用神经网络与遗传算法联合预测新兴股市波动率，精度高误差小。
A Consolidated Volatility Prediction with Back Propagation Neural Network and Genetic Algorithm
- 结合反向传播神经网络与遗传算法构建综合预测模型。
- 在新兴股市波动率预测中误差低，结果准确可靠。
- 适合金融风控、量化交易等需要精准波动率的场景。
本文提出一种基于人工智能算法的新兴股市波动率预测新方法。传统方法包括历史波动率、蒙特卡洛模拟和隐含波动率。本文设计了一个融合反向传播神经网络与遗传算法的综合模型，用于预测新兴股市未来波动率，实验结果显示该方法具有较高准确性且误差较小。
原文摘要 · Abstract (English)
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.
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