The Effect of Regression Methods on the Performance of Options Pricing using Machine Learning-Reference-Cited by-同舟云学术

The Effect of Regression Methods on the Performance of Options Pricing using Machine Learning

Published:2022-07-16 Issue: Volume: Page:
ISSN:
Container-title:2022 13th International Conference on E-business, Management and Economics
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Author:

Li Lin¹^ORCID,Gong Zuoquan¹^ORCID,Zheng Li¹^ORCID,Yu Xi²^ORCID,Deng Mingsen²^ORCID

Affiliation:

1. School of Information, Guizhou University of Finance and Economics, China

2. School of Information, Guizhou University of Finance and Economics, China and International Joint Research Center for Data Science and High Performance Computing, Guizhou University of Finance and Economics, China

Funder

Natural Science and Technology Foundation of Department of Education of Guizhou Province

Natural Science and Technology Foundation of Guizhou Province

Plan Project for Guizhou Provincial Science and Technology

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3556089.3556130

Reference16 articles.

1. Leif BG Andersen. 2007. Efficient simulation of the Heston stochastic volatility model. Available at SSRN 946405(2007). Leif BG Andersen. 2007. Efficient simulation of the Heston stochastic volatility model. Available at SSRN 946405(2007).

2. Empirical option pricing: a retrospection

3. Fischer Black and Myron Scholes. 2019. The pricing of options and corporate liabilities. In World Scientific Reference on Contingent Claims Analysis in Corporate Finance: Volume 1: Foundations of CCA and Equity Valuation. World Scientific 3–21. Fischer Black and Myron Scholes. 2019. The pricing of options and corporate liabilities. In World Scientific Reference on Contingent Claims Analysis in Corporate Finance: Volume 1: Foundations of CCA and Equity Valuation. World Scientific 3–21.

4. Conformalized Kernel Ridge Regression

5. AI in Finance: Challenges, Techniques, and Opportunities