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Generalized Extreme Value Distribution with Time-Dependence Using the AR and MA Models in State Space Form
http://hdl.handle.net/2261/43064
http://hdl.handle.net/2261/4306438f5ecbf-f9ef-4241-a563-84dbd690eca0
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 | Generalized Extreme Value Distribution with Time-Dependence Using the AR and MA Models in State Space Form | |||||
言語 | ||||||
言語 | eng | |||||
キーワード | ||||||
主題 | Extreme values | |||||
主題Scheme | Other | |||||
キーワード | ||||||
主題 | Generalized extreme value distribution | |||||
主題Scheme | Other | |||||
キーワード | ||||||
主題 | Markov chain | |||||
主題Scheme | Other | |||||
キーワード | ||||||
主題 | Monte Carlo | |||||
主題Scheme | Other | |||||
キーワード | ||||||
主題 | Mixture sampler | |||||
主題Scheme | Other | |||||
キーワード | ||||||
主題 | State space model | |||||
主題Scheme | Other | |||||
キーワード | ||||||
主題 | Stock returns | |||||
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 |
Nakajima, Jouchi
× Nakajima, Jouchi× Kunihama, Tsuyoshi× Omori, Yasuhiro× Frühwirth-Schnatter, Sylvia |
|||||
著者所属 | ||||||
著者所属 | Department of Statistical Science, Duke University | |||||
著者所属 | ||||||
著者所属 | Faculty of Economics, University of Tokyo | |||||
著者所属 | ||||||
著者所属 | Department of Applied Statistics, Johannes Kepler University Linz | |||||
抄録 | ||||||
内容記述タイプ | Abstract | |||||
内容記述 | A new state space approach is proposed to model the time-dependence in an extreme value process. The generalized extreme value distribution is extended to incorporate the time-dependence using a state space representation where the state variables either fol- low an autoregressive (AR) process or a moving average (MA) process with innovations arising from a Gumbel distribution. Using a Bayesian approach, an efficient algorithm is proposed to implement Markov chain Monte Carlo method where we exploit an accu- rate approximation of the Gumbel distribution by a ten-component mixture of normal distributions. The methodology is illustrated using extreme returns of daily stock data. The model is tted to a monthly series of minimum returns and the empirical results support strong evidence of time-dependence among the observed minimum returns | |||||
内容記述 | ||||||
内容記述タイプ | Other | |||||
内容記述 | Revised version of CIRJE-F-689 (2009); subsequently published in Computational Statistics and Data Analysis, 56-11, 3241-3259. November 2012. | |||||
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-782, 発行日 2011-01 |
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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/2011/2011cf782ab.html |