Goodness-of-fit tests for the multivariate Student-t distribution based on i.i.d. data, and for GARCH observations

dc.contributor.author	SIMOS MEINTANIS et al
dc.date.accessioned	2026-01-20T07:22:37Z
dc.date.issued	2024
dc.description	aDepartment of Economics, National and Kapodistrian University of Athens, Athens, Greece bPure and Applied Analytics, North-West University, Potchefstroom, South Africa cFaculty of Mathematics, University of Belgrade, Belgrade, Serbia
dc.description.abstract	We consider goodness-of-fit tests for the multivariate Student's t-distribution with i.i.d. data and for the innovation distribution in a generalized autoregressive conditional heteroskedasticity model. The methods are based on the empirical characteristic function and are relatively easy to implement, invariant under linear transformations, and globally consistent. Asymptotic properties of the proposed procedures are investigated, while the finite-sample properties are illustrated by means of a Monte Carlo study. The procedures are also applied to real data from the financial markets.
dc.identifier.uri	http://hdl.handle.net/10394/45428
dc.language.iso	en
dc.publisher	Springer New York
dc.subject	Goodness-of-fit test
dc.subject	heavy-tailed distribution
dc.subject	empirical characteristic function
dc.subject	CCC-GARCH
dc.title	Goodness-of-fit tests for the multivariate Student-t distribution based on i.i.d. data, and for GARCH observations
dc.type	Article

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