Quasi-maximum likelihood estimator of Laplace \((1,1)\) for GARCH models

DOI10.1515/math-2017-0131zbMath1485.62132OpenAlexW2789620064MaRDI QIDQ1698475

Muhammad Amin, Yongxia Shi, Haiyan Xuan, Lixin Song

Publication date: 15 February 2018

Published in: Open Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1515/math-2017-0131

zbMATH Keywords

asymptotic normalityquasi-maximum likelihood estimatorstrong consistencyGARCH modelLaplace \((1,1)\)

Mathematics Subject Classification ID

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

Related Items (3)

Nonstationary generalised autoregressive conditional heteroskedasticity modelling for fitting higher order moments of financial series within moving time windows ⋮ Ai algorithms for fitting GARCH parameters to empirical financial data ⋮ Standard Laplace quasi-maximum likelihood estimator for GARCH processes

Cites Work

• Self-weighted and local quasi-maximum likelihood estimators for ARMA-GARCH/IGARCH models
• Global self-weighted and local quasi-maximum exponential likelihood estimators for ARMA-GARCH/IGARCH models
• Quasi-maximum-likelihood estimation in conditionally heteroscedastic time series: a stochastic recurrence equations approach
• A double-threshold GARCH model of stock market and currency shocks on stock returns
• Stationarity of GARCH processes and of some nonnegative time series
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• The rate of consistency of the quasi-maximum likelihood estimator.
• Maximum likelihood estimation of pure GARCH and ARMA-GARCH processes
• Generalized autoregressive conditional heteroscedasticity
• The efficiency of the estimators of the parameters in GARCH processes.
• Conditional Heteroskedasticity in Asset Returns: A New Approach
• Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation
• Consistency and Asymptotic Normality of the Quasi-Maximum Likelihood Estimator in IGARCH(1,1) and Covariance Stationary GARCH(1,1) Models

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