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

被引：1

作者：

Nascimento, Diego ^{[1
]}

Xavier, Cleber ^{[1
]}

Felipe, Israel ^{[2
]}

Louzada Neto, Francisco ^{[1
]}

机构：

[1] Univ Sao Paulo, Sao Paulo, Brazil

[2] Univ Fed Ouro Preto, Ouro Preto, Brazil

来源：

JOURNAL OF MODERN APPLIED STATISTICAL METHODS | 2019年 / 18卷 / 01期

关键词：

DCC-GARCH; Bayesian model; visual data mining; financial contagion;

D O I：

10.22237/jmasm/1556669220

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

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.

引用

页数：16

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