Dynamic covariance estimation using sparse Bayesian factor stochastic volatility models

Publication: Chapter in book/Conference proceedingContribution to conference proceedings


We address the "curse of dimensionality" arising in time-varying covariance estimation by modeling the underlying volatility dynamics of a time series vector through a lower dimensional collection of latent dynamic factors. Furthermore, we apply a Normal-Gamma shrinkage prior to the elements of the factor loadings matrix, thereby increasing parsimony even more. Estimation is carried out via MCMC in order to obtain draws from the high-dimensional posterior and predictive distributions. To guarantee efficiency of the samplers, we utilize several ancillarity-sufficiency interweaving strategies (ASIS) for sampling the factor loadings. Estimation and forecasting performance is evaluated for simulated and real-world data.
Original languageEnglish
Title of host publicationProceedings of the 30th International Workshop on Statistical Modelling, Volume 2
Editors Herwig Friedl, Helga Wagner
Place of PublicationLinz
Pages139 - 142
Publication statusPublished - 2015

Austrian Classification of Fields of Science and Technology (ÖFOS)

  • 102022 Software development
  • 101018 Statistics
  • 101026 Time series analysis
  • 502025 Econometrics

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