Multivariate stochastic volatility with Bayesian dynamic linear models

@article{Triantafyllopoulos2008MultivariateSV,
  title={Multivariate stochastic volatility with Bayesian dynamic linear models},
  author={Kostas Triantafyllopoulos},
  journal={Journal of Statistical Planning and Inference},
  year={2008},
  volume={138},
  pages={1021-1037}
}

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References

SHOWING 1-10 OF 62 REFERENCES
Bayesian Analysis of Stochastic Volatility Models
Time varying volatility is a characteristic of many financial series. An alternative to the popular ARCH framework is a Stochastic Volatility model which is harder to estimate than the ARCH family.
Multivariate Stochastic Volatility Models: Bayesian Estimation and Model Comparison
In this paper we show that fully likelihood-based estimation and comparison of multivariate stochastic volatility (SV) models can be easily performed via a freely available Bayesian software called
Classical and Bayesian Analysis of Univariate and Multivariate Stochastic Volatility Models
In this paper, efficient importance sampling (EIS) is used to perform a classical and Bayesian analysis of univariate and multivariate stochastic volatility (SV) models for financial return series.
Stochastic Volatility: Likelihood Inference And Comparison With Arch Models
In this paper, Markov chain Monte Carlo sampling methods are exploited to provide a unified, practical likelihood-based framework for the analysis of stochastic volatility models. A highly effective
Multivariate stochastic volatility
We provide a detailed summary of the large and vibrant emerging literature that deals with the multivariate modeling of conditional volatility of financial time series within the framework of
Bayesian Dynamic Factor Models and Portfolio Allocation
We discuss the development of dynamic factor models for multivariate financial time series, and the incorporation of stochastic volatility components for latent factor processes. Bayesian inference
Bayesian inference in a matrix normal dynamic linear model with unknown covariance matrices
In this paper, we consider the problem of estimating the parameters of a matrix normal dynamic linear model when the variance and covariance matrices of its error terms are unknown and can be
Multivariate Stochastic Volatility: A Review
TLDR
A wide range of MSV models is presented, namely, asymmetric models, factor models, time-varying correlation models, and alternative MSV specifications, including models based on the matrix exponential transformation, the Cholesky decomposition, and the Wishart autoregressive process.
Multivariate stochastic variance models
Changes in variance, or volatility, over time can be modelled using the approach based on autoregressive conditional heteroscedasticity (ARCH). However, the generalizations to multivariate series can
Factor Multivariate Stochastic Volatility via Wishart Processes
This paper proposes a high dimensional factor multivariate stochastic volatility (MSV) model in which factor covariance matrices are driven by Wishart random processes. The framework allows for
...
...