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高频欧元期货中的趋势跟随信号识别

文章 arXiv papers · 作者: Laurent Schoeffel
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总结

本研究探讨金融价格序列中是否存在可检测的随机游走之外的结构。作者认为，传统时间序列方法可能难以区分微小的偏离随机性现象，因此采用受核物理学方法启发的现代多变量统计推断。

作者使用欧元期货合约的高频观测数据，报告称可以推断出一些非随机成分，具体来说是依赖于波动区间的趋势跟随行为。摘录未说明触发条件、样本期、统计检验或样本外交易结果。因此，其结论仅涉及在所研究序列中检测到特定结构；这本身并不能证明该信号扣除成本后仍有盈利能力，也不能证明其适用于其他市场。

核心观点

  • 论文考察市场价格变化是否与随机游走存在可检测的差异。
  • 研究采用多变量统计推断来识别细微的非随机结构。
  • 分析使用欧元期货合约的高频数据。
  • 报告的非随机成分包括随波动区间变化的趋势跟随行为。
  • 摘录未提供扣除成本后的交易盈利证据，也未证明结果适用于其他市场。

标签

期货趋势跟踪高频交易统计学
全文
# Statistical Methods for Estimating the non-random Content of Financial Markets


# Statistical Methods for Estimating the non-random Content of Financial Markets









For the pedestrian observer, financial markets look completely random with erratic and uncontrollable behavior. To a large extend, this is correct. At first approximation the difference between real price changes and the random walk model is too small to be detected using traditional time series analysis. However, we show in the following that this difference between real financial time series and random walks, as small as it is, is detectable using modern statistical multivariate analysis, with several triggers encoded in trading systems. This kind of analysis are based on methods widely used in nuclear physics, with large samples of data and advanced statistical inference. Considering the movements of the Euro future contract at high frequency, we show that a part of the non-random content of this series can be inferred, namely the trend-following content depending on volatility ranges.

在遵守原作品许可的前提下，附作者信息全文展示。 许可协议: abstract CC0

此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。