Testing power-law cross-correlations: rescaled covariance test
Testing power-law cross-correlations: rescaled covariance test
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DOI：
10.1140/epjb/e2013-40705-y
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发表时间：
2013-07
期刊：
影响因子：
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通讯作者：
L. Kristoufek
中科院分区：
文献类型：
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作者：
L. Kristoufek
本文介绍了一种新的检验方法--重标协方差检验。该测试是基于幂律发散的长期互相关过程的部分和的协方差。利用异方差和自相关鲁棒估计的长期协方差，我们开发了一个测试，具有理想的统计特性，能够很好地区分短期和长期的互相关。在估计二元长期记忆参数之前，这种检验应用作分析长期互相关的起点。作为应用，我们证明了金融市场的波动率与交易量、波动率与收益率之间的关系可以被标记为幂律互相关关系。
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 power-law cross-correlated one.