英文标题：
《Identification of cross and autocorrelations in time series within an
approach based on Wigner eigenspectrum of random matrices》
---
作者：
Michal Sawa, Dariusz Grech
---
最新提交年份：
2014
---
英文摘要：
We present an original and novel method based on random matrix approach that enables to distinguish the respective role of temporal autocorrelations inside given time series and cross correlations between various time series. The proposed algorithm is based on properties of Wigner eigenspectrum of random matrices instead of commonly used Wishart eigenspectrum methodology. The proposed approach is then qualitatively and quantitatively applied to financial data in stocks building WIG (Warsaw Stock Exchange Index).
---
中文摘要：
我们提出了一种基于随机矩阵方法的新颖方法，该方法能够区分给定时间序列中时间自相关的各自作用以及不同时间序列之间的互相关。该算法基于随机矩阵的Wigner特征谱的性质，而不是常用的Wishart特征谱方法。然后，将所提出的方法定性和定量地应用于股票构建WIG（华沙股票交易所指数）中的财务数据。
---
分类信息：

一级分类：Physics 物理学
二级分类：Data Analysis, Statistics and Probability 数据分析、统计与概率
分类描述：Methods, software and hardware for physics data analysis: data processing and storage; measurement methodology; statistical and mathematical aspects such as parametrization and uncertainties.
物理数据分析的方法、软硬件：数据处理与存储；测量方法；统计和数学方面，如参数化和不确定性。
--
一级分类：Quantitative Finance 数量金融学
二级分类：Statistical Finance 统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
--

---
PDF下载：
-->