Deep Learning (Rough) Volatility – Blanka Horvath, Kings College London
The talk is based on the paper “Deep Learning Volatility” (with Aitor Muguruza and Mehdi Tomas) available on ArXiv: https://arxiv.org/abs/1901.09647 and SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3322085#
About the speaker
Blanka is a Honorary Lecturer in the Department of Mathematics at Imperial College London and a Lecturer at King’s College London. Her research interests are in the area of Stochastic Analysis and Mathematical Finance.
Her interests include asymptotic and numerical methods for option pricing, smile asymptotics for local- and stochastic volatility models (the SABR model and fractional volatility models in particular), Laplace methods on Wiener space and heat kernel expansions.
Blanka completed her PhD in Financial Mathematics at ETH Zürich with Josef Teichmann and Johannes Muhle-Karbe. She holds a Diploma in Mathematics from the University of Bonn and an MSc in Economics from the University of Hong Kong.
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