Comparison of specification tests for GARCH models

DOI10.1016/j.csda.2013.03.009zbMath1506.62067OpenAlexW2063415040MaRDI QIDQ1623530

Bruno Rémillard, Kilani Ghoudi

Publication date: 23 November 2018

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2013.03.009

zbMATH Keywords

bootstrapempirical processesmultipliersgoodness of fit testsresidualsGARCH modelspseudo-observationssquared residuals

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Nonparametric hypothesis testing (62G10) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10)

Related Items (12)

A random walk through Canadian contributions on empirical processes and their applications in probability and statistics ⋮ Empirical characteristic function tests for GARCH innovation distribution using multipliers ⋮ Goodness‐of‐fit tests for the multivariate Student‐t distribution based on i.i.d. data, and for GARCH observations ⋮ Serial independence tests for innovations of conditional mean and variance models ⋮ Goodness-of-fit tests for parametric specifications of conditionally heteroscedastic models ⋮ CHARACTERIZATIONS OF MULTINORMALITY AND CORRESPONDING TESTS OF FIT, INCLUDING FOR GARCH MODELS ⋮ Fast tests for the two-sample problem based on the empirical characteristic function ⋮ A residual-based test for multivariate GARCH models using transformed quadratic residuals ⋮ A two-sample test for the error distribution in nonparametric regression based on the characteristic function ⋮ A new class of tests for multinormality with i.i.d. And garch data based on the empirical moment generating function ⋮ Copula-based dynamic models for multivariate time series ⋮ Semi-parametric copula-based models under non-stationarity

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