Journal of the Korean Data and Information Science Society

The Korean Data and Information Science Society (한국데이터정보과학회)
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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 (Department of Information Statistics, Anyang University) ;
  • Chun, Heuiju (Department of Statistics &Information Science, Dongduk Women's University)
  • 이우식 (안양대학교 정보통계학과) ;
  • 전희주 (동덕여자대학교 정보통계학과)
  • Received : 2016.02.11
  • Accepted : 2016.03.03
  • Published : 2016.03.31
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Abstract

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.

2008년 글로벌 금융위기 이후 중국은 위안화 국제화의 점진적 추진을 진행하면서 중국상하이 외환시장과 중국홍콩 외환시장에서 거래되는 통화인 역내위안화와 역외위안화를 형성시켰다. 본 연구는 위안화 국제화와 점진적인 중국 자본계정 개방에 따라 급변하는 외환시장상황의 변동성을 정확하게 파악하기 위해서 GARCH모형 (일반화된 자기회귀 조건부이분산성모형)에 다단계인공신경망을 결합한 MLP-GARCH 모형과 GARCH모형과 기계학습의 일종인 딥러닝 (deep learning)을 통합한 DL-GARCH을 가지고 위안화 변동성예측을 비교 실험과 분석을 하였다. 비교분석 결과 DL-GARCH 모형은 MLP-GARCH보다 모형 위안화 역내 외 환율변동성 예측 면에서 더욱 더 개선된 예측값을 제공하였다. 그래서 이분산시계열모형을 딥러닝과 결합한 DL-GARCH 모형은 시계열의 환율 변동성 예측 문제에 딥러닝을 응용할 수 있음을 확인하였다. 향후 이분산시계열과 결합된 딥러닝 모형은 다른 금융시계열 데이터에 응용하여 그 일반화 가능성을 높일 수 있을 것이다.

Keywords

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