Alumni

Thursday, January 14, 2021

Coding the future of finance

Chao Qian is preparing for the trading environment of the future. More than 60 percent of stock volume is attributed to algorithmic trading, but older generations of traders have never learned to code. “In the future, I expect that everyone will need to be able to code and be comfortable with AI and machine learning,” he affirmed. “Thanks to the Masters of Quantitative Finance (MQF) program, I will be ready.”

Internet users from Canadian rural and remote communities suffer from frequent Internet interruptions, which generally result from various network issues. The lack of human resources, expertise and support make these issues difficult to identifyand fix. Remote areas lack responsive and cost-effective operations or maintenance efforts.

Statistics and Actuarial Science PhD candidate Yilin Chen is one of two students to claim the 2020 Huawei Prize for Best Research paper by a Mathematics Graduate Student. The $4,000 prize affirms the value of Chen’s efforts to establish a framework for analyzing nonprobability survey samples in her winning paper: Doubly Robust Interference with Nonprobability Survey Samples.

Four graduate students were awarded a departmental research presentation award by the Department of Statistics and Actuarial Science, but that's not all they have in common. They all came to Waterloo because they knew of the excellence of the Statistics programs, research, and professors. Their backgrounds vary, as do their research areas, but they have all had a great experience.

A Machine Learning Approach to Portfolio Risk Management


Risk measurement, valuation and hedging form an integral task in portfolio risk management for insurance companies and other financial institutions. Portfolio risk arises because the values of constituent assets and liabilities change over time in response to changes in the underlying risk factors. The quantification of this risk requires modeling the dynamic portfolio value process. This boils down to compute conditional expectations of future cash flows over long time horizons, e.g., up to 40 years and beyond, which is computationally challenging. 

This lecture presents a framework for dynamic portfolio risk management in discrete time building on machine learning theory. We learn the replicating martingale of the portfolio from a finite sample of its terminal cumulative cash flow. The learned replicating martingale is in closed form thanks to a suitable choice of the reproducing kernel Hilbert space. We develop an asymptotic theory and prove
convergence and a central limit theorem. We also derive finite sample error bounds and concentration inequalities. As application we compute the value at risk and expected shortfall of the one-year loss of some stylized portfolios.