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http://dx.doi.org/10.5351/KJAS.2020.36.6.805

FPCA for volatility from high-frequency time series via R-function  

Yoon, Jae Eun (Department of Statistics, Sookmyung Women's University)
Kim, Jong-Min (Statistics Discipline, University of Minnesota-Morris)
Hwang, Sun Young (Department of Statistics, Sookmyung Women's University)
Publication Information
The Korean Journal of Applied Statistics / v.33, no.6, 2020 , pp. 805-812 More about this Journal
Abstract
High-frequency data are now prevalent in financial time series. As a functional data arising from high-frequency financial time series, we are concerned with the intraday volatility to which functional principal component analysis (FPCA) is applied in order to achieve a dimension reduction. A review on FPCA and R function is made and high-frequency KOSPI volatility is analysed as an application.
Keywords
functional PCA; high-frequency time series; R-functions;
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Times Cited By KSCI : 2  (Citation Analysis)
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