The ZD-GARCH model: a new way to study heteroscedasticity

DOI10.1016/j.jeconom.2017.09.003zbMath1378.62076OpenAlexW2214006042WikidataQ59567384 ScholiaQ59567384MaRDI QIDQ1680184

Ke Zhu, Dong Li, Shiqing Ling, XingFa Zhang

Publication date: 23 November 2017

Published in: Journal of Econometrics (Search for Journal in Brave)

Full work available at URL: https://mpra.ub.uni-muenchen.de/68621/1/MPRA_paper_68621.pdf

zbMATH Keywords

asymptotic normalityheteroscedasticitystability testGARCH modelconditional heteroscedasticitytop Lyapunov exponentgeneralized quasi-maximum likelihood estimatorPortmanteau testzero-drift GARCH model

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Asymptotic properties of nonparametric inference (62G20) Applications of statistics to actuarial sciences and financial mathematics (62P05) Nonparametric estimation (62G05)

Related Items (8)

Asymptotics for semi-strong augmented GARCH(1,1) model ⋮ On the test of the volatility proxy model ⋮ Time series models for realized covariance matrices based on the matrix-F distribution ⋮ A new generalized exponentially weighted moving average quantile model and its statistical inference ⋮ A simulation study on the Markov regime-switching zero-drift GARCH model ⋮ Inference for asymmetric exponentially weighted moving average models ⋮ Testing the existence of moments for GARCH processes ⋮ Hybrid quantile estimation for asymmetric power GARCH models

Uses Software

• RiskMetrics

Cites Work

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