CIRJE-F-791 "Efficient Estimation and Particle Filter for Max-Stable Processes"
Author Name Kunihama, Tsuyoshi, Yasuhiro Omori and Zhengjun Zhang
Date March 2011
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Remarks Revised version of CIRJE-F-757 (2010); subsequenlty published in Journal of Time Series Analysis, 33-1, 61-80. January 2012.
Abstract

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.