Strong consistency and asymptotic normality of least squares estimators for PGARCH and PARMA-PGARCH models

DOI10.1016/j.spl.2010.06.007zbMath1203.62155OpenAlexW1563135472MaRDI QIDQ990922

Ines Lescheb, Abdelouahab Bibi

Publication date: 1 September 2010

Published in: Statistics \& Probability Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.spl.2010.06.007

zbMATH Keywords

asymptotic normalitystrong consistencyleast squares estimationPARMA-PGARCH modelsPGARCH models

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Markov processes: estimation; hidden Markov models (62M05)

Related Items (10)

Composite quantile regression estimation for P-GARCH processes ⋮ M-estimation for periodic GARCH model with high-frequency data ⋮ A conditional least squares approach to PGARCH and PARMA-PGARCH time series estimation ⋮ On the stationarity and existence of moments of the periodic EGARCH process ⋮ A note on integrated periodic \textit{GARCH} processes ⋮ Probabilistic properties of a Markov-switching periodic GARCH process ⋮ QMLE for periodic time-varying asymmetric log GARCH models ⋮ Estimation and Asymptotic Properties in PeriodicGARCH(1, 1) Models ⋮ Estimation for periodic ARMA models with unspecified noises ⋮ QMLE of periodic time-varying bilinear– GARCH models

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• Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation

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