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http://dx.doi.org/10.5351/KJAS.2017.30.5.747

Choice of frequency via principal component in high-frequency multivariate volatility models  

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)
Publication Information
The Korean Journal of Applied Statistics / v.30, no.5, 2017 , pp. 747-757 More about this Journal
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.
Keywords
high frequency; multivariate volatility; principal component;
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Times Cited By KSCI : 5  (Citation Analysis)
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