A Consolidated Volatility Prediction with Back Propagation Neural Network and Genetic Algorithm
Computational Finance
2025-08-27 v7 Machine Learning
Neural and Evolutionary Computing
Abstract
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.
Cite
@article{arxiv.2412.07223,
title = {A Consolidated Volatility Prediction with Back Propagation Neural Network and Genetic Algorithm},
author = {Zong Ke and Jingyu Xu and Zizhou Zhang and Yu Cheng and Wenjun Wu},
journal= {arXiv preprint arXiv:2412.07223},
year = {2025}
}
Comments
6 pages, 7 figures, 1 table, The paper will be published by IEEE on conference: 2024 3rd International Conference on Image Processing, Computer Vision and Machine Learning (ICICML 2024) (V4)