Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
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Forecasting realized volatility using data normalization and recurrent neural network

  • Yoonjoo Lee (School of Computing, KAIST) ;
  • Dong Wan Shin (Department of Statistics, Ewha Womans University) ;
  • Ji Eun Choi (Department of Statistics, Pukyong National University)
  • Received : 2023.07.08
  • Accepted : 2023.11.14
  • Published : 2024.01.31
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Abstract

We propose recurrent neural network (RNN) methods for forecasting realized volatility (RV). The data are RVs of ten major stock price indices, four from the US, and six from the EU. Forecasts are made for relative ratio of adjacent RVs instead of the RV itself in order to avoid the out-of-scale issue. Forecasts of RV ratios distribution are first constructed from which those of RVs are computed which are shown to be better than forecasts constructed directly from RV. The apparent asymmetry of RV ratio is addressed by the Piecewise Min-max (PM) normalization. The serial dependence of the ratio data renders us to consider two architectures, long short-term memory (LSTM) and gated recurrent unit (GRU). The hyperparameters of LSTM and GRU are tuned by the nested cross validation. The RNN forecast with the PM normalization and ratio transformation is shown to outperform other forecasts by other RNN models and by benchmarking models of the AR model, the support vector machine (SVM), the deep neural network (DNN), and the convolutional neural network (CNN).

Keywords

Acknowledgement

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (RS-2023-00239009, 2022R1F1A1068578, RS-2023-00242528).

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