New Frontiers in Data Analytics for Risk and Asset Management

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Summary

Data science continues to influence the field quantitative finance, and at this upcoming event, we will discuss two prominent examples. At the micro level, “Deep Asset-Liability Management (ALM)” offers a feasible approach to solving the most computationally challenging optimization problems in risk- and asset management. On a macro scale, algorithmic and systematic data analysis can be used in systemic risk management, for generating “worst case” stress scenarios to identify vulnerable institutions. This event features the 2020 Swiss Risk Award winners Thomas Krabichler and Josef Teichmann, as well as Top-5 finalist Eric Schaanning who will shed further light on these developments.

Presenters

Eric Schaanning, European Central Bank
Thomas Krabichler, Ostschweizer Fachhochschule (OST)
Josef Teichmann, Department of Mathematics, ETH Zürich

Chapter Event

In our chapter events we present one or more speakers to share knowledge, updates and best practises on a specific risk topic. In small groups of risk professionals you can exchange thoughts and test ideas. More on SRA chapters. This event is jointly hosted by the chapters Risk Analytics and Models and Stress Testing and Scenario Generation.