DOI QR코드

DOI QR Code

주성분을 이용한 다변량 고빈도 실현 변동성의 주기 선택

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

  • 진민경 (숙명여자대학교 통계학과) ;
  • 윤재은 (숙명여자대학교 통계학과) ;
  • 황선영 (숙명여자대학교 통계학과)
  • 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)
  • 투고 : 2017.08.03
  • 심사 : 2017.09.10
  • 발행 : 2017.10.31

초록

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.

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.

키워드

참고문헌

  1. Andersen, T. G. and Bollerslev, T. (1997). Intraday periodicity and volatility persistence in financial markets, Journal of Empirical Finance, 4, 115-158. https://doi.org/10.1016/S0927-5398(97)00004-2
  2. Andersen, T. G., Bollerslev, T., Diebold, F. X., and Labys, P. (2003). Modelling and forecasting realized volatility, Econometrics, 71, 579-625. https://doi.org/10.1111/1468-0262.00418
  3. Bollerslev, T. (1986). Generalized autoregressive conditional heteroscedasticity, Journal of Econometrics, 31, 307-327. https://doi.org/10.1016/0304-4076(86)90063-1
  4. Choi, S. M., Hong, S. Y., Choi, M. S., Park, J. A., Baek, J. S., and Hwang, S. Y. (2009). Analysis of multivariate-GARCH via DCC modeling, Korean Journal of Applied Statistics, 22, 995-1005. https://doi.org/10.5351/KJAS.2009.22.5.995
  5. Engle, R. F. (1982). Autoregressive conditional heteroskedasticity with estimates of the variance of United Kingdom inflation, Econometrics, 50, 987-1007. https://doi.org/10.2307/1912773
  6. Engle, R. F. (2002). Dynamic conditional correlation: a simple class of multivariate generalized autoregressive conditional heteroskedasticity models, Journal of Business and Economic Statistics, 20, 339-350. https://doi.org/10.1198/073500102288618487
  7. Lee, G. J. and Hwang, S. Y. (2017). Multivariate volatility for high-frequency financial series, The Korean Journal of Applied Statistics, 30, 169-180. https://doi.org/10.5351/KJAS.2017.30.1.169
  8. Hansen, P. R. and Lunde, A. (2006). Realized variance and market microstructure noise, Journal of Business & Economic Statistics, 24, 127-161. https://doi.org/10.1198/073500106000000071
  9. Hwang, S. Y., Choi, M. S., and Do, J. D. (2009). Assessments for multivariate-GARCH models using backtesting: case study, Korean Journal of Applied Statistics, 22, 261-270. https://doi.org/10.5351/KJAS.2009.22.2.261
  10. Oh, R. and Shin, D. W. (2012). Market microstructure noise and optimal sampling frequencies for the realized variances of stock prices of four leading Korean companies, Korean Journal of Applied Statistics, 25, 15-27. https://doi.org/10.5351/KJAS.2012.25.1.015
  11. Seong, W. H. (1997). Applied Multivariate Analysis: Theory, Methods, SAS Application, Tamjin, Seoul.
  12. Tsay, R. S. (2010). Analysis of Financial Time Series (3rd ed), John Wiley, New York.
  13. Tsay, R. S. (2014). Multivariate Time Series Analysis With R and Financial Application, John Wiley, New York.
  14. Xiao, L. (2013). Realized volatility forecasting : empirical evidence from stock market indices and exchange rates, Applied Financial Economics, 23, 57-69. https://doi.org/10.1080/09603107.2012.707769
  15. Yoon, J .E. and Hwang, S. Y. (2015). Volatility computations for financial time series: high frequency and hybrid method, Korean Journal of Applied Statistics, 28, 1163-1170. https://doi.org/10.5351/KJAS.2015.28.6.1163
  16. Zhou, B. (1996). High-frequency data and volatility in foreign-exchange rates, Journal of Business & Economic Statistics, 14, 45-52.