The Korean Journal of Applied Statistics (응용통계연구)

The Korean Statistical Society (한국통계학회)
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Choice of frequency via principal component in high-frequency multivariate volatility models

주성분을 이용한 다변량 고빈도 실현 변동성의 주기 선택

  • Jin, M.K. (Department of Statistics, Sookmyung Women's University) ;
  • Yoon, J.E. (Department of Statistics, Sookmyung Women's University) ;
  • Hwang, S.Y. (Department of Statistics, Sookmyung Women's University)
  • 진민경 (숙명여자대학교 통계학과) ;
  • 윤재은 (숙명여자대학교 통계학과) ;
  • 황선영 (숙명여자대학교 통계학과)
  • Received : 2017.08.03
  • Accepted : 2017.09.10
  • Published : 2017.10.31
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Abstract

We investigate multivariate volatilities based on high frequency time series. The PCA (principal component analysis) method is employed to achieve a dimension reduction in multivariate volatility. Multivariate realized volatilities (RV) with various frequencies are calculated from high frequency data and "optimum" frequency is suggested using PCA. Specifically, RVs with various frequencies are compared with existing daily volatilities such as Cholesky, EWMA and BEKK after dimension reduction via PCA. An analysis of high frequency stock prices of KOSPI, Samsung Electronics and Hyundai motor company is illustrated.

본 논문은 다변량 실현 변동성 계산에서 주기 선택 방안에 대해 연구하고 있다. 고빈도(high frequency) 시계열 자료에 기초한 일간 변동성인 실현변동성을 계산하고 차원 축소 방법인 주성분을 도입하였다. Cholesky 모형을 포함한 다양한 다변량 변동성모형을 주성분을 통해 비교하였으며 KOSPI/삼성전자/현대차 고빈도 수익률 자료를 이용하여 예시하였다.

Keywords

References

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