Bayesian estimation of smoothly mixing time-varying parameter GARCH models

DOI10.1016/j.csda.2013.09.019zbMath1506.62038OpenAlexW2007949724MaRDI QIDQ1623521

Cathy W. S. Chen, Edward M. H. Lin, Richard H. Gerlach

Publication date: 23 November 2018

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2013.09.019

zbMATH Keywords

Markov chain Monte Carlo methodforecastingvalue-at-risksmooth transitionstructure breakstime-varying GARCH model

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to actuarial sciences and financial mathematics (62P05) Statistical methods; risk measures (91G70) Bayesian inference (62F15) Monte Carlo methods (65C05)

Related Items (1)

Generalized Poisson autoregressive models for time series of counts

Uses Software

• CAViaR

Cites Work

• Modelling volatility by variance decomposition
• On the shape of posterior densities and credible sets in instrumental variable regression models with reduced rank: an application of flexible sampling methods using neural networks
• A Bayesian approach to relaxing parameter restrictions in multivariate GARCH models
• Fractionally integrated generalized autoregressive conditional heteroskedasticity
• ARCH modeling in finance. A review of the theory and empirical evidence
• Generalized autoregressive conditional heteroscedasticity
• Bayesian value-at-risk and expected shortfall forecasting via the asymmetric Laplace distribution
• Modelling long memory and structural breaks in conditional variances: an adaptive FIGARCH approach
• Statistical inference for time-varying ARCH processes
• Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation
• Equation of State Calculations by Fast Computing Machines
• Falling and explosive, dormant, and rising markets via multiple‐regime financial time series models
• Monte Carlo sampling methods using Markov chains and their applications

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