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A method to get a more stationary process and its application in finance with high-frequency data of Chinese index futures

机译:一种获得更加静止过程的方法及其在中国指数期货高频数据的金融中的应用

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摘要

Technical indicators have been widely used in financial markets for a long time. Wang and Zheng (2014) proposed in their book that the technical indicators can be transformed into the stationary process and investigated the profitability and availability. But in fact, we can only test that a data series form a weakly stationary process but a strongly stationary process. Nevertheless, the convergence of a more stationary process will vanish faster, thus it is much better if we can get a more stationary process. In this paper, we propose a method to get a more strongly (or weakly) process named mean reverting process that based on the original strongly (or weakly) stationary process. We particularly give some examples based on high-frequency data of CSI300 Stock Index Futures to show that some technical indicators are mean reverting process. We talk about its advantage and application in high frequency trading. (C) 2019 Elsevier B.V. All rights reserved.
机译:技术指标已广泛用于金融市场长期。 王某和郑(2014)在本书中提出,技术指标可以转变为静止过程,并调查盈利能力和可用性。 但实际上,我们只能测试数据系列形成弱固定过程,而是强烈的静止过程。 尽管如此,更加静止过程的融合将更快地消失,因此如果我们可以获得更加静止的过程会更好。 在本文中,我们提出了一种方法来获得更强烈(或弱)的过程,命名为卑鄙的恢复过程,这是基于原始的强烈(或弱)静止过程。 我们特别提出了基于CSI300股指期货的高频数据的一些示例,以表明某些技术指标是卑鄙的过程。 我们讨论了高频交易中的优势和应用。 (c)2019 Elsevier B.v.保留所有权利。

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