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http://dx.doi.org/10.5351/KJAS.2018.31.5.667

The fGARCH(1, 1) as a functional volatility measure of ultra high frequency time series  

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)
Publication Information
The Korean Journal of Applied Statistics / v.31, no.5, 2018 , pp. 667-675 More about this Journal
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 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.
Keywords
fGARCH; ultra high frequency; functional volatility;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
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