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In empirical finance, conditional distributions of financial returns are
often established by specifying the standardized error distributions of
GARCH-type models. In this article, we apply the maximum entropy
(MaxEnt) approach and propose a moment combination and selection
method to explore this distribution-building problem.We demonstrate
that this framework is useful for unifying and comparing existing
distribution specifications, generating more suitable distribution specifications, and shedding light on the roles of different moments in the distribution-building process. We also show the applicability of our
method to real data by means of an empirical study on stock index
returns.
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