In the paper a novel class of the conditional distributions of the Copula-based M-GARCH models is studied. Initially we consider the multivariate distribution, defined as a product of independent univariate skewed Student-t components. Then, an orthogonal transformation is applied in order to model heavy tails and asymmetry along free set of coordinate axes. We apply the Bayesian approach to model comparison and check the empirical importance of proposed generalisations. Also the posterior inference about some bivariate processes on the Warsaw Stock Exchange is presented.

1. PRESENTATION: On the empirical importance of the orthogonal transformation in copula-based M-GARCH models. bayesian comparison, PDF document, version 1.4, 1.4MB

• Legal notice:
  

  Copyright (c) Pielaszek Research, all rights reserved.
  The above materials, including auxiliary resources, are subject to Publisher's copyright and the Author(s) intellectual rights. Without limiting Author(s) rights under respective Copyright Transfer Agreement, no part of the above documents may be reproduced without the express written permission of Pielaszek Research, the Publisher. Express permission from the Author(s) is required to use the above materials for academic purposes, such as lectures or scientific presentations.
  In every case, proper references including Author(s) name(s) and URL of this webpage: https://science24.com/paper/23348 must be provided.

1. Some recent advances in empirical analyses of economic cycles
2. Almost Periodically Correlated Time Series in Business Fluctuations Analysis
3. An approach to measuring the relation between risk and return. Bayesian analysis for WIG data.
4. Bayesian Comparison of GARCH Processes with Asymmetric and Heavy Tailed Conditional Distributions