Riskfuel partners with the University of Toronto on ground-breaking research for AI in capital markets
Riskfuel, a capital markets technology company using AI to revolutionize derivatives valuation and trading, announced a research partnership with the University of Toronto Department of Statistical Sciences focused on deep learning in financial modelling.
The partnership brings together some of the leading experts in AI-powered valuations for over-the-counter (OTC) derivatives, a $600 trillion market that includes interest rate swaps, credit default swaps, and structured products that aren’t traded on the stock exchange.
Riskfuel CEO Ryan Ferguson, who wrote one of the seminal papers on applying machine learning to derivatives valuation, will lead his team of financial industry veterans in working with Professor Sebastian Jaimungal, Director of the Masters of Financial Insurance program at the University of Toronto.
“We are excited to be partnering with some of University of Toronto’s world-class researchers on a huge, fast-developing new area of opportunity,” said Ferguson. “We have already demonstrated that AI in capital markets will be transformational, and working with Prof. Jaimungal will allow us to stay at the forefront of innovation.”
The University of Toronto and Riskfuel received funding from the Natural Sciences and Engineering Research Council of Canada (NSERC) for the project. Riskfuel will share its industry expertise and ground-breaking applications, while Prof. Jaimungal and his team from the Department of Statistical Sciences will explore new possibilities from the latest research on machine learning and finance.
Their research project focuses on volatility surfaces, which use geometry to provide a window into the dynamics of current conditions in financial markets. Volatility surfaces are a necessary input for an AI model looking at derivatives, but these complex objects often confound traders and quantitative modellers.
This research will contribute to a better understanding of the population of potential volatility surfaces, which will allow AI models to train on every possible future state. That means models that can respond correctly no matter the conditions in financial markets.