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Modeling and Forecasting the Volatility of the Nikkei 225 Realized Volatility Using the ARFIMA-GARCH Model
http://hdl.handle.net/2261/25771
http://hdl.handle.net/2261/25771acaa7b13-86c8-4397-96aa-5f423a480909
Item type | テクニカルレポート / Technical Report(1) | |||||
---|---|---|---|---|---|---|
公開日 | 2013-05-31 | |||||
タイトル | ||||||
タイトル | Modeling and Forecasting the Volatility of the Nikkei 225 Realized Volatility Using the ARFIMA-GARCH Model | |||||
言語 | ||||||
言語 | eng | |||||
キーワード | ||||||
主題 | classification: C22, C53, G15. | |||||
主題Scheme | Other | |||||
キーワード | ||||||
主題 | ARFIMA-GARCH | |||||
主題Scheme | Other | |||||
キーワード | ||||||
主題 | Volatility of realized volatility | |||||
主題Scheme | Other | |||||
キーワード | ||||||
主題 | Realized bipower variation | |||||
主題Scheme | Other | |||||
キーワード | ||||||
主題 | Jump detection | |||||
主題Scheme | Other | |||||
キーワード | ||||||
主題 | BDS test | |||||
主題Scheme | Other | |||||
キーワード | ||||||
主題 | Hong-Li test | |||||
主題Scheme | Other | |||||
キーワード | ||||||
主題 | High-frequency Nikkei 225 data | |||||
主題Scheme | Other | |||||
資源タイプ | ||||||
資源 | http://purl.org/coar/resource_type/c_18gh | |||||
タイプ | technical report | |||||
アクセス権 | ||||||
アクセス権 | metadata only access | |||||
アクセス権URI | http://purl.org/coar/access_right/c_14cb | |||||
著者 |
Ishida, Isao
× Ishida, Isao× Toshiaki, Watanabe |
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著者所属 | ||||||
著者所属 | University of Tokyo | |||||
著者所属 | ||||||
著者所属 | Hitotsubashi University | |||||
抄録 | ||||||
内容記述タイプ | Abstract | |||||
内容記述 | 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. | |||||
内容記述 | ||||||
内容記述タイプ | Other | |||||
内容記述 | 本文フィルはリンク先を参照のこと | |||||
書誌情報 |
Discussion paper series. CIRJE-F 巻 CIRJE-F-608, 発行日 2009-01 |
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書誌レコードID | ||||||
収録物識別子タイプ | NCID | |||||
収録物識別子 | AA11450569 | |||||
フォーマット | ||||||
内容記述タイプ | Other | |||||
内容記述 | application/pdf | |||||
日本十進分類法 | ||||||
主題 | 335 | |||||
主題Scheme | NDC | |||||
出版者 | ||||||
出版者 | 日本経済国際共同センター | |||||
出版者別名 | ||||||
Center for International Research on the Japanese Economy | ||||||
関係URI | ||||||
識別子タイプ | URI | |||||
関連識別子 | http://www.cirje.e.u-tokyo.ac.jp/research/dp/2009/2009cf608ab.html |