Kastner, Gregor, Frühwirth-Schnatter, Sylvia, Lopes, Hedibert Freitas. 2015. Dynamic covariance estimation using sparse Bayesian factor stochastic volatility models. In Proceedings of the 30th International Workshop on Statistical Modelling, Volume 2, Hrsg. Herwig Friedl, Helga Wagner, S. 139-142. Linz: None.

RIS

@INPROCEEDINGS{Kastner2015,
title = {Dynamic covariance estimation using sparse Bayesian factor stochastic volatility models},
author = {Gregor Kastner and Sylvia Frühwirth-Schnatter and Hedibert Freitas Lopes},
publisher = {None},
year = {2015},
address = {Linz},
url = {http://ifas.jku.at/iwsm2015/},
language = {EN},
pages = {139-142},
series = {Proceedings of the 30th International Workshop on Statistical Modelling, Volume 2},
abstract = {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.},
}