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A deep learning analysis of the Chinese Yuan’s volatility in the onshore and offshore markets
Journal of the Korean Data & Information Science Society 2016;27:327-35
Published online March 31, 2016;
© 2016 Korean Data & Information Science Society.

Woosik Lee1 · Heuiju Chun2

1Department of Information Statistics, Anyang University
2Department of Statistics &Information Science, Dongduk Women’s University
Correspondence to: Heuiju Chun
Associate professor, Department of Statistics & Information Science, Dongduk Women’s University, Seoul 02748, Korea.
Received February 11, 2016; Revised February 28, 2016; Accepted March 3, 2016.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
The People’s Republic of China has vigorously been pursuing the internationalization of the Chinese Yuan or Renminbi after the financial crisis of 2008. In this view, an abrupt increase of use of the Chinese Yuan in the onshore and offshore markets are important milestones to be one of important currencies. One of the most frequently used methods to forecast volatility is GARCH model. Since a prediction error of the GARCH model has been reported quite high, a lot of efforts have been made to improve forecasting capability of the GARCH model. In this paper, we have proposed MLPGARCH and a DL-GARCH by employing Artificial Neural Network to the GARCH. In an application to forecasting Chinese Yuan volatility, we have successfully shown their overall outperformance in forecasting over the GARCH.
Keywords : Deep learning, DL-GARCH, GARCH, The Chinese Yuan, volatility.

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