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자연과학대학
통계학전공
Journal papers
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Forecasting realized volatility using data normalization and recurrent neural network
Title
Forecasting realized volatility using data normalization and recurrent neural network
Authors
Lee
;
Yoonjoo
;
Shin
;
Dong Wan
;
Choi
;
Ji Eun
Ewha Authors
신동완
SCOPUS Author ID
신동완
Issue Date
2024
Journal Title
Communications for Statistical Applications and Methods
ISSN
2287-7843
Citation
Communications for Statistical Applications and Methods vol. 31, no. 1, pp. 105 - 127
Keywords
asymmetry
;
normalization
;
ratio transformation
;
realized volatility
;
recurrent neural network
Publisher
Korean Statistical Society
Indexed
SCOPUS; KCI
Document Type
Article
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 Minmax (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). © 2024 The Korean Statistical Society, and Korean International Statistical Society. All rights reserved.
DOI
10.29220/CSAM.2024.31.1.105
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