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Deep Learning (Rough) Volatility - Blanka Horvath, Kings College London
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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.
About the Turing
The Alan Turing Institute, headquartered in the British Library, London, was created as the national institute for data science in 2015. In 2017, as a result of a government recommendation, we added artificial intelligence to our remit.
The Institute is named in honour of Alan Turing (23 June 1912 – 7 June 1954), whose pioneering work in theoretical and applied mathematics, engineering and computing are considered to be the key disciplines comprising the fields of data science and artificial intelligence.
To learn more about what we do, watch our new video on 'What is The Alan Turing Institute'
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Deep Learning (Rough) Volatility Paper Review
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Deep Learning (Rough) Volatility - Blanka Horvath, Kings College London
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Blanka Horvath, King's College London, on machine learning and volatility modelling
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