Physics-informed convolutional transformer for predicting volatility surface-Reference-Cited by-同舟云学术

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Physics-informed convolutional transformer for predicting volatility surface

Published:2024-01-09 Issue:2 Volume:24 Page:203-220
ISSN:1469-7688
Container-title:Quantitative Finance
language:en
Short-container-title:Quantitative Finance

Author:

Kim Soohan¹,Yun Seok-Bae¹,Bae Hyeong-Ohk²,Lee Muhyun³,Hong Youngjoon⁴

Affiliation:

1. Department of Mathematics, Sungkyunkwan University, Suwon, Republic of Korea

2. Department of Financial Engineering, Ajou University, Suwon, Republic of Korea

3. Samsung Securities, 11 Seocho-daero 74-gil, Seocho-gu, Seoul, Republic of Korea

4. Department of Mathematical Sciences, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea

Funder

Korea Foundation

Samsung Science and Technology Foundation

National Research Foundation of Korea

Ministry of Education and Technology

Korea government

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/14697688.2023.2294799

Reference42 articles.

1. The equity option volatility smile: an implicit finite-difference approach

2. A Study on Numerical Solution of Black-Scholes Model

3. Calibrating volatility surfaces via relative-entropy minimization

4. Azad R. Asadi-Aghbolaghi M. Fathy M. and Escalera S. Bi-directional ConvLSTM U-Net with densley connected convolutions. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops Oct 2019.

5. Asymptotics and calibration of local volatility models

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