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

Long Li, Si Bao, Jing-Chao Chen and Tao Jiang

Physica A: Statistical Mechanics and its Applications, 2019, vol. 525, issue C, 1405-1417

Abstract: 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.

Keywords: Technical indicator; Stationary process; Mean reverting process; High-frequency trading; BIAS; Error process; Ornstein–Uhlenbeck process (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:525:y:2019:i:c:p:1405-1417

DOI: 10.1016/j.physa.2019.04.085

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