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A Consolidated Volatility Prediction with Back Propagation Neural Network and Genetic Algorithm

Author

Listed:
  • Zong Ke
  • Jingyu Xu
  • Zizhou Zhang
  • Yu Cheng
  • Wenjun Wu

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.

Suggested Citation

  • Zong Ke & Jingyu Xu & Zizhou Zhang & Yu Cheng & Wenjun Wu, 2024. "A Consolidated Volatility Prediction with Back Propagation Neural Network and Genetic Algorithm," Papers 2412.07223, arXiv.org, revised Jan 2025.
    • Handle: RePEc:arx:papers:2412.07223
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    References listed on IDEAS

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    1. Zhao, Le & Nguyen, Vinh Huy & Li, Chen, 2024. "The volatility-liquidity dynamics of single-stock ETFs," Finance Research Letters, Elsevier, vol. 69(PB).
    2. Bollerslev, Tim & Ole Mikkelsen, Hans, 1996. "Modeling and pricing long memory in stock market volatility," Journal of Econometrics, Elsevier, vol. 73(1), pages 151-184, July.
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