2020-04-01T23:57:59Zhttps://repository.dl.itc.u-tokyo.ac.jp/?action=repository_oaipmhoai:repository.dl.itc.u-tokyo.ac.jp:000429062020-02-19T05:01:01Z00062:07433:0743400009:07435:07436
Generalized Extreme Value Distribution with Time-Dependence Using the AR and MA Models in State Space FormengExtreme valuesGeneralized extreme value distributionMarkov chainMonte CarloMixture samplerState space modelStock returnshttp://hdl.handle.net/2261/43064Technical ReportNakajima, JouchiKunihama, TsuyoshiOmori, YasuhiroFrühwirth-Schnatter, SylviaA 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 returnsRevised version of CIRJE-F-689 (2009); subsequently published in Computational Statistics and Data Analysis, 56-11, 3241-3259. November 2012.本文フィルはリンク先を参照のことDiscussion paper series. CIRJE-FCIRJE-F-7822011-01AA11450569application/pdf335日本経済国際共同センターhttp://www.cirje.e.u-tokyo.ac.jp/research/dp/2011/2011cf782ab.html2017-06-16