动态条件相关GARCH：一种基于贝叶斯方法的多变量时间序列新方法

Q3 Mathematics

Journal of Modern Applied Statistical Methods Pub Date : 2020-02-25 DOI:10.22237/jmasm/1556669220

Diego Nascimento, C. Xavier, I. Felipe, F. L. Neto

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引用次数: 2

摘要

在参数估计中，考虑了动态条件相关GARCH（DCC-GARCH）变异模型，通过马尔可夫链采用蒙特卡罗方法，直观地展示了时间相关性变化。对来自不同大陆的发达国家和发展中国家的主要金融市场的15个指数进行了分析。指数的表现是相似的，具有共同的演变。大多数指数回报率，尤其是SPX和NDX，随着时间的推移，呈现出更高的正相关性。

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

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.

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来源期刊

Journal of Modern Applied Statistical Methods STATISTICS & PROBABILITY-

CiteScore

0.50

自引率

0.00%

发文量

期刊介绍： The Journal of Modern Applied Statistical Methods is an independent, peer-reviewed, open access journal designed to provide an outlet for the scholarly works of applied nonparametric or parametric statisticians, data analysts, researchers, classical or modern psychometricians, and quantitative or qualitative methodologists/evaluators.