• Corpus ID: 88520952

BayesDccGarch - An Implementation of Multivariate GARCH DCC Models

@article{Fioruci2014BayesDccGarchA,
  title={BayesDccGarch - An Implementation of Multivariate GARCH DCC Models},
  author={Jos{\'e} Augusto Fioruci and Ricardo S. Ehlers and Francisco Louzada},
  journal={arXiv: Computation},
  year={2014}
}
Multivariate GARCH models are important tools to describe the dynamics of multivariate times series of financial returns. Nevertheless, these models have been much less used in practice due to the lack of reliable software. This paper describes the {\tt R} package {\bf BayesDccGarch} which was developed to implement recently proposed inference procedures to estimate and compare multivariate GARCH models allowing for asymmetric and heavy tailed distributions. 

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References

SHOWING 1-10 OF 19 REFERENCES

Multivariate GARCH Models

This article contains a review of multivariate GARCH models. Most common GARCH models are presented and their properties considered. This also includes nonparametric and semiparametric models.

Dynamic Conditional Correlation : A Simple Class of Multivariate GARCH Models

Time varying correlations are often estimated with Multivariate Garch models that are linear in squares and cross products of returns. A new class of multivariate models called dynamic conditional

Bayesian multivariate GARCH models with dynamic correlations and asymmetric error distributions

TLDR
This work develops and applies stochastic simulation techniques for GARCH models with multivariate skewed distributions using the Bayesian approach and considers a flexible class of multivariate distributions which can model both skewness and heavy tails.

Computational tools for comparing asymmetric GARCH models via Bayes factors

  • R. Ehlers
  • Computer Science, Mathematics
    Math. Comput. Simul.
  • 2012

Bayesian Estimation of the GARCH(1,1) Model with Normal Innovations

In this article, we propose the Bayesian estimation of the parsimonious but effective GARCH(1,1) model with Normal innovations. We sample the parameters joint posterior distribution using the

A New Class of Multivariate Skew Densities, With Application to Generalized Autoregressive Conditional Heteroscedasticity Models

We propose a practical and flexible method to introduce skewness in multivariate symmetric distributions. Applying this procedure to the multivariate Student density leads to a “multivariate

Bayesian Comparison of GARCH Processes with Skewnes Mechanism in Conditional Distributions

TLDR
This paper discusses the results of Bayesian comparison of alternative skewing mechanisms applied in the initial Student-t GARCH framework and presents formal Bayesian inference about conditional asymmetry of the distribution of the daily returns in all competing specifications on the basis of the skewness measure defined by Arnold and Groenveld (1995).

A Multivariate GARCH Model with Time-Varying Correlations

In this paper we propose a new multivariate GARCH model with time-varying correlations. We adopt the vech representation based on the conditional variances and the conditional correlations. While

Generalized autoregressive conditional heteroskedasticity