Graduate School of Economics, University of Tokyo
Faculty of Economics, University of Tokyo
Department of Statistics, University of Wisconsin Madison
抄録
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
本文フィルはリンク先を参照のこと
雑誌名
Discussion paper series. CIRJE-F
巻
CIRJE-F-757
発行年
2010-08
書誌レコードID
AA11450569
フォーマット
application/pdf
日本十進分類法
335
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
Bayesian Estimation and Particle Filter for Max-Stable Processes
