Sparse Bayesian Time-Varying Covariance Estimation in Many Dimensions

Gregor Kastner

Publikation: Working/Discussion PaperWU Working Paper

221 Downloads (Pure)

Abstract

Dynamic covariance estimation for multivariate time series suffers from the curse of dimensionality. This renders parsimonious estimation methods essential for conducting reliable statistical inference. In this paper, the issue is addressed by modeling the underlying co-volatility dynamics of a time series vector through a lower dimensional collection of latent time-varying stochastic factors. Furthermore, we apply a Normal-Gamma prior to the elements of the factor loadings matrix. This hierarchical shrinkage prior effectively pulls the factor loadings of unimportant factors towards zero, thereby increasing parsimony even more. We apply the model to simulated data as well as daily log-returns of 300 S&P 500 stocks and demonstrate the effectiveness of the shrinkage prior to obtain sparse loadings matrices and more precise correlation estimates. Moreover, we investigate predictive performance and discuss different choices for the number of latent factors. Additionally to being a stand-alone tool, the algorithm is designed to act as a "plug and play" extension for other MCMC samplers; it is implemented in the R package factorstochvol. (author's abstract)
OriginalspracheEnglisch
ErscheinungsortVienna
HerausgeberWU Vienna University of Economics and Business
DOIs
PublikationsstatusVeröffentlicht - 18 Sept. 2016

Publikationsreihe

ReiheResearch Report Series / Department of Statistics and Mathematics
Nummer129

WU Working Paper Reihe

  • Research Report Series / Department of Statistics and Mathematics

Zitat