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Volatility models with innovations from new maximum entropy densities at work
Fischer, Matthias J.; Gao, Yang; Herrmann, Klaus - Wirtschafts- und Sozialwissenschaftliche Fakultät, … - 2010
Generalized autoregressive conditional heteroskedasticity (GARCH) processes have become very popular as models for … densities and their parametric competitors within different generalized GARCH models such as APARCH and GJR-GARCH. …
Persistent link: https://www.econbiz.de/10008518271
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Skew generalized secant hyperbolic distributions: unconditional and conditional fit to asset returns
Fischer, Matthias J. - Wirtschafts- und Sozialwissenschaftliche Fakultät, … - 2002
A generalization of the hyperbolic secant distribution which allows both for skewness and for leptokurtosis was given by Morris (1982). Recently, Vaughan (2002) proposed another flexible generalization of the hyperbolic secant distribution which has a lot of nice properties but is not able to...
Persistent link: https://www.econbiz.de/10008543753
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Models for time-varying moments using maximum entropy applied to a generalized measure of volatility
Herrmann, Klaus - Wirtschafts- und Sozialwissenschaftliche Fakultät, … - 2008
We use an information-theoretic approach to interpret Engle's (1982) and Bollerslev's (1986) GARCH model as a model for …
Persistent link: https://www.econbiz.de/10008493563
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Non-Extensitivity versus informative moments for financial models: a unifying framework and empirical results
Herrmann, Klaus - Wirtschafts- und Sozialwissenschaftliche Fakultät, … - 2009
generalize the notion of GARCH processes in an information-theoretic sense and are able to capture skewness and kurtosis better …
Persistent link: https://www.econbiz.de/10008493567
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The impacts of outliers on different estimators for GARCH processes: an empirical study
Ardelean, Vlad - Wirtschafts- und Sozialwissenschaftliche Fakultät, … - 2009
The Maximum likelihood estimation (MLE) is the most widely used method to estimate the parameters of a GARCH …
Persistent link: https://www.econbiz.de/10008493578