Simulation-based sequential analysis of Markov switching stochastic volatility models
From MaRDI portal
Publication:1020116
DOI10.1016/j.csda.2006.07.019zbMath1162.62426OpenAlexW2170621196MaRDI QIDQ1020116
Carlos Marinho Carvalho, Hedibert Freitas Lopes
Publication date: 29 May 2009
Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.csda.2006.07.019
Markov chain Monte CarloBayes factorparticle filterssequential analysisstochastic volatility modelsBayesian time series
Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to actuarial sciences and financial mathematics (62P05) Bayesian inference (62F15) Economic time series analysis (91B84) Markov processes (60J99)
Related Items
Sequential estimation of mixtures of structured autoregressive models, Approximate posterior distributions for convolutional two-level hidden Markov models, Multivariate Wishart stochastic volatility and changes in regime, Time-varying extreme pattern with dynamic models, Shifts in volatility driven by large stock market shocks, Periodic autoregressive stochastic volatility, Long memory and nonlinearities in realized volatility: a Markov switching approach, Long memory and regime switching in the stochastic volatility modelling, Dynamic changepoint detection in count time series: a particle filter approach, Weak Stationarity of Ornstein-Uhlenbeck Processes with Stochastic Speed of Mean Reversion, Some variants of adaptive sampling procedures and their applications, Particle learning and smoothing, An efficient sequential learning algorithm in regime-switching environments, Comparison of the performance of particle filter algorithms applied to tracking of a disease epidemic, Real time detection of structural breaks in GARCH models, Bayesian modeling of financial returns: A relationship between volatility and trading volume, Particle filters and Bayesian inference in financial econometrics, Factor stochastic volatility with time varying loadings and Markov switching regimes, Optimisation of interacting particle systems for rare event estimation, Volatility spillovers, interdependence and comovements: a Markov switching approach, Deriving the autocovariances of powers of Markov-switching GARCH models, with applications to statistical inference, Approximation of multiple integrals over hyperboloids with application to a quadratic portfolio with options, Joint parameter and state estimation based on marginal particle filter and particle swarm optimization, Variable Selection in Switching Dynamic Regression Models
Cites Work
- Unnamed Item
- Bayesian forecasting and dynamic models.
- Autoregressive conditional heteroskedasticity and changes in regime
- Sampling-Based Approaches to Calculating Marginal Densities
- Dynamic Generalized Linear Models and Bayesian Forecasting
- Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation
- Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models
- DATA AUGMENTATION AND DYNAMIC LINEAR MODELS
- Partial non-Gaussian state space
- On Gibbs sampling for state space models
- Filtering via Simulation: Auxiliary Particle Filters