framework. The patterns we see are very stable over decades of data. Although each asset class has its own PCA fingerprint, we show that a model with only a handful of parameters — encoding an overall effective “stiffness” of the volatility surface — captures the main dynamics. These parameters are remarkably consistent across equity indices, commodities, and FX. Building directly on the PCA results, we construct a practical factor model that explains option risk quite well, making it suitable for modelling risk of complex option portfolios.	Pavel Ioselevich, Vice President, Capital Fund Management Pavel Ioselevich is a Vice President – Quantitative Researcher at Capital Fund Management (CFM), focused on option pricing and modelling risk of option portfolios. Pavel holds a PhD in theoretical condensed matter physics from the Landau Institute for Theoretical Physics.
16:15 – 17:00	Is There a Replication Crisis in Finance? Recent years have seen growing concern that many empirical findings in finance fail to replicate out of sample or in real-world trading. This presentation examines whether finance is experiencing a replication crisis similar to that documented in other scientific disciplines. We review evidence showing that a large fraction of published return anomalies lack both internal validity—due to multiple testing, data-snooping, and in-sample overfitting—and external validity when confronted with realistic implementation constraints. We discuss how selection bias, survivorship bias, parameter tuning, and unobserved researcher degrees of freedom can systematically inflate reported performance, even when standard statistical tools such as t-statistics are applied. Particular emphasis is placed on the challenges posed by noisy, non-stationary financial time series and by the difficulty managers face in assessing how many implicit trials underlie an observed strategy. Finally, we outline a set of pragmatic mitigation techniques—hypothesis-driven research, parameter discipline, regularization, bootstrapping, ensemble methods, and rigorous out-of-sample testing—while emphasizing their limits. The key conclusion is that overfitting cannot be fully eliminated: genuine alpha exists but typically operates near the boundary of statistical significance. Sustainable quantitative investing therefore requires humility, robust research processes, and continuous adaptation rather than reliance on apparently “proven” historical results.	Marco Dion, Head of Quant Research, Qube Research & Technologies Marco Dion currently serves as Portfolio Manager and Head of Research at Qube Research & Technologies (QRT), a global quantitative investment manager focused on data-driven systematic strategies across multiple asset classes and geographies. Before joining QRT in October 2017, Marco had a long tenure at J.P. Morgan, where he was a Managing Director heading the Quant Research efforts in Europe then globally before leading the Central Risk Book (CRB) trading desk, HFT and Systematic Trading in Europe. Prior to his time at J.P. Morgan, Marco worked in quantitative roles in various prop trading firms covering options trading and equity stat-arb
17:00 – 18:30	Drinks and Canapés Reception	Drinks and Canapés Reception

Event Highlights