Modelling volatility by variance decomposition

DOI10.1016/j.jeconom.2013.03.006zbMath1283.62180OpenAlexW2124823571MaRDI QIDQ71677

Timo Teräsvirta, Cristina Amado, Cristina Amado, Timo Teräsvirta

Publication date: August 2013

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

Full work available at URL: http://hdl.handle.net/1822/11660

zbMATH Keywords

iterative algorithmmaximum likelihood estimationconditional heteroskedasticitynonlinear time seriestime-varying parameter model

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Statistical methods; risk measures (91G70) Point estimation (62F10)

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Cites Work

• Quasi-maximum-likelihood estimation in conditionally heteroscedastic time series: a stochastic recurrence equations approach
• Fractionally integrated generalized autoregressive conditional heteroskedasticity
• Testing the constancy of regression parameters against continuous structural change
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• Modelling long memory and structural breaks in conditional variances: an adaptive FIGARCH approach
• Statistical inference for time-varying ARCH processes
• PARAMETER ESTIMATION IN NONLINEAR AR–GARCH MODELS
• Semiparametric Estimator of Time Series Conditional Variance
• An Introduction to Univariate GARCH Models
• Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation
• Quadratic ARCH Models
• SEQUENTIAL CHANGE-POINT DETECTION IN GARCH(p,q) MODELS
• Handbook of Volatility Models and Their Applications
• Maximization by Parts in Likelihood Inference

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