Sparse Bayesian time-varying covariance estimation in many dimensions

DOI10.1016/j.jeconom.2018.11.007zbMath1452.62773arXiv1608.08468OpenAlexW2515333341MaRDI QIDQ117775

Gregor Kastner, Gregor Kastner

Publication date: May 2019

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

Full work available at URL: https://arxiv.org/abs/1608.08468

zbMATH Keywords

curse of dimensionalityshrinkageminimum variance portfoliodynamic correlationfactor stochastic volatility

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to actuarial sciences and financial mathematics (62P05) Bayesian inference (62F15) Portfolio theory (91G10)

Related Items

Skew selection for factor stochastic volatility models ⋮ Efficient Bayesian Inference for Nonlinear State Space Models With Univariate Autoregressive State Equation ⋮ Matrix Autoregressive Spatio-Temporal Models ⋮ Parsimony inducing priors for large scale state-space models ⋮ Covariance structure estimation with Laplace approximation ⋮ Can volatility solve the naive portfolio puzzle? ⋮ Large-scale minimum variance portfolio allocation using double regularization ⋮ A Bayesian panel vector autoregression to analyze the impact of climate shocks on high-income economies ⋮ Comparing stochastic volatility specifications for large Bayesian VARs ⋮ Shrinkage with shrunken shoulders: Gibbs sampling shrinkage model posteriors with guaranteed convergence rates ⋮ High-dimensional conditionally Gaussian state space models with missing data ⋮ Bayesian prediction of jumps in large panels of time series data ⋮ An effcient exact Bayesian method for state space models with stochastic volatility ⋮ Editorial introduction on complexity and big data in economics and finance: recent developments from a Bayesian perspective ⋮ Bayesian shrinkage in mixture-of-experts models: identifying robust determinants of class membership ⋮ factorstochvol ⋮ Large-scale portfolio allocation under transaction costs and model uncertainty

Uses Software

• R
• stochvol
• GIGrvg
• factorstochvol
• rSGDLM

Cites Work

• Unnamed Item
• Unnamed Item
• Ancillarity-sufficiency interweaving strategy (ASIS) for boosting MCMC estimation of stochastic volatility models
• Achieving shrinkage in a time-varying parameter model framework
• On leverage in a stochastic volatility model
• Analysis of high dimensional multivariate stochastic volatility models
• GPU-accelerated Bayesian learning and forecasting in simultaneous graphical dynamic linear models
• Factor stochastic volatility with time varying loadings and Markov switching regimes
• Hierarchical shrinkage priors for regression models
• Dynamics \& sparsity in latent threshold factor models: a study in multivariate EEG signal processing
• Forecast density combinations of dynamic models and data driven portfolio strategies
• Large time-varying parameter VARs
• Posterior contraction in sparse Bayesian factor models for massive covariance matrices
• Regression density estimation using smooth adaptive Gaussian mixtures
• Stochastic model specification search for Gaussian and partial non-Gaussian state space models
• Dynamic financial index models: modeling conditional dependencies via graphs
• Inference with normal-gamma prior distributions in regression problems
• Sparse Bayesian infinite factor models
• Selection of Multivariate Stochastic Volatility Models via Bayesian Stochastic Search
• The Bayesian Lasso
• Multivariate Stochastic Variance Models
• Dynamic dependence networks: Financial time series forecasting and portfolio decisions
• Hierarchical Shrinkage in Time‐Varying Parameter Models
• Forecasting Performance of an Open Economy DSGE Model
• Common risk factors in the returns on stocks and bonds
• Determining the Number of Factors in Approximate Factor Models
• Factor Multivariate Stochastic Volatility via Wishart Processes
• Classical and Bayesian Analysis of Univariate and Multivariate Stochastic Volatility Models
• Monte Carlo Likelihood Estimation for Three Multivariate Stochastic Volatility Models
• Identification, estimation and testing of conditionally heteroskedastic factor models