Building Dynamic Correlation Network for Financial Asset Returns
Building Dynamic Correlation Network for Financial Asset Returns
复制标题
构建金融资产收益动态关联网络
DOI：
10.1109/sitis.2015.39
复制
发表时间：
2015
期刊：
影响因子：
--
通讯作者：
Takashi Isogai
中科院分区：
文献类型：
--
作者：
Takashi Isogai
研究了高波动性金融收益的动态相关矩阵估计问题，并利用动态相关矩阵建立了动态相关网络。广泛使用的通过固定样本周期的移动窗口来计算时间相关线性相关矩阵的方法在应用于厚尾收益率时可能存在基本问题。为了克服这一困难，本文采用了一个多元波动率模型DCC-GARCH来过滤厚尾收益率，并估计收益率的动态相关性。计算了时间相关矩阵，并与传统的计算方法进行了比较，突出了动态相关方法的优点。作为一个案例研究，该模型被拟合到日本股票收益率，分析动态变化的相关矩阵。该方法不仅适用于金融收益率，也可应用于其他高波动性时间序列数据的动态相关网络构建。
This paper studies the dynamic correlation matrix estimation of highly volatile financial returns, which is used to build a dynamic correlation network. The widely used method of calculating time-dependent linear correlation matrices by moving window of a fixed sample period can have fundamental problems when applied to fat-tailed returns. A multivariate volatility model, DCC-GARCH, is employed to filter the fat-tailed returns and estimate the dynamic correlation of returns in order to overcome such difficulties. The time-dependent correlation matrices are calculated and compared with the ones that are calculated by the traditional calculation method to highlight the advantages of the proposed dynamic correlation based method. As a case study, the model is fitted to the Japanese stock returns to analyze dynamic changes in the correlation matrix. The method is not limited to financial returns, but can also be applied to build a dynamic correlation network of other time series data with high volatility.
影响因子：
1.6
作者：
Mantegna, RN
通讯作者：
Mantegna, RN