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Bayesian Estimation and Particle Filter for Max-Stable Processes
http://hdl.handle.net/2261/37281
http://hdl.handle.net/2261/3728116bc1bdb-b878-420c-bafc-217f7ab528ea
| 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 | Bayesian Estimation and Particle Filter for Max-Stable Processes | |||||
| 言語 | ||||||
| 言語 | eng | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | Bayesian analysis | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | Extreme value theory | |||||
| 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 | Marginal likelihood | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | Maxima of moving maxima processes | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | Stock returns | |||||
| 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 |
Kunihama, Tsuyoshi
× Kunihama, Tsuyoshi× Omori, Yasuhiro× Zhang, Zhengjun |
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| 著者別名 | ||||||
| 識別子Scheme | WEKO | |||||
| 識別子 | 97047 | |||||
| 姓名 | 大森, 裕浩 | |||||
| 著者所属 | ||||||
| 著者所属 | Graduate School of Economics, University of Tokyo | |||||
| 著者所属 | ||||||
| 著者所属 | Faculty of Economics, University of Tokyo | |||||
| 著者所属 | ||||||
| 著者所属 | Department of Statistics, University of Wisconsin Madison | |||||
| 抄録 | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | Extreme values are often correlated over time, for example, in a financial time series, and these values carry various risks. Max-stable processes such as maxima of moving maxima (M3) processes have been recently considered in the literature to describe timedependent dynamics, which have been difficult to estimate. This paper first proposes a feasible and efficient Bayesian estimation method for nonlinear and non-Gaussian state space models based on these processes and describes a Markov chain Monte Carlo algorithm where the sampling efficiency is improved by the normal mixture sampler. Furthermore, a unique particle filter that adapts to extreme observations is proposed and shown to be highly accurate in comparison with other well-known filters. Our proposed algorithms were applied to daily minima of high-frequency stock return data, and a model comparison was conducted using marginal likelihoods to investigate the time-dependent dynamics in extreme stock returns for financial risk management. | |||||
| 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-757, 発行日 2010-08 |
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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 | 335 | |||||
| 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/2010/2010cf757ab.html | |||||
