Functional ARCH (fARCH) for high-frequency time series: illustration

고빈도 시계열 분석을 위한 함수 변동성 fARCH(1) 모형 소개와 예시

  • Yoon, J.E. (Department of Statistics, Sookmyung Women's University) ;
  • Kim, Jong-Min (Statistics Discipline, University of Minnesota-Morris) ;
  • Hwang, S.Y. (Department of Statistics, Sookmyung Women's University)
  • 윤재은 (숙명여자대학교 통계학과) ;
  • 김종민 (미네소타대학교 통계분야) ;
  • 황선영 (숙명여자대학교 통계학과)
  • Received : 2017.10.10
  • Accepted : 2017.10.28
  • Published : 2017.12.31


High frequency time series are now prevalent in financial data. However, models need to be further developed to suit high frequency time series that account for intraday volatilities since traditional volatility models such as ARCH and GARCH are concerned only with daily volatilities. Due to $H{\ddot{o}}rmann$ et al. (2013), functional ARCH abbreviated as fARCH is proposed to analyze intraday volatilities based on high frequency time series. This article introduces fARCH to readers that illustrate intraday volatility configuration on the KOSPI and the Hyundai motor company based on the data with one minute high frequency.

본 논문은 고빈도 시계열 자료 분석을 위한 최신 함수-변동성 functional ARCH : fARCH(1) 모형을 독자들에게 소개하고 국내 자료 적합을 예시하고 있다. fARCH(1) 모형을 KOSPI/현대차 1분 단위 고빈도 수익률 자료에 적합하여 기존의 ARCH 모형에서는 할 수 없었던 다이나믹한 일중(intraday) 변동성을 추정할 수 있음을 보여주고 있다.



Supported by : 한국연구재단


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