• Title/Summary/Keyword: 중국 역외 위안화

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A study on the relationship between the onshore and offshore Chinese Yuan markets (중국 역내·외 위안화 현물시장간의 상호 연계성 연구)

  • Lee, Woosik;Chun, Heuiju
    • Journal of the Korean Data and Information Science Society
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    • v.26 no.6
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    • pp.1387-1395
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    • 2015
  • Since the financial crisis of 2008, the People's Republic of China has aggressively been pursuing the internationalization of the Chinese Yuan or Renminbi. In this regard, rapidly increasing use of the Chinese Yuan in the onshore and offshore markets are important milestones. This paper analyzes relationship between the onshore and offshore Chinese Yuan spot markets. Major findings of this paper are as follows : First, there is full feedback relationship between the Onshore and Offshore Chinese Yuan Markets. Second, the difference between the yuan's offshore exchange rate and the onshore was getting tight. Third, the offshore Yuan market affects on the onshore market based on the empirical tests.

A deep learning analysis of the Chinese Yuan's volatility in the onshore and offshore markets (딥러닝 분석을 이용한 중국 역내·외 위안화 변동성 예측)

  • Lee, Woosik;Chun, Heuiju
    • Journal of the Korean Data and Information Science Society
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    • v.27 no.2
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    • pp.327-335
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    • 2016
  • 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 MLP-GARCH 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.