Dynamic Conditional Correlation GARCH: A Multivariate Time Series Novel using a Bayesian Approach
Abstract
The Dynamic Conditional Correlation GARCH (DCC-GARCH) mutation model is considered using a Monte Carlo approach via Markov chains in the estimation of parameters, time-dependence variation is visually demonstrated. Fifteen indices were analyzed from the main financial markets of developed and developing countries from different continents. The performances of indices are similar, with a joint evolution. Most index returns, especially SPX and NDX, evolve over time with a higher positive correlation.
Más información
| Título según WOS: | ID WOS:000605728200006 Not found in local WOS DB |
| Título de la Revista: | JOURNAL OF MODERN APPLIED STATISTICAL METHODS |
| Volumen: | 18 |
| Número: | 1 |
| Editorial: | WAYNE STATE UNIV PRESS |
| Fecha de publicación: | 2019 |
| DOI: |
10.22237/jmasm/1556669220 |
| Notas: | ISI |