Candidate point selection using a self-attention mechanism for generating a smooth volatility surface under the SABR model-Reference-Cited by-同舟云学术

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Candidate point selection using a self-attention mechanism for generating a smooth volatility surface under the SABR model

Published:2021-07 Issue: Volume:173 Page:114640
ISSN:0957-4174
Container-title:Expert Systems with Applications
language:en
Short-container-title:Expert Systems with Applications

Author:

Kim Hyeonuk,Park Kyunghyun,Jeon Junkee,Song Changhoon,Bae Jungwoo,Kim Yongsik,Kang Myungjoo

Funder

Institute for Information and Communications Technology Promotion

Ministry of Science and ICT, South Korea

National Research Foundation of Korea

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,General Engineering

Reference29 articles.

1. Ackerer, D., Tagasovska, N., & Vatter, T. (2019). Deep smoothing of the implied volatility surface. SSRN 3402942.

2. Semi-parametric forecasts of the implied volatility surface using regression trees;Audrino;Statistics and Computing,2010

3. Bloch, D.A. (2019). Neural Networks Based Dynamic Implied Volatility Surface. SSRN 3492662.

4. Chang, C., & Lin, C. (2011). LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology. ACM Transactions on Intelligent Systems and Technology, 2 (27), 1–27.

5. Chen, P., FAN, R., & Lin, C. (2006). A study on SMO-type decomposition methods for support vector machines. IEEE Transactions on Neural Networks and Learning Systems, 17 (4), 893-908.

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