Sparse Bayesian Time-Varying Covariance Estimation in Many Dimensions

Kastner, Gregor (2016) Sparse Bayesian Time-Varying Covariance Estimation in Many Dimensions. Research Report Series / Department of Statistics and Mathematics, 129. WU Vienna University of Economics and Business, Vienna.


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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)

Item Type: Paper
Keywords: dynamic conditional correlation / factor stochastic volatility / curse of dimensionality / shrinkage / predictive distribution
Classification Codes: JEL C32; C51; C58
Divisions: Departments > Finance, Accounting and Statistics > Statistics and Mathematics
Depositing User: Gregor Kastner
Date Deposited: 19 Sep 2016 07:11
Last Modified: 22 Oct 2019 00:41


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