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Modeling and Forecasting the Volatility of the Nikkei 225 Realized Volatility Using the ARFIMA-GARCH Model

Isao Ishida and Toshiaki Watanabe

Global COE Hi-Stat Discussion Paper Series from Institute of Economic Research, Hitotsubashi University

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.

Keywords: ARFIMA-GARCH; Volatility of realized volatility; Realized bipower variation; Jump detection; BDS test; Hong-Li test; High-frequency Nikkei 225 data (search for similar items in EconPapers)
JEL-codes: C22 C53 G15 (search for similar items in EconPapers)
Date: 2009-02
New Economics Papers: this item is included in nep-ets, nep-fmk, nep-for, nep-mst and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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http://gcoe.ier.hit-u.ac.jp/research/discussion/2008/pdf/gd08-032.pdf (application/pdf)

Related works:
Working Paper: Modeling and Forecasting the Volatility of the Nikkei 225 Realized Volatility Using the ARFIMA-GARCH Model (2009) Downloads
Working Paper: Modeling and Forecasting the Volatility of the Nikkei 225 Realized Volatility Using the ARFIMA-GARCH Model (2009) Downloads
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