## Seminar Advanced Topics in Finance

The goal of the seminar is to understand, implement and improve several high level techniques from mathematical Finance and machine learning.
I recommend the following reading list and in particular the references on universal approximation in the first paper:

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We have two first meetings on tuesday, February 20 and 27, at 2 pm in HG D 7.2.

- Helmut Bölcskei, Philipp Grohs, Gitta Kutyniok, Philipp Petersen: Optimal Approximation with Sparsely Connected Deep Neural Networks, arxiv.1705.01714.
- Hans Bühler, Lukas Gonon, Josef Teichmann, Ben Wood: Deep Hedging, arXiv:1802.03042, submitted, 2018.
- Weinan E, Jiequn Han, Arnulf Jentzen: Deep learning-based numerical methods for high-dimensional parabolic partial differential equations and backward stochastic differential equations, arxiv.1706.04702.
- Terry Lyons, Rough paths, Signatures and the modelling of functions on streams, arxiv.1405.4537.
- Catherine F. Higham, Desmond J. Higham, Deep Learning: An Introduction for Applied Mathematicians, arxiv.1801.05894.
- Na Lei, Kehua Su, Li Cui, Shing-Tung Yau, David Xianfeng Gu, A Geometric View of Optimal Transportation and Generative Model, arxiv.1710.05488.