The Korean Journal of Applied Statistics (응용통계연구)

The Korean Statistical Society (한국통계학회)
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FPCA for volatility from high-frequency time series via R-function

FPCA를 통한 고빈도 시계열 변동성 분석: R함수 소개와 응용

  • 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)
  • Received : 2020.09.17
  • Accepted : 2020.10.22
  • Published : 2020.12.31
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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.

본 논문은 최근 금융시계열 분야에서 자주 등장하는 고빈도 시계열 변동성 분석을 다루고 있다. 고빈도 시계열 변동성 분석을 위해 차원 축소를 목적으로 하는 함수형 주성분분석을 적용하였으며 이를 수행하는 R의 두 함수를 비교하고 있다. 응용으로서, KOSPI 고빈도 자료에 적용해 보았다.

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References

  1. Aue, A., Horvath, L., and Pellatt, D. F. (2017). Functional generalized autoregressive conditional heteroskedasticity, Journal of Time Series Analysis, 38, 3-21. https://doi.org/10.1111/jtsa.12192
  2. Conway, J. B. (1994). A Course in Functional Analysis, Springer, New York.
  3. Hadjipantelis, Dai, Ji, Han, Muller, and Wang (2017). Functional PCA in R, R manual.
  4. Hormann, S., Horvath, L., and Reeder, R. (2013). A functional version of the ARCH model, Econometric Theory, 29, 267-288. https://doi.org/10.1017/S0266466612000345
  5. Horvath, L. and Kokoszka, P. (2012). Inference for Functional Data with Applications, Springer.
  6. Kim, J.-M. and Hwang, S. Y. (2020). Functional ARCH directional dependence via copula for intraday volatility based on high-frequency financial time series, Applied Economics, online published.
  7. Ramsay, J. O. and Silverman, B. W. (1997). Functional Data Analysis, Springer, New York.
  8. Ramsay, J. O. and Silverman, B. W. (2002). Applied Functional Data Analysis, Springer, New York.
  9. Ramsay, J. O., Hooker, G. and Silverman, B.W. (2009). Functional Data Analysis with R and MATLAB, Springer, New York.
  10. Rao, C. R. (1958). Some statistical methods for comparison of growth curves, Biometrics, 14, 1-17. https://doi.org/10.2307/2527726
  11. Rao, C. R. (1987). Prediction in growth curve models (with discussion), Statistical Science, 2, 434-471. https://doi.org/10.1214/ss/1177013119
  12. Tucker, L. R. (1958) Determination of parameters of a functional relationship by factor analysis, Psychometrika, 23, 19-23. https://doi.org/10.1007/BF02288975
  13. Wang, J. L., Chiou, J. M., and Muller, H. G. (2016). Functional data analysis, Annual Review of Statistics and Its Application, 3, 257-295. https://doi.org/10.1146/annurev-statistics-041715-033624
  14. Yao, F., Muller, H. G., and Wang, J. L. (2005). Functional data analysis for sparse longitudinal data, Journal of the American Statistical Association, 100, 577-590. https://doi.org/10.1198/016214504000001745
  15. Yoon, J. E., Kim, J. M., and Hwang, S. Y. (2017). Functional ARCH (fARCH) for high-frequency time series: illustration, Korean Journal of Applied Statistics, 30, 983-991. https://doi.org/10.5351/KJAS.2017.30.6.983