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초록
We investigate multivariate volatilities based on high frequency time series. The PCA (principal compo-nent 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.
키워드
high frequency; multivariate volatility; principal component; 고빈도 시계열; 다변량 변동성; 주성분
- 제목
- Choice of frequency via principal component in high-frequency multivariate volatility models
- 제목 (타언어)
- 주성분을 이용한 다변량 고빈도 실현 변동성의 주기 선택
- 저자
- 진민경; 윤재은; 황선영
- 발행일
- 2017-10
- 저널명
- 응용통계연구
- 권
- 30
- 호
- 5
- 페이지
- 747 ~ 757