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[학술저널]

  • 학술저널

Yongjin Jeong(Seoul National University) Sangyeol Lee(Seoul National University)

DOI : 10.7465/jkdi.2019.30.5.1187

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초록

This study designs a nonlinear ARMA-GARCH model well adapted to a recurrent neural network (RNN) scheme. The classical ARMA-GARCH model has been a functional device to model financial time series with high volatilities. However, its linear structure somewhat restricts its usage in practice because of nonlinear and nonstationary features of time series. Considering this, we incorporate hyperbolic tangent functions into the ARMA component for improving the adaptability to RNN, and suggest an RNN-adapted nonlinear ARMA-GARCH model. This model is evaluated through a comparison study with the linear ARMA-GARCH model by applying algorithmic trading strategies and forecasts to the S&P500 daily closing index. Our findings confirm the validity of the proposed method.

목차

Abstract
1. Introduction
2. Model description
3. Data analysis
4. Concluding remarks
References

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