Generalized extreme value distribution with time-dependence using the AR and MA models in state space form

DOI10.1016/j.csda.2011.04.017zbMath1255.62150OpenAlexW2123399453MaRDI QIDQ1927108

Yasuhiro Omori, Sylvia Frühwirth-Schnatter, Tsuyoshi Kunihama, Jouchi Nakajima

Publication date: 30 December 2012

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2011.04.017

zbMATH Keywords

extreme valuesMarkov chain Monte Carlostock returnsgeneralized extreme value distributionstate space modelmixture sampler

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to actuarial sciences and financial mathematics (62P05) Bayesian inference (62F15) Statistics of extreme values; tail inference (62G32) Numerical analysis or methods applied to Markov chains (65C40)

Related Items (11)

Modeling tails of aggregate economic processes in a stochastic growth model ⋮ AR(1) processes driven by second-chaos white noise: Berry-Esséen bounds for quadratic variation and parameter estimation ⋮ Stochastic tail index model for high frequency financial data with Bayesian analysis ⋮ A Bayesian hierarchical model for spatial extremes with multiple durations ⋮ Efficient estimation and particle filter for max-stable processes ⋮ Extreme value distributions: an overview of estimation and simulation ⋮ Bayesian modeling of dynamic extreme values: extension of generalized extreme value distributions with latent stochastic processes ⋮ Modified maximum spacings method for generalized extreme value distribution and applications in real data analysis ⋮ Bayesian state-space modeling in gene expression data analysis: an application with biomarker prediction ⋮ Dynamic generalized extreme value modeling via particle filters ⋮ Extreme value autoregressive model and its applications

Uses Software

• BayesDA
• ismev

Cites Work

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