Bayesian analysis of stochastic volatility models with mixture-of-normal distributions

DOI10.1016/j.matcom.2008.12.013zbMath1162.91362OpenAlexW2075382529MaRDI QIDQ1025340

Manabu Asai

Publication date: 18 June 2009

Published in: Mathematics and Computers in Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.matcom.2008.12.013

zbMATH Keywords

Bayes factormarginal likelihoodStudent-\(t\) distributionMarkov-chain Monte Carlogeneralized error distributionmixture-of-normal distributionsmulti-move sampler

Mathematics Subject Classification ID

Applications of statistics to actuarial sciences and financial mathematics (62P05) Statistical methods; risk measures (91G70) Economic time series analysis (91B84) Stochastic models in economics (91B70)

Related Items (8)

Realized stochastic volatility with general asymmetry and long memory ⋮ カルマン・フィルターによるRealized Stochastic Volatilityモデルの疑似最尤推定について ⋮ Bayesian inference for a mixture double autoregressive model ⋮ Bayesian estimation for stochastic volatility model with jumps, leverage effect and generalized hyperbolic skew Student's t-distribution ⋮ A threshold stochastic volatility model with explanatory variables ⋮ Bayesian analysis of heavy-tailed market microstructure model and its application in stock markets ⋮ An empirical evaluation of fat-tailed distributions in modeling financial time series ⋮ On asymmetric generalised t stochastic volatility models

Cites Work

• Unnamed Item
• Comparison of MCMC methods for estimating stochastic volatility models
• Bayesian analysis of stochastic volatility models with fat-tails and correlated errors
• Empirical modeling of exchange rate dynamics
• Estimation of stochastic volatility models via Monte Carlo maximum likelihood
• Bayes inference in regression models with ARMA\((p,q)\) errors
• Stochastic volatility in asset prices. Estimation with simulated maximum likelihood
• Kurtosis of GARCH and stochastic volatility models with non-normal innovations
• Markov chain Monte Carlo methods for stochastic volatility models.
• Markov chains for exploring posterior distributions. (With discussion)
• Bayesian analysis of ARMA-GARCH models: a Markov chain sampling approach
• Marginal Likelihood from the Gibbs Output
• Conditional Heteroskedasticity in Asset Returns: A New Approach
• Comparison of MCMC Methods for Estimating GARCH Models
• Non-Gaussian State-Space Modeling of Nonstationary Time Series
• Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models
• Likelihood analysis of non-Gaussian measurement time series
• Marginal Likelihood From the Metropolis–Hastings Output
• The simulation smoother for time series models
• Accept–reject Metropolis–Hastings sampling and marginal likelihood estimation
• Portfolio Analysis in a Stable Paretian Market
• A multi-move sampler for estimating non-Gaussian time series models: Comments on Shephard & Pitt (1997)
• Multivariate Stochastic Volatility: A Review
• AUTOMATED INFERENCE AND LEARNING IN MODELING FINANCIAL VOLATILITY
• Getting It Right
• Bayesian inference on GARCH models using the Gibbs sampler

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