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- W1683380628 abstract "We prove the strong consistency and the asymptotic normality of the maximum likelihood estimator of the parameters of a general conditionally heteroscedastic model with $alpha$-stable innovations. Then, we relax the assumptions and only suppose that the innovation process converges in distribution toward a stable process. Using a pseudo maximum likelihood estimator with a stable density, we also obtain the strong consistency and the asymptotic normality of the estimator. This framework seems relevant for financial data exhibiting heavy tails. We apply this method to several financial index and compute stable Value-at-Risk." @default.
- W1683380628 created "2016-06-24" @default.
- W1683380628 creator A5042286042 @default.
- W1683380628 date "2012-12-28" @default.
- W1683380628 modified "2023-09-27" @default.
- W1683380628 title "Maximum Likelihood Estimation for Conditionally Heteroscedastic Models when the Innovation Process is in the Domain of Attraction of a Stable Law" @default.
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