Congratulations to Silvana Pesenti, assistant professor at the U of T Department of Statistical Sciences, for winning the 2022 Rising Star in quant finance award for the "originality and mathematical solidity of her work."
Pesenti, who specializes in insurance risk management, is being recognized for her innovative approach to a well-known problem in portfolio optimisation: managing the closeness between a portfolio and the benchmark against which its performance will be measured. In collaboration with Professor Sebastian Jaimungal, Pesenti decided to tackle the problem from a different perspective and redefine the closeness to a benchmark in terms of the Wasserstein distance. This tool allows managers to assess the difference between two distributions and has been used for a variety of financial applications, such as verifying the robustness of risk measures or identifying economic or financial regimes.
Pesenti's and Jaimungal's paper Portfolio Optimisation within a Wasserstein Ball explains the technical aspects of their methods and provides a simulation technique to estimate the terminal wealth resulting from the optimal strategy.
Congratulations again from the entire department!
About the Risk Awards
The Risk Awards are the longest-running and most prestigious awards for firms and individuals involved in risk transfer markets and in risk management.
The judging process takes three months, from the submission of pitch documents, through dozens of off-the-record meetings, and concludes with a due diligence phase in which clients are canvassed (also off the record) for their views on shortlisted firms.
Rising star in quant finance: Silvana Pesenti
Risk Awards 2022: New approach allows portfolios to be optimised and aligned with benchmarks
February 17, 2022, by Risk.net
For active managers of many pensions or mutual funds, portfolio optimisation comes with the additional challenge of managing the closeness between a portfolio and the benchmark against which its performance will be measured.
In the vast literature examining the problem, researchers have shared a multitude of ideas. These include adapting Harry Markovitz’s mean-variance optimisation, measuring the tracking error between the benchmark and the portfolio, and building models in which investors have varying utility functions with respect to a benchmark.
In 2020, Silvana Pesenti, assistant professor of insurance risk management at the University of Toronto, decided to tackle the problem from a different perspective and redefine the closeness to a benchmark in terms of the Wasserstein distance. This tool, which allows managers to assess the difference between two distributions, has been used for a variety of financial applications, such as verifying the robustness of risk measures or identifying economic or financial regimes.