Podcast: Barzykin and Guéant on FX market-making

Industry quant teams up with academics to build better risk tools for FX markets


Foreign exchange is the largest financial market in the world. It is also among the least well understood. Much of the academic literature focuses on the dealer-to-client market and assumes banks simply quote prices and wait for clients to trade. The reality is far more complex, leading a trio of researchers to take a stab at designing better models for the market.

“In our two papers, we build models in which market-makers skew their quotes, but they can also unwind in the inter-bank segment of the market. So we have the dealer-to-dealer and the dealer-to-client markets,” says Olivier Guéant, professor of applied mathematics at Université Paris 1 Panthéon-Sorbonne.


Trading over-the-counter with other dealers greatly mitigates execution uncertainty, which is crucial for risk management. But it also means dealers face transaction costs and market impact, which must be factored into models. “Our model provides a framework for optimal risk management under these conditions,” explains Alexander Barzykin, director of global FX and commodities at HSBC.

Guéant and Barzykin were recent guests on an episode of sister publication Risk.net's Quantcast podcast series. The discussion focuses on two papers they co-authored with Philippe Bergault, assistant professor at Université Paris Dauphine-PSL, which were recently published in Risk.net.

The first of these works, published in August 2022, presents a market-making model for baskets of currency pairs. The framework can be used for determining the optimal level of externalisation, or hedging in the open market, and by extension, the level of risk that can be held in the portfolio and offset with client trades, or internalised .

The second paper, published in March 2023, builds on the previous work to develop a portfolio-level, multi-currency model that allows the user to determine optimal prices and hedging rates as functions of their inventory, risk aversion and market-driven parameters.

That took some tricks and ad hoc solutions. First, monetary amounts were converted to USD. That may sound trivial, but it significantly simplified the calculations. Second, they had to deal with the curse of dimensionality, a product of a partial integral differential equation in the formulation. Luckily, Guéant and Bergault had previously developed a solution that reduced the problem to a linear quadratic control problem, which turned out to be applicable to this case.

Guéant sees clear benefits in a portfolio-level approach: “The consequence for clients of the market-maker using a portfolio-level model is that they should experience better prices and less market footprint because there is less externalisation.”

Guéant and Barzykin also discuss their long collaboration, which is likely to continue with further extensions of their model – though they are confident the current version is now ready for use. Meanwhile, the model has caught the attention of the IEF/SCOR Foundation for Science, which recognised Guéant as best young researcher in finance and insurance on March 21.


00:00 Introduction

03:46 Inventory risk, internalisation and externalisation

08:45 The currency-pairs approach

10:55 The multi-currency, portfolio-level approach

22:43 Applicability and results of the multi-currency model

32:02 Risk management using the model

35:25 Collaboration between industry and academia

42:10 The next version of the model

To hear the full interview, listen in the player above, or download. If you like this Risk.net podcast, go to the free Quantcast series. Or go to the iTunes store, Spotify or Google Podcasts to listen and subscribe.

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