Dynamic Conditional Correlation GARCH: A Multivariate Time Series Novel using a Bayesian Approach
| Autor: | Israel José dos Santos Felipe, Diego C. Nascimento, Francisco Louzada Neto, Cleber Martins Xavier |
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| Rok vydání: | 2020 |
| Předmět: |
Statistics and Probability
History Multivariate statistics INFERÊNCIA BAYESIANA Financial contagion Polymers and Plastics Series (mathematics) Autoregressive conditional heteroskedasticity Bayesian probability 020207 software engineering 02 engineering and technology Bayesian inference 01 natural sciences Industrial and Manufacturing Engineering Correlation 010104 statistics & probability 0202 electrical engineering electronic engineering information engineering Econometrics Business and International Management 0101 mathematics Statistics Probability and Uncertainty Mathematics |
| Zdroj: | Repositório Institucional da USP (Biblioteca Digital da Produção Intelectual) Universidade de São Paulo (USP) instacron:USP |
| ISSN: | 1538-9472 |
| DOI: | 10.22237/jmasm/1556669220 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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