Bayesian analysis of spherically parameterized dynamic multivariate stochastic volatility models

Guanyu Hu (), Ming-Hui Chen and Nalini Ravishanker
Additional contact information
Guanyu Hu: University of Missouri
Ming-Hui Chen: University of Connecticut
Nalini Ravishanker: University of Connecticut

Computational Statistics, 2023, vol. 38, issue 2, No 12, 845-869

Abstract: Abstract In this paper, we propose multivariate stochastic volatility models with a spherical parameterization of a Cholesky decomposition to make a time-dependent correlation matrix be positive definite without any constraints. An attractive feature of our model is that it can be easily fit using the R package NIMBLE. In addition to the spherical transformation, we introduce a multivariate L measure as a Bayesian model comparison criterion to assess the fit of different models. We present extensive simulation studies to examine the empirical performance of the proposed method and illustrate the methodology on time series of energy usage in a science building on the main campus of the University of Connecticut.Please confirm if the inserted city and country name is correct. Amend if necessary.RightPlease confirm if the corresponding author is correctly identified. Amend if necessary.Right

Keywords: Dynamic correlation; MCMC; Multivariate L measure; Time series (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s00180-022-01266-9 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:compst:v:38:y:2023:i:2:d:10.1007_s00180-022-01266-9

Ordering information: This journal article can be ordered from
http://www.springer.com/statistics/journal/180/PS2

DOI: 10.1007/s00180-022-01266-9

Access Statistics for this article

Computational Statistics is currently edited by Wataru Sakamoto, Ricardo Cao and Jürgen Symanzik

More articles in Computational Statistics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().