英文标题：
《Testing power-law cross-correlations: Rescaled covariance test》
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作者：
Ladislav Kristoufek
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最新提交年份：
2013
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英文摘要：
We introduce a new test for detection of power-law cross-correlations among a pair of time series - the rescaled covariance test. The test is based on a power-law divergence of the covariance of the partial sums of the long-range cross-correlated processes. Utilizing a heteroskedasticity and auto-correlation robust estimator of the long-term covariance, we develop a test with desirable statistical properties which is well able to distinguish between short- and long-range cross-correlations. Such test should be used as a starting point in the analysis of long-range cross-correlations prior to an estimation of bivariate long-term memory parameters. As an application, we show that the relationship between volatility and traded volume, and volatility and returns in the financial markets can be labeled as the one with power-law cross-correlations.
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中文摘要：
我们介绍了一种新的检测一对时间序列之间幂律互相关的测试——重标度协方差测试。该测试基于长程互相关过程部分和协方差的幂律发散。利用长期协方差的异方差和自相关稳健估计，我们开发了一个具有理想统计特性的测试，能够很好地区分短期和长期互相关。在估计双变量长期记忆参数之前，这种测试应该作为分析长期互相关的起点。作为一个应用，我们证明了金融市场中波动性与交易量、波动性与收益之间的关系可以被标记为具有幂律互相关的关系。
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分类信息：

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
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