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Both unconditional mixed-normal distributions and GARCH models with fat-tailed conditional distributions have been employed for modeling financial return data. We consider a mixed-normal distribution coupled with a GARCH-type structure which allows for conditional variance in each of the components as well as dynamic feedback between the components. Special cases and relationships with previously proposed specifications are discussed and stationarity conditions are derived. An empirical application to NASDAQ-index data indicates the appropriateness of the model class and illustrates that the approach can generate a plausible disaggregation of the conditional variance process, in which the components' volatility dynamics have a clearly distinct behavior that is, for example, compatible with the well-known leverage effect. Klassifikation: C22, C51, G10
Coccolith mass is an important parameter for estimating coccolithophore contribution to carbonate sedimentation, organic carbon ballasting and coccolithophore calcification. Single coccolith mass is often estimated based on the ks model, which assumes that length and thickness increase proportionally. To evaluate this assumption, this study compared coccolith length, thickness, and mass of seven Emiliania huxleyi strains and one Gephyrocapsa oceanica strain grown in 25, 34, and 44 salinity artificial seawater. While coccolith length increased with salinity in four E. huxleyi strains, thickness did not increase significantly with salinity in three of these strains. Only G. oceanica showed a consistent increase in length with salinity that was accompanied by an increase in thickness. Coccolith length and thickness was also not correlated in 14 of 24 individual experiments, and in the experiments in which there was a positive relationship r2 was low (<0.4). Because thickness did not increase with length in E. huxleyi, the increase in mass was less than expected from the ks model, and thus, mass can not be accurately estimated from coccolith length alone.
We extend the canonical income process with persistent and transitory risk to shock distributions with left-skewness and excess kurtosis, to which we refer as higher- order risk. We estimate our extended income process by GMM for household data from the United States. We find countercyclical variance and procyclical skewness of persistent shocks. All shock distributions are highly leptokurtic. The existing tax and transfer system reduces dispersion and left-skewness of shocks. We then show that in a standard incomplete-markets life-cycle model, first, higher-order risk has sizable welfare implications, which depend crucially on risk attitudes of households; second, higher-order risk matters quantitatively for the welfare costs of cyclical idiosyncratic risk; third, higher-order risk has non-trivial implications for the degree of self-insurance against both transitory and persistent shocks.