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Identifier to cite or link to this item: https://hdl.handle.net/10086/17083

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Title
Modeling and Forecasting the Volatility of the Nikkei 225 Realized Volatility Using the ARFIMA-GARCH Model
Creator
Creator Name
Ishida, Isao Ishida, Isao
石田, 功 石田, 功
Affiliation
Affiliation Name
University of Tokyo
Creator Name
Watanabe, Toshiaki Watanabe, Toshiaki
渡部, 敏明 渡部, 敏明
Affiliation
Affiliation Name
Hitotsubashi University
Editor
Contributor Name
社会科学の高度統計・実証分析拠点構築 = Research Unit for Statistical and Empirical Analysis in Social Sciences
Key Word
ARFIMA-GARCH
Volatility of realized volatility
Realized bipower variation
Jump detection
BDS test
Hong-Li test
High-frequency Nikkei 225 data
JEL
C22
C53
G15
Abstract
In this paper, we apply the ARFIMA-GARCH model to the realized volatility and the continuous sample path variations constructed from high-frequency Nikkei 225 data. While the homoskedastic ARFIMA model performs excellently in predicting the Nikkei 225 realized volatility time series and their square-root and log transformations, the residuals of the model suggest presence of strong conditional heteroskedasticity similar to the finding of Corsi et al. (2007) for the realized S&P 500 futures volatility. An ARFIMA model augmented by a GARCH(1,1) specification for the error term largely captures this and substantially improves the fit to the data. In a multi-day forecasting setting, we also find some evidence of predictable time variation in the volatility of the Nikkei 225 volatility captured by the ARFIMA-GARCH model.
Description
This version: January 14, 2009; First draft: October 24, 2008
Publisher
Institute of Economic Research, Hitotsubashi University
Issued Date
2009-02
Language
English(eng)
Resource Type
technical report
Version Type
VoR
Is Part OfRelation -
Related Title
Global COE Hi-Stat Discussion Paper Series ; No. 32
Number of Pages
24
Sponsorship
グローバルCOEプログラム = Global COE Program
Edition
[Revised version]
Appears in Collections
Global COE Hi-Stat Discussion Paper Series
URL
https://hdl.handle.net/10086/17083