Forecasting the elasticity of variance with LSTM recurrent neural networks

Hyun Gyoon Kim, Jeong Hoon Kim

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


Volatility forecasting is an important tool because it can be used in many different applications across the industry including risk management, derivatives trading and optimal portfolio selection. On the other hand, machine learning tends to be more accurate in making predictions when large volumes of data are involved in the system which the financial services industry tends to encounter. In this paper, we show that a fractional stochastic generalization of the elasticity of variance can contain latent features of the market elasticity of variance by using an artificial recurrent neural network architecture called LSTM (Long Short-Term Memory) to forecast the elasticity of variance. It is shown that the forecast only with the elasticity of variance data has no statistically significant difference from forward filling, but information on the Hurst exponent can improve the power of forecasting the elasticity of variance.

Original languageEnglish
Pages (from-to)209-218
Number of pages10
JournalInternational Journal of Computer Mathematics
Issue number1
Publication statusPublished - 2023

Bibliographical note

Publisher Copyright:
© 2022 Informa UK Limited, trading as Taylor & Francis Group.

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Computational Theory and Mathematics
  • Applied Mathematics


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