A Consolidated Volatility Prediction with Back Propagation Neural Network and Genetic Algorithm
This paper provides a unique approach with AI algorithms to predict emerging stock markets volatility. Traditionally, stock volatility is derived from historical volatility, Monte Carlo simulation and implied volatility as well. In this paper, the writer designs a consolidated model with back -propagation neural network and genetic algorithm to predict future volatility of emerging stock markets and found that the results are quite accurate with low errors.
Authors
- Zong Ke (ORCID: https://orcid.org/0009-0006-0450-5614)
- Jingyun Xu (ORCID: https://orcid.org/0000-0001-5511-6826)
- Yu Cheng (ORCID: https://orcid.org/0009-0003-9436-674X)
- Wenjun Wu (ORCID: https://orcid.org/0000-0002-1627-5880)
- Zizhou Zhang
Institutions
- University of Illinois Urbana-Champaign (US)
- National University of Singapore (SG)
- Northern Arizona University (US)
- Columbia University (US)
Publication Details
- Published
- 2024-11-22
- DOI
- https://doi.org/10.1109/icicml63543.2024.10958142
- Citations
- 14
- Primary Topic
- Advanced Algorithms and Applications
- Type
- article
- Field-Weighted Citation Impact
- 4.60