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- W2077801602 abstract "Time series presenting non-Gaussian features such as heteroscedasticity or sudden bursts of activity play a central role in many fields including finance, insurance, and seismology. The heteroscedastic mixture transition distribution (HMTD) model, which generalizes several specifications previously proposed in the statistical literature, is a new model especially designed to handle series of this kind. By allowing the standard deviation of each component to be a function of the past of the observed process, a better modeling of the conditional probability distribution function of future observations is obtained. A numerical example shows that the HMTD can perform better than standard models such as ARMA and GARCH. Different issues related to the numerical estimation of mixture models are also discussed." @default.
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- W2077801602 date "2003-01-01" @default.
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- W2077801602 title "Mixture transition distribution (MTD) modeling of heteroscedastic time series" @default.
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- W2077801602 doi "https://doi.org/10.1016/s0167-9473(02)00191-3" @default.
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