Quantitative Bio-Science

  • 제37권2호
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  • Pages.73-79
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  • 2018
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  • 2288-1344(pISSN)
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  • 2508-7185(eISSN)
계명대학교 자연과학연구소 (Institute of Natural Science, College of Natural Sciences, Keimyung University)
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Volatility for High Frequency Time Series Toward fGARCH(1,1) as a Functional Model

  • Hwang, Sun Young (Department of Statistics, Sookmyung Women's University) ;
  • Yoon, Jae Eun (Department of Statistics, Sookmyung Women's University)
  • 투고 : 2018.10.07
  • 심사 : 2018.11.12
  • 발행 : 2018.11.30
⟨ 이전 논문 다음 논문 ⟩

초록

As high frequency (HF, for short) time series is now prevalent in the presence of real time big data, volatility computations based on traditional ARCH/GARCH models need to be further developed to suit the high frequency characteristics. This article reviews realized volatilities (RV) and multivariate GARCH (MGARCH) to deal with high frequency volatility computations. As a (functional) infinite dimensional models, the fARCH and fGARCH are introduced to accommodate ultra high frequency (UHF) volatilities. The fARCH and fGARCH models are developed in the recent literature by Hormann et al. [1] and Aue et al. [2], respectively, and our discussions are mainly based on these two key articles. Real data applications to domestic UHF financial time series are illustrated.

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과제정보

연구 과제 주관 기관 : National Research Foundation of Korea

참고문헌

  1. Hormann S, Horvath L, Reeder R. A functional version of the ARCH model. Economet Theor 2013;29:267-288. https://doi.org/10.1017/S0266466612000345
  2. Aue A, Horvath L, Pellatt D. Functional generalized autoregressive conditional heteroskedasticity. J Time Ser Anal 2017;38:3-21. https://doi.org/10.1111/jtsa.12192
  3. Bollerslev T. Generalized autoregressive heteroskedasticity. J Econometrics 1986;31:307-327. https://doi.org/10.1016/0304-4076(86)90063-1
  4. Kim JY, Hwang SY. A threshold-asymmetric realized volatility for high frequency financial time series. Korean J Appl Stat 2018;31:205-216.
  5. Tsay RS. Multivariate time series analysis with R and financial application. John New York: John Wiley; 2014.
  6. Lee GJ, Hwang SY. Multivariate volatility for high-frequency financial series. Korean J Appl Stat 2017;30:169-180. https://doi.org/10.5351/KJAS.2017.30.1.169
  7. Tsay RS. Analysis of financial time series. 3rd Edition. New York: John Wiley; 2010.
  8. Yoon JE, Kim JM, Hwang SY. Functional ARCH (fARCH) for high-frequency time series: illustration. Korean J Appl Stat 2017;30:983-991.
  9. Yoon JE, Kim JM, Hwang SY. The fGARCH(1,1) as a functional volatility measure of ultra high frequency time series. Korean J Appl Stat 2018;31:667-675.
  10. Hansen PR, Lunde A. A forecast comparison of volatility models: does anything beat a GARCH(1,1)? J Appl Economet 2005;20:873-889. https://doi.org/10.1002/jae.800
  11. Engle RF. Dynamic conditional correlation: a simple class of multivariate generalized autoregressive conditional heteroskedasticity models. J Bus Econ Stat 2002;20:339-350. https://doi.org/10.1198/073500102288618487
  12. Jin MK, Yoon JE, Hwang SY. Choice of frequency via principal component in high-frequency multivariate volatility models. Korean J Appl Stat 2017;30:747-757. https://doi.org/10.5351/KJAS.2017.30.5.747
  13. Brockwell PJ, Davis RA. Time series : theory and methods. New York: Springer; 1991.
  14. Kowal DR, Matteson DS, Ruppert D. Functional autoregression for sparsely sampled data. J Bus Econ Stat 2018, in press.
  15. Hwang SY, Kim TY. Statistical issues on instrumental scores for non-likelihood stochastic models. QBS 2017;36:105-110.

피인용 문헌

  1. Volatility asymmetry in functional threshold GARCH model vol.41, pp.1, 2018, https://doi.org/10.1111/jtsa.12495