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Block Sampler and Posterior Mode Estimation for Asymmetric Stochastic Volatility Models
http://hdl.handle.net/2261/8037
http://hdl.handle.net/2261/803753b0041c-0d88-40b1-bb3b-fe2851eb2eac
| Item type | テクニカルレポート / Technical Report(1) | |||||
|---|---|---|---|---|---|---|
| 公開日 | 2017-01-17 | |||||
| タイトル | ||||||
| タイトル | Block Sampler and Posterior Mode Estimation for Asymmetric Stochastic Volatility Models | |||||
| 言語 | ||||||
| 言語 | eng | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | Asymmetric stochastic volatility model | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | Bayesian analysis | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | Disturbance smoother | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | Kalman filter | |||||
| 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 | Metropolis-Hastings algorithm | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | Simulation smoother | |||||
| 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 |
Omori, Yasuhiro
× Omori, Yasuhiro× Watanabe, Toshiaki |
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| 著者所属 | ||||||
| 著者所属 | Faculty of Economics, University of Tokyo | |||||
| 著者所属 | ||||||
| 著者所属 | Institute of Economic Research, Hitotsubashi University | |||||
| 抄録 | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | This article introduces a new efficient simulation smoother and disturbance smoother for asymmetric stochastic volatility models where there exists a correlation between today’s return and tomorrow’s volatility. The state vector is divided into several blocks where each block consists of many state variables. For each block, corresponding disturbances are sampled simultaneously from their conditional posterior distribution. The algorithm is based on the multivariate normal approximation of the conditional posterior density and exploits a conventional simulation smoother for a linear and Gaussian state space model. The performance of our method is illustrated using two examples (1) simple asymmetric stochastic volatility model and (2) asymmetric stochastic volatility model with state-dependent variances. The popular single move sampler which samples a state variable at a time is also conducted for comparison in the first example. It is shown that our proposed sampler produces considerable improvement in the mixing property of the Markov chain Monte Carlo chain. | |||||
| 内容記述 | ||||||
| 内容記述タイプ | Other | |||||
| 内容記述 | forthcoming in Computational Statistics and Data Analysis. | |||||
| 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-507, 発行日 2007-08 |
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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Scheme | NDC | |||||
| 主題 | 330 | |||||
| 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/2007/2007cf507ab.html | |||||
