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Bayesian analysis of spherically parameterized dynamic multivariate stochastic volatility models

Author

Listed:
  • Guanyu Hu

    (University of Missouri)

  • Ming-Hui Chen

    (University of Connecticut)

  • Nalini Ravishanker

    (University of Connecticut)

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

Suggested Citation

  • Guanyu Hu & Ming-Hui Chen & Nalini Ravishanker, 2023. "Bayesian analysis of spherically parameterized dynamic multivariate stochastic volatility models," Computational Statistics, Springer, vol. 38(2), pages 845-869, June.
    • Handle: RePEc:spr:compst:v:38:y:2023:i:2:d:10.1007_s00180-022-01266-9
    DOI: 10.1007/s00180-022-01266-9
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    References listed on IDEAS

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