NEWS & EVENTS

By Shawn Hutchins

Since its founding in 2002, the Center for Computational Finance and Economic Systems (CoFES) has collaborated with departments at Rice University’s Brown School of Engineering, School of Social Sciences and the Jones School of Business to offer interdisciplinary training programs and courses.

“We have been introducing exciting new lecture series and courses in computational finance and applied time series and forecasting that are beneficial to many graduate degrees. This year we had a record number of students who responded with great interest,” said Kathy Ensor, Rice’s Noah G. Harding Professor of Statistics and director of CoFES. “The courses not only broaden students’ learning, but also introduce them to technologies being used every day at top-tier global financial institutions.”

In STAT 499/699 – Computational Finance, students acquire a deeper understanding of a specific topic in quantitative finance. Two sections of this course are offered each semester for one credit. Both courses successfully transitioned to on-line delivery mid-semester as Rice responded to the Covid-19 pandemic.

This past spring, statistics doctoral graduate student Michael Jackson led section one that featured a series of Quantopian lectures on the concepts of high-frequency trading and use of quantitative models of extremely large datasets to analyze dynamic financial markets and securities. 

“The course provided students with the opportunity to learn in a more collaborative environment and discuss topics around some of the most advanced quantitative finance subject areas,” said Jackson, who is in his fourth year of graduate studies at Rice. His research with CoFES under Dr. Ensor’s direction focuses on time series analysis with applications in financial modeling and risk management.

In addition to attending lectures, Jackson said students delivered speed presentations upon the completion of the course. “The students benefited from opportunities to present advanced topics.”

Section two focused on how blockchain technology and cryptocurrencies are empowering positive and radical change in increasingly globalized and data-driven societies. The lectures were led by Manolo Sánchez, adjunct professor in management – operations management at Rice’s Jones Graduate School of Business and James Aman, founder and chief technology officer of Topl.

Sánchez is currently a director at Fannie Mae (FNM), OnDeck Capital (ONDK) and BanCoppel (Mexico) after having served as chairman and CEO of BBVA Compass for 10 years. He is also a Founding Advisor to SpringLabs, a fintech company developing a blockchain-based, peer-to-peer network to allow financial institutions to share credit and identity data. At Rice, he teaches disruption in financial services with a focus on cryptocurrencies and blockchain.

“Moving the course online within such a short time frame was a bit challenging, however, the students' interest remained keen throughout the process and we maintained a very high level of participation to the end,” said Aman. “In the transition, we revamped the course presentation and focused on more live coding exercises.”

A guest speaker from Celo also gave a talk about their company’s focus to expand financial inclusion through blockchains.

In 2015, Aman attend one of the first workshops on cryptoledgers and cryptocurrencies hosted by CoFES. At the time he was working on his doctorate in atomic/molecular physics. He earned his Ph.D. and M.S. in 2019 and 2016 and his bachelor’s in physics and in chemical physics in 2011, all from Rice University.

Aman said the 2015 CoFES lecture was, “The first time I internalized the scope and size of problems that blockchains could solve. I was inspired by how blockchain is a great societal and financial tool that can be used to generate value through accountability of actions throughout a supply chain.”

Both Aman and Sanchez will lead the STAT 499/699 Blockchain session this fall. John Dobelman, a professor in the practice of statistics and associate director of CoFES, will lead the fall session in Computational Finance.