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}
}
View PDF on arXiv
18 Citations
Highly Influential Citations
2
Background Citations
7
Methods Citations
8
Results Citations
2

Figures and Tables from this paper

18 Citations

Multivariate Stochastic Volatility Estimation Using Particle Filters
A particle filter algorithm is proposed for sequential estimation of volatility and cross-correlation of multivariate financial time series. The returns of prices of assets, such as shares traded in…
Multi‐variate stochastic volatility modelling using Wishart autoregressive processes
A new multi‐variate stochastic volatility estimation procedure for financial time series is proposed. A Wishart autoregressive process is considered for the volatility precision covariance matrix,…
Forecasting with time-varying vector autoregressive models
The purpose of this paper is to propose a time-varying vector autoregressive model (TV-VAR) for forecasting multivariate time series. The model is casted into a state-space form that allows flexible…
A Bayesian GED-Gamma stochastic volatility model for return data: a marginal likelihood approach
TLDR
A Bayesian GED-Gamma SV model with a direct marginal likelihood that is a product of the generalized Student's t-distributions in which the latent states are related across time through a stationary Gaussian evolution equation is proposed.
Non-Gaussian stochastic volatility model with jumps via Gibbs sampler
TLDR
This work presents an alternative model capable of estimating the volatility of financial time series, in an automatic way, since all full conditional posterior distributions are known, and it is possible to obtain an exact sample of parameters via Gibbs Sampler.
Stochastic volatility with big data
The thesis aims to discuss stochastic volatility when a big amount of data is involved. Therefore I follow Windle and Carvalho (2015) and Casarin (2015) papers where a state-space model for…
CHAPTER ON BAYESIAN INFERENCE FOR STOCHASTIC VOLATILITY MODELING
This chapter reviews the major contributions over the last two decades to the literature on the Bayesian analysis of stochastic volatility (SV) models (univariate and multivariate). Bayesian…
A new filtering inference procedure for a GED state-space volatility model
  • Thiago Rezende
  • Computer Science
    Journal of Statistical Planning and Inference
  • 2022
Structural breaks and volatility forecasting in the copper futures market
This paper examines whether structural breaks contain incremental information for forecasting the volatility of copper futures. Considering structural breaks in volatility, we develop four…
Parsimony inducing priors for large scale state–space models
...
...

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…
...
...