Dynamic Conditional Correlation GARCH: A Multivariate Time Series Novel using a Bayesian Approach

Authors

Diego Nascimento, University of Sao PauloFollow
Cleber Xavier, University of Sao PauloFollow
Israel Felipe, Federal University of Ouro PretoFollow
Francisco Louzada Neto, University of Sao PauloFollow

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.

DOI

10.22237/jmasm/1556669220

Recommended Citation

Nascimento, D., Xavier, C., Felipe, I., & Neto, F. L. (2019). Dynamic conditional correlation GARCH: A multivariate time series novel using a Bayesian approach. Journal of Modern Applied Statistical Mehtods, 18(1), eP2722. doi: 10.22237/jmasm/1556669220