http://swrc.ontoware.org/ontology#TechnicalReport
Generalized Extreme Value Distribution with Time-Dependence Using the AR and MA Models in State Space Form
en
Extreme values
Generalized extreme value distribution
Markov chain
Monte Carlo
Mixture sampler
State space model
Stock returns
Nakajima Jouchi
Kunihama Tsuyoshi
Omori Yasuhiro
Frühwirth-Schnatter Sylvia
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
Revised version of CIRJE-F-689 (2009); subsequently published in Computational Statistics and Data Analysis, 56-11, 3241-3259. November 2012.
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Discussion paper series. CIRJE-F
CIRJE-F-782
2011-01
AA11450569
application/pdf
335
日本経済国際共同センター
http://www.cirje.e.u-tokyo.ac.jp/research/dp/2011/2011cf782ab.html