Quants of the year: Vladimir Lucic and Alex Tse

Most of the advances in the lucrative field of market-making happen behind closed doors. Almost no information about the algorithms that underpin the business exists in the public domain or financial literature. The limited resources available focus on equity single names or futures. The literature says little about options and nothing about option portfolios.

Risk.net’s quants of the year – Vladimir Lucic, head of quant at Marex Solutions and a visiting professor at Imperial College London, and Alex Tse, associate professor at University College London – broke that silence and unveiled a market-making framework that is already making a difference in the industry.

Their model provides a market-making algorithm for a portfolio of European-style equity options, and can be adapted for non-equity options, cryptocurrencies and even American-style options. A trader inputs signals from their trading strategy together with implied volatility, order flow information and a measure of how spreads affect flows, to derive the optimal quotes for bid and ask. The model provides quotes in a closed form, meaning they can be derived by solving relatively straightforward equations.

“The elegance of this is that with just some simplifying assumptions, you can get rather neat, closed-form solutions for the whole portfolio,” Lucic says.

The model uses two sets of inputs. One set depends on the comparison between the implied volatility surface and the traders’ subjective views on realised volatility – which is to say: their own trading signals.

The second comprises information about the market microstructure. This includes a measure of the expected activity for a particular option such as the number of orders, and an elasticity measure, which captures the sensitivity of the order flow to changes in the width of the spread.

The model will serve as the backbone of the forthcoming market-making business of Marex Solutions and has been tested by traders in other divisions of the firm. “It’s unusual that people can produce this sort of academic improvement in a model and then commercialise so quickly,” says Nilesh Jethwa, Marex Solutions’ chief executive officer.

Quant edge

Lucic’s work with Marex began with a pro-bono, part-time consultation on areas such as volatility modelling within the crypto market. The collaboration was eventually formalised in 2022, since when Lucic has built the quant team from the ground up.

Marex Solutions had previously relied on third-party models for its market-making business. Lucic’s work developing the firm’s “quant library” will, in time, provide an edge over competitors, Jethwa believes. “[It will allow us] to give clients better services, to give better prices and manage our risk more efficiently. That’s why it made sense to bring Vlad in house, not just in the quant team, but also as part of the management structure.”

The idea to develop a new market-making model for option portfolios came from Lucic himself. “Vladimir didn’t ask, nor was he asked,” says Jethwa. “He just thought we should get better at this and went and did it.”

Marex expects the equity market-making application to enter production in mid-2026. A cryptocurrency market version of the model launched in early 2025.

Four hats

Lucic started his career with TD Bank in Toronto after obtaining his PhD at the University of Waterloo in stochastic partial differential equations for non-linear filtering. He moved to London with TD Bank in 2002 and, after a short spell at Nomura, joined Barclays in 2007. There, he worked for 10 years under Vladimir Piterbarg, now head quant at Natwest Markets, in a group that has collectively received this award three times.

“I was surrounded by more senior people, excellent in what they did,” says Lucic. “It was really pushing me. I give Piterbarg credit for the intellectual rigour and balancing it with the business.”

After leaving Barclays and before joining Marex Solutions, Lucic developed the volatility investment strategies arm for non-commodities at Macquarie. In this role, he got to know Damiano Brigo and Jack Jacquier from Imperial College London, leading to a lecturer position in the university’s MSc in Mathematics and Finance, which he continues to hold.

Tse grew up and studied for his undergraduate degree in Hong Kong before moving to the UK to attend the MSc in Mathematical Finance at Warwick University. Upon his return to Hong Kong, he joined ANZ as an equity derivatives trader, a position he held for four years and which gave him a sense of what matters in volatility modelling for the industry.

He decided to switch back to academia and returned to Warwick University to get his PhD in statistics and financial mathematics. He subsequently held academic positions at Cambridge University, Imperial College London and Exeter University. In 2021 he joined University College London as an associate professor.

Tse’s research has focused on techniques of stochastic control and stochastic optimisation. Initially, the direction was skewed towards applications within financial economics and people’s investment choices under different risk preferences and market conditions. But the techniques he worked on proved transferable to many other interesting problems, including algo trading and market-making, he says.

Setting foundations

In 2022, Tse was asked to teach a master’s course in algo trading at UCL. “It was a lucky coincidence that I met Vlad at an event in 2022, and he told me about the market-making problem on options he was thinking about,” Tse recalls. “I immediately realised there were some very nice connections between what I had been working on and this particular problem.”

The two quants started work on the topic, says Lucic, aiming to create a framework that others could build on, inspired by and drawing on the work of Avellaneda and Stoikov with their model of equity market-making in 2008. “They created the framework. They planted the seeds, which then led to many different ways to look at the problem they investigated.”

“We try to use the same approach, just lifted to options. It’s all about managing the market-making within the concept of risk management of a larger portfolio,” he says. To do market-making for the whole book, one has to systematically account for the risk of all the options and skew the quotes towards the common risk at book level.

Opening doors

At Marex Solutions, the work has “opened doors” for the firm, Jethwa says. “Now we have this model and we are looking at using it to increase our equity derivative market-making capabilities.”

Lucic and Tse have collaborated already with Jean-Jacques Darmon, head of Kospi trading in the capital markets business of Marex Spectron in Singapore, to improve the volatility modelling underpinning Darmon’s trading.

Darmon says their model allows him to update data on Kospi flow in real time, making it possible to offer tighter spreads even in periods of high volatility. “It’s fundamental for us to be able to leave our quotes on in the market and to capture flow information as much as possible.” Testing of the model in this context continues and early results are promising, the firm says.

“The most interesting part is the ability to adjust the skew and integrate price expectations,” Darmon adds. In addition, the model effectively allows the traders to look at areas of the volatility surface separately. “By capturing volatility, they’re effectively capturing the volatility of volatility, which can be very different by area in the curve.” The volatility surface displays different volatility levels and variability at different strikes and tenures.

Next steps

Looking forward, Tse has been working to extend the market-making model to take account of market impact. Such a step could markedly change the trading strategy, because full delta-hedging – when accounting for how the market-maker’s own trading could move prices – would be sub-optimal.

And going forward, Lucic and Tse are considering the use of reinforcement learning to compute quotes faster. “Reinforcement learning can be used to compute the optimal spreads as a function of the state of the market and the trading book,” Tse says.

Other extensions are also possible, such as fine tuning the baseline model to cater to the specific nature of other asset classes. “Zero-day options are an interesting case,” says Tse, given the model’s ability to capture data in real time and derive optimal quotes quickly.

Pioneer

In addition to his work on market-making, Lucic was the pioneer in studying boundary conditions in common finance models such as the Cox-Ingersoll-Ross model, which is the foundation of the Heston model. “He was the first who rigorously addressed this problem, and his work was foundational in subsequent research on the subject,” says Piterbarg.

“He is an absolutely top-notch researcher. When I have questions or doubts about volatility modelling, I call him,” Piterbarg adds.

Lucic’s interest in the crypto market led to a research project with another of Risk.net’s quant of the year award winners, Artur Sepp. In that work, the quants developed volatility strategies on inverse options, which are contracts quoted in the underlying crypto and can be traded without holding a fiat account.

In other work at Marex, meanwhile, Lucic has been developing models for the firm’s structuring and quantitative investment solutions businesses.

“He’s just a good guy to have around,” Jethwa says. “He believes that with a small but effective team, we can go really far. He creates good teams and he’s very encouraging with junior people.”