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Exploring the predictability of range-based volatility estimators using RNNs

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  • G'abor Petneh'azi
  • J'ozsef G'all

Abstract

We investigate the predictability of several range-based stock volatility estimators, and compare them to the standard close-to-close estimator which is most commonly acknowledged as the volatility. The patterns of volatility changes are analyzed using LSTM recurrent neural networks, which are a state of the art method of sequence learning. We implement the analysis on all current constituents of the Dow Jones Industrial Average index, and report averaged evaluation results. We find that changes in the values of range-based estimators are more predictable than that of the estimator using daily closing values only.

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  • G'abor Petneh'azi & J'ozsef G'all, 2018. "Exploring the predictability of range-based volatility estimators using RNNs," Papers 1803.07152, arXiv.org.
    • Handle: RePEc:arx:papers:1803.07152
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    File URL: http://arxiv.org/pdf/1803.07152
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    Cited by:

    1. Díaz-Mendoza, Ana-Carmen & Pardo, Angel, 2020. "Holidays, weekends and range-based volatility," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    2. Ivan Letteri & Giuseppe Della Penna & Giovanni De Gasperis & Abeer Dyoub, 2022. "DNN-ForwardTesting: A New Trading Strategy Validation using Statistical Timeseries Analysis and Deep Neural Networks," Papers 2210.11532, arXiv.org.

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