Volatility for High Frequency Time Series Toward fGARCH (1,1) as a Functional Model
- Authors
- 황선영; 윤재은
- Issue Date
- Nov-2018
- Publisher
- 자연과학연구소
- Keywords
- Functional GARCH (fGARCH); High frequency; Multivariate GARCH (MGARCH)
- Citation
- Quantitative Bio-Science, v.37, no.2, pp 73 - 79
- Pages
- 7
- Journal Title
- Quantitative Bio-Science
- Volume
- 37
- Number
- 2
- Start Page
- 73
- End Page
- 79
- URI
- https://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/4172
- DOI
- 10.22283/qbs.2018.37.2.73
- ISSN
- 2288-1344
2508-7185
- Abstract
- 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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