Speaker's bio
Erich Walter
Farkas
(ETH Zurich)
Walter Farkas is a Professor of Quantitative Finance at the Department of Finance at the University of Zurich (UZH) and an Associate Faculty member of the Department of Mathematics of ETH Zurich. Prof. Farkas is also the program director of the Master of Science in Quantitative Finance, a specialized degree jointly offered by the UZH and ETH since 2003.
He is member of the Executive Education Board of the University of Zurich, heading its Certificate of Advanced Studies (CAS) in Risk Management for Banking and Finance, and is the Director of Teaching Center of the Department of Finance of UZH.
Walter is a Faculty member of the Swiss Finance Institute and also a Board member and founder of the Swiss Risk Association, a non-profit organization and an open forum for facilitating the dialog on risk management, after serving for eight years, 2013-2021, as one of the Co-presidents.
Before Zurich, Walter was researching and teaching in Munich, Regensburg, Jena, Mainz and Bucharest. He holds a Habilitation from the Ludwig-Maximilians-University of Munich, a Doctorate from the Friedrich-Schiller-University of Jena, a Master of Science and Licence Diploma in Mathematics from the University of Bucharest. He has also a Certificate of Advanced Studies for Board members from Bern-Rochester.
His research interest involves Mathematical Finance, Quantitative Risk Management, Volatility Modeling, Risk Measures, and Option Pricing.
AI in portfolio optimization: challenges and opportunities
Artificial intelligence (AI) automates statistical analysis with the goal of generating highly accurate outcome distributions. The reliability of these predictions, however, depends critically on whether future observations align with past data. For AI to be effective, the patterns it identifies must recur over time. In the context of efficient financial markets, where identified performance patterns are unlikely to persist, this poses a unique challenge. In this talk, based on an ongoing research project with Andreas Zimmermann, we explore the transformative opportunities AI brings to portfolio optimization. Specifically, we will discuss how AI enables the quantification of complex interdependencies among financial instruments and facilitates the integration of diverse outcome distributions across varying market conditions, ultimately enhancing portfolio optimization strategies.