Analysing the information flow between financial time series - An improved estimator for transfer entropy
Analysing the information flow between financial time series - An improved estimator for transfer entropy
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DOI：
10.1140/epjb/e2002-00379-2
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发表时间：
2002-11-01
影响因子：
1.6
通讯作者：
Kantz, H
中科院分区：
文献类型：
--
作者：
Marschinski, R;Kantz, H
在最近引入的转移熵的概念，我们试图衡量两个金融时间序列，道琼斯指数和DAX股票指数之间的信息流。基于香农熵，这种无模型方法原则上允许我们检测所有类型的统计依赖关系，即线性和非线性时间相关性。然而，当可用的数据是有限的，预期的效果是相当小的，一个简单的实施遭受严重的错误估计，由于有限的样本效应，使它基本上不可能评估所获得的值的意义。因此，我们引入了一个修改后的估计，称为有效转移熵，从而导致在这种条件下的改进结果。在应用程序中，我们可以确认两个金融时间序列之间的一分钟时间尺度上的信息传输。从数据中也恢复了两个指数对经济的不同影响。数值结果，然后解释一方面作为一个指数的能力，以解释未来的观察，另一方面在一个双变量自回归随机模型的框架内的耦合强度。道琼斯指数和DAX指数之间的耦合的非线性特征的证据。
Following the recently introduced concept of transfer entropy, we attempt to measure the information flow between two financial time series, the Dow-Jones and DAX stock index. Being based on Shannon entropies, this model-free approach in principle allows us to detect statistical dependencies of all types, i.e. linear and nonlinear temporal correlations. However, when available data is limited and the expected effect is rather small, a straightforward implementation suffers' badly from misestimation due to finite sample effects, making it basically impossible to assess the significance of the obtained values. We therefore introduce a modified estimator, called effective transfer entropy, which leads to improved results in such conditions. In the application, we then manage to confirm an information transfer on a time scale of one minute between the two financial time series. The different economic impact of the two indices is also recovered from the data. Numerical results are then interpreted on one hand as capability of one index to explain future observations of the other, and on the other hand within terms of coupling strengths in the framework of a bivariate autoregressive stochastic model. Evidence is given for a nonlinear character of the coupling between Dow Jones and DAX.