Efficient estimation and particle filter for max-stable processes

Tsuyoshi Kunihama, Yasuhiro Omori () and Zhengjun Zhang
Additional contact information
Zhengjun Zhang: Department of Statistics, University of Wisconsin Madison

No CIRJE-F-791, CIRJE F-Series from CIRJE, Faculty of Economics, University of Tokyo

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.

Pages: 34 pages
Date: 2011-03
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Citations: View citations in EconPapers (1)

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Related works:
Journal Article: Efficient estimation and particle filter for max‐stable processes (2012)
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