Research & Innovation

Giving Wealth Managers the Tools to Ride the Waves of a Digital Market

Stevens Researcher's Models Are Helping the Financial Services Industry Understand Volatility

A professor speaking to a class in the high-tech Hanlon Financial Systems Center. A real-time stock ticker is shown in the foreground.
Tools in the high-tech Hanlon Financial Systems Center are vital to the research work Dr. Zhenyu Cui does in stochastic volatility modeling. 'What distinguishes the School of Business is our focus on analytics and technology,' he says.

Researchers have all kinds of different motivators for the work they do.

Dr. Zhenyu Cui’s is straightforward. He likes to solve hard problems. It’s what brought him from a background in actuarial science to stochastic modeling applications in financial engineering — essentially, solving problems that help financial services companies value assets, optimize portfolios and manage risk — at Stevens Institute of Technology.

Dr. Zhenyu Cui with the Manhattan skyline in the backdrop.
Dr. Zhenyu Cui.

“I find these kinds of problems interesting,” Dr. Cui said. “I like to challenge myself by taking on hard problems. And with finance, you’re talking about the kinds of problems that have practical impact, because stochastic volatility models are an industry standard in finance.”

Stochastic volatility models are at the heart of Dr. Cui’s research interests. Stochastic models estimate the probability of various outcomes by randomizing certain variables. In the case of functions like portfolio management, this random variation is usually based on fluctuations observed in historical data, helping to predict asset performance amid uncertainty. 

Simply put, stochastic volatility models account for the fact that the volatility of asset prices fluctuates over time. Older models treated volatility as a constant, which limited their usefulness. When the latent volatility is treated as a stochastic process itself, it leads to better simulation performance and more accurate forecasting. 

Creating real-world impact in finance

In his research, Dr. Cui develops analytical tools that can improve those models — an area of great need in industry, with wealth managers chasing every advantage in creating portfolios that deliver maximum return. And while his work involves leveraging the tools in the high-tech Hanlon Financial Systems Center to implement his models, by fetching data and running simulations to test his assumptions, he begins his research more simply — writing out his mathematical formulation on paper, which helps him think about theoretical ideas. 

His first breakthrough came in 2011, with the publication of “Pricing Timer Options” in the Journal of Computational Finance. In this research, he helped create analytical tools to help Société Générale manage risk around a new volatility derivative the French investment bank launched. 

“Our approach was limited — we only considered a very particular kind of volatility model in our work,” Dr. Cui said. “But it’s been cited quite a bit by people in academia, who have worked on extensions, like considering price jumps and interest rate term structure.” 

That paper’s practical applications also attracted interests from industry, with some of its findings included in a prominent white paper series from Bloomberg, and led to some invitations to give talks about his work to quants and other professionals at Morgan Stanley’s offices in New York.

More recently, Dr. Cui advised Jinhyoung Kim, a Ph.D. student, on research around a new variable annuity product offered by New York Life to allow for additional flexibility in investment and retirement planning. The two researchers looked at how to manage risk around this annuity, using a celebrated statistical tool — the Hermite Series Expansion — that had not yet been used in an insurance context. Their paper was recently accepted by the Journal of Management Science and Engineering, and Kim received a job offer from Moody’s.

“How we educate our students is an important practical contribution to the finance world,” Dr. Cui said. “Moody’s needs talented students with analytical skills. They build those skills from an academic standpoint, doing research and writing papers, but then apply them in business, creating great value for the enterprise.” 

A community of tech-savvy scholars

When he joined Stevens in August 2015, one thing that appealed to Dr. Cui was the ability to join a strong network of collaborators. Since joining Stevens, he’s published papers and written grants with several professors, including Feng Mai, Ionut Florescu, Chihoon Lee and Rupak Chatterjee.

“What distinguishes the School of Business is our focus on analytics and technology — and because Stevens keeps hiring scholars that have created impact in those areas, it’s very easy and also joyful to work with them,” Dr. Cui said. 

Sometimes, not speaking the same “language” can be helpful, too. Dr. Cui worked with Dr. Mai on a grant from Accenture that surveyed existing research on blockchain applications — a new research avenue for Dr. Cui, but long an interest of Dr. Mai. 

Dr. Feng Mai with the New York City skyline in the background.
Dr. Feng Mai.

“I’m a technical guy, but I learned a lot from Feng about the business and information systems sides to blockchain,” he said. “Working with him has helped me get out of my confined research area and explore something new and exciting.”

It’s clear from Dr. Mai that he was able to benefit, also. 

“Zhenyu’s recent work on financial networks and consumer choice models have prompted me to think about how the theoretical insights from the models can be empirically tested,” he said. “In addition, I teach social network analytics, and Zhenyu's work provided my class with some great examples of why network models are crucial to understanding the interdependence of financial institutions.”

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