Stochastic volatility: Bayesian computation using automatic differentiation and the extended Kalman filter

DOI10.1111/1368-423X.t01-1-00116zbMath1065.91533MaRDI QIDQ4458366

Andreas Berg, David A. Fournier, Renate Meyer

Publication date: 17 March 2004

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

zbMATH Keywords

heavy tailed distributionsMarkov chain Monte Carlo (MCMC) techniquesnon-Gaussian nonlinear state-space models

Mathematics Subject Classification ID

Applications of statistics to actuarial sciences and financial mathematics (62P05) Bayesian inference (62F15) Economic time series analysis (91B84) Statistical methods; economic indices and measures (91B82)

Related Items

AD Model Builder: using automatic differentiation for statistical inference of highly parameterized complex nonlinear models, A flexible and automated likelihood based framework for inference in stochastic volatility models, The hierarchical-likelihood approach to autoregressive stochastic volatility models, A Bayesian analysis of the Bingham distribution, Multivariate stochastic volatility with Bayesian dynamic linear models, Unnamed Item, Multivariate Stochastic Volatility Models: Bayesian Estimation and Model Comparison, Variational Bayesian identification and prediction of stochastic nonlinear dynamic causal models, Simulation-Based Estimation Methods for Financial Time Series Models, Comparison of MCMC methods for estimating stochastic volatility models

Uses Software

• BUGS
• Fahrmeir

Cites Work

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