WEKO3
アイテム
Realized Stochastic Volatility with Leverage and Long Memory
http://hdl.handle.net/2261/52573
http://hdl.handle.net/2261/52573856c18d5-d33e-4465-b655-4765376b2867
| 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 | Realized Stochastic Volatility with Leverage and Long Memory | |||||
| 言語 | ||||||
| 言語 | eng | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | ARFIMA | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | leverage effect | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | long memory | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | Markov Chain Monte Carlo | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | Mixture sampler | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | Realized volatility | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | Realized stochastic volatility model | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | State space model | |||||
| 資源タイプ | ||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_18gh | |||||
| 資源タイプ | 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 |
Shirota, Shinichiro
× Shirota, Shinichiro× Hizu, Takayuki× Omori, Yasuhiro |
|||||
| 著者所属 | ||||||
| 値 | Graduate school of Economics, The University of Tokyo | |||||
| 著者所属 | ||||||
| 値 | Mitsubishi UFJ Trust and Banking | |||||
| 著者所属 | ||||||
| 値 | Faculty of Economics, The University of Tokyo | |||||
| 抄録 | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | The daily return and the realized volatility are simultaneously modeled in the stochastic volatility model with leverage and long memory. The dependent variable in the stochastic volatility model is the logarithm of the squared return, and its error distribution is approximated by a mixture of normals. In addition, we incorporate the logarithm of the realized volatility into the measurement equation, assuming that the latent log volatility follows an Autoregressive Fractionally Integrated Moving Average (ARFIMA) process to describe its long memory property. Using a state space representation, we propose an efficient Bayesian estimation method implemented using Markov chain Monte Carlo method (MCMC). Model comparisons are performed based on the marginal likelihood, and the volatility forecasting performances are investigated using S&P500 stock index returns. | |||||
| 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-869, 発行日 2012-11 |
|||||
| 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Scheme | NDC | |||||
| 主題 | 335 | |||||
| 出版者 | ||||||
| 出版者 | 日本経済国際共同センター | |||||
| 出版者別名 | ||||||
| 値 | 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/2012/2012cf869ab.html | |||||
