Estimation and Asymptotic Properties in PeriodicGARCH(1, 1) Models

DOI10.1080/03610926.2011.633201zbMath1462.62166OpenAlexW2128842361MaRDI QIDQ2864659

Ines Lescheb, Abdelouahab Bibi

Publication date: 26 November 2013

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610926.2011.633201

zbMATH Keywords

consistencyasymptotic normalityYule-Walker estimatorperiodically correlated processesperiodic GARCH models

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Economic time series analysis (91B84)

Related Items (2)

M-estimation for periodic GARCH model with high-frequency data ⋮ Probabilistic properties of a Markov-switching periodic GARCH process

Cites Work

• A conditional least squares approach to PGARCH and PARMA-PGARCH time series estimation
• On periodic GARCH processes: stationarity, existence of moments and geometric ergodicity
• Strong consistency and asymptotic normality of least squares estimators for PGARCH and PARMA-PGARCH models
• Stationarity of GARCH processes and of some nonnegative time series
• Limit theory for the sample autocorrelations and extremes of a GARCH \((1,1)\) process.
• Arnold Zellner, 1927–2010
• Structure and estimation of a class of nonstationary yet nonexplosive GARCH models
• ERGODICITY, MIXING, AND EXISTENCE OF MOMENTS OF A CLASS OF MARKOV MODELS WITH APPLICATIONS TO GARCH AND ACD MODELS
• The stochastic equation Y_n+1=A_nY_n + B_n with stationary coefficients
• Geometric Ergodicity and Moment Conditions for a Seasonal GARCH Model with Periodic Coefficients
• Periodically Correlated Random Sequences
• A CLOSED-FORM ESTIMATOR FOR THE GARCH(1,1) MODEL

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