The fGARCH(1,1) as a functional volatility measure of ultra high frequency time series함수적 변동성 fGARCH(1, 1)모형을 통한 초고빈도 시계열 변동성
- Other Titles
- 함수적 변동성 fGARCH(1, 1)모형을 통한 초고빈도 시계열 변동성
- Authors
- 윤재은; 김종민; 황선영
- Issue Date
- Oct-2018
- Publisher
- 한국통계학회
- Keywords
- 함수적 변동성; 초고빈도 시계열; 함수적-GARCH 모형; fGARCH; ultra high frequency; functional volatility
- Citation
- 응용통계연구, v.31, no.5, pp 667 - 675
- Pages
- 9
- Journal Title
- 응용통계연구
- Volume
- 31
- Number
- 5
- Start Page
- 667
- End Page
- 675
- URI
- https://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/1971
- DOI
- 10.5351/KJAS.2018.31.5.667
- ISSN
- 1225-066X
- Abstract
- When a financial time series consists of daily (closing) returns, traditional volatility models such as autoregressive conditional heteroskedasticity (ARCH) and generalized ARCH (GARCH) are useful to figure out daily volatilities. With high frequency returns in a day, one may adopt various multivariate GARCH techniques (MGARCH) (Tsay, Multivariate Time Series Analysis With R and Financial Application, John Wiley, 2014) to obtain intraday volatilities as long as the high frequency is moderate. When it comes to the ultra high frequency (UHF) case (e.g., one minute prices are available everyday), a new model needs to be developed to suit UHF time series in order to figure out continuous time intraday-volatilities. Aue {\it et al.} (Journal of Time Series Analysis, 38, 3-21, 2017) proposed functional GARCH (fGARCH) to analyze functional volatilities based on UHF data. This article introduces fGARCH to the readers and illustrates how to estimate fGARCH equations using UHF data of KOSPI and Hyundai motor company.
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