Deep learning volatility: a deep neural network perspective on pricing and calibration in (rough) volatility models-Reference-Cited by-同舟云学术

Deep learning volatility: a deep neural network perspective on pricing and calibration in (rough) volatility models

Published:2020-10-26 Issue:1 Volume:21 Page:11-27
ISSN:1469-7688
Container-title:Quantitative Finance
language:en
Short-container-title:Quantitative Finance

Author:

Horvath Blanka¹,Muguruza Aitor²,Tomas Mehdi³

Affiliation:

1. King's College London, The Alan Turing Institute London, UK

2. Imperial College London & NATIXIS London, UK

3. CMAP & LadHyx, École Polytechnique, Palaiseau, France

Funder

H2020 European Research Council

Publisher

Informa UK Limited

Subject

General Economics, Econometrics and Finance,Finance

Link

https://www.tandfonline.com/doi/pdf/10.1080/14697688.2020.1817974

Reference58 articles.

1. On the short-time behavior of the implied volatility for jump-diffusion models with stochastic volatility

2. Alòs, E., García-Lorite, D. and Muguruza, A., On smile properties of volatility derivatives and exotic products: Understanding the VIX skew. arXiv:1808.03610, 2018.

3. Approximation and estimation bounds for artificial neural networks

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