Modeling the Volatility Clustering with Recurrent Neural Networks

DOI

Bibliographic Information

Other Title
  • リカレントニューラルネットワークによるボラティリティ変動モデリング

Description

How to model and forecast the volatility, which is the risk of financial assets, is one of the important issues in the financial institution management. Therefore, many prior studies have proposed various models reflected real financial markets. In this study, we attempt to apply the Recurrent Neural Network architecture (Simple RNN, LSTM, GRU) to modeling the volatility clustering and forecasting the future volatility. Using the Recurrent Neural Network architecture, there is a possibility that we could automatically capture structures of the conditional volatility, as ever we have designed manually. In comparison with the GARCH (1,1) model, we analysis a predictability for the conditional volatility.

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Keywords

Details 詳細情報について

  • CRID
    1390282680686222848
  • NII Article ID
    130007021161
  • DOI
    10.11497/jasmin.2017s.0_29
  • Text Lang
    ja
  • Data Source
    • JaLC
    • CiNii Articles
  • Abstract License Flag
    Disallowed

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