Copula multivariate GARCH model with constrained Hamiltonian Monte Carlo

DOI10.1515/DEMO-2019-0006zbMath1439.62127OpenAlexW3122448347MaRDI QIDQ2178935

Louis Bélisle, Martin Burda

Publication date: 12 May 2020

Published in: Dependence Modeling (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1515/demo-2019-0006

zbMATH Keywords

Markov chain Monte Carlodynamic conditional volatilityvarying correlation model

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) 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) Bayesian inference (62F15) Characterization and structure theory for multivariate probability distributions; copulas (62H05) Monte Carlo methods (65C05) Economic time series analysis (91B84)

Related Items (1)

Drawdown risk measures for asset portfolios with high frequency data

Uses Software

• CODA

Cites Work

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• Copula-based multivariate GARCH model with uncorrelated dependent errors
• An introduction to copulas.
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• A comparison of generalized hybrid Monte Carlo methods with and without momentum flip
• Weak convergence and optimal scaling of random walk Metropolis algorithms
• Optimal tuning of the hybrid Monte Carlo algorithm
• Time-varying joint distribution through copulas
• Simulating Hamiltonian Dynamics
• Simulation Run Length Control in the Presence of an Initial Transient
• Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation

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