Extremes of multivariate ARMAX processes
We define a new multivariate time series model by generalizing the ARMAX process in a multivariate way. We give conditions on stationarity and analyze local dependence and domains of attraction. As a consequence of the obtained results, we derive new multivariate extreme value distributions.We chara...
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| Formato: | article |
| Idioma: | eng |
| Publicado em: |
2013
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| Texto completo: | http://hdl.handle.net/1822/24610 |
| País: | Portugal |
| Oai: | oai:repositorium.sdum.uminho.pt:1822/24610 |
| Resumo: | We define a new multivariate time series model by generalizing the ARMAX process in a multivariate way. We give conditions on stationarity and analyze local dependence and domains of attraction. As a consequence of the obtained results, we derive new multivariate extreme value distributions.We characterize the extremal dependence by computing the multivariate extremal index and bivariate upper tail dependence coefficients. An estimation procedure for the multivariate extremal index is presented. We also address the marginal estimation and propose a new estimator for the ARMAX autoregressive parameter. |
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