Heavy tailed time series with extremal independence
AbstractWe consider strictly stationary heavy tailed time series whose finite-dimensional exponent measures are concentrated on axes, and hence their extremal properties cannot be tackled using classical multivariate regular variation that is suitable for time series with extremal dependence. We recover relevant information about limiting behavior of time series with extremal independence by introducing a sequence of scaling functions and conditional scaling exponent. Both quantities provide more information about joint extremes than a widely used tail dependence coefficient. We calculate the scaling functions and the scaling exponent for variety of models, including Markov chains, exponential autoregressive model, stochastic volatility with heavy tailed innovations or volatility. Theory is illustrated by numerical studies and data analysis.
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Bibliographic InfoPaper provided by arXiv.org in its series Papers with number 1307.1501.
Date of creation: Jul 2013
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Web page: http://arxiv.org/
This paper has been announced in the following NEP Reports:
- NEP-ALL-2013-07-20 (All new papers)
- NEP-ECM-2013-07-20 (Econometrics)
- NEP-ETS-2013-07-20 (Econometric Time Series)
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