A radial basis function artificial neural network test for ARCH

DOI10.1016/S0165-1765(00)00267-6zbMath0963.91071OpenAlexW2149998159WikidataQ126421264 ScholiaQ126421264MaRDI QIDQ1583167

Andrew P. Blake, George Kapetanios

Publication date: 26 October 2000

Published in: Economics Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0165-1765(00)00267-6

zbMATH Keywords

bootstraphypothesis testingartificial neural networksconditional heteroscedasticity

Mathematics Subject Classification ID

Neural networks for/in biological studies, artificial life and related topics (92B20) Statistical methods; economic indices and measures (91B82)

Related Items (8)

Testing for ARCH in the presence of nonlinearity of unknown form in the conditional mean ⋮ Testing for Neglected Nonlinearity Using Regularized Artificial Neural Networks ⋮ Pure Significance Tests of the Unit Root Hypothesis Against Nonlinear Alternatives ⋮ Testing for Neglected Nonlinearity in Cointegrating Relationships ⋮ A neural network method for nonlinear time series analysis ⋮ TESTS OF THE MARTINGALE DIFFERENCE HYPOTHESIS USING BOOSTING AND RBF NEURAL NETWORK APPROXIMATIONS ⋮ A radial basis function artificial neural network test for neglected nonlinearity ⋮ Robust Lagrange multiplier test for detecting ARCH/GARCH effect using permutation and bootstrap

Cites Work

• Unnamed Item
• Edgeworth correction by bootstrap in autoregressions
• ARCH modeling in finance. A review of the theory and empirical evidence
• Testing for neglected nonlinearity in time series models. A comparison of neural network methods and alternative tests
• A comparison of the power of some tests for conditional heteroscedasticity
• Bootstrap in moving average models
• Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation

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