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趋势跟随与均值回归如何影响长期价格方差

文章 arXiv papers · 作者: Lawrence Middleton et al.
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总结

本文研究总体趋势跟随或均值回归交易行为如何改变资产价格动态。研究明确建模交易方向，并将这两种行为与序列相关联系起来：趋势跟随可能产生正相关，而均值回归可能产生负相关。模型用于考察相较随机游走基准，这些相关性如何影响长期方差。

理论分析表明，趋势跟随会提高长期方差，而均值回归行为可能降低长期方差。对美国大型股票和高频EUR/USD数据的应用显示，其可预测性高于随机游走假设下的水平。所提供的描述未给出模型设定、样本期或预测改善指标，也未证明这种可预测性会带来盈利交易策略。因此，最好将这些证据视为理解交易风格如何影响收益依赖性和风险的框架。

核心观点

  • 趋势跟随和均值回归交易风格分别可能引发正价格相关和负价格相关。
  • 作者使用明确表示交易方向的概率模型。
  • 理论结果显示，相较随机游走模型，趋势跟随会提高长期方差。
  • 相较随机游走情形，均值回归条件可能降低长期方差。
  • 对美国大型股票和高频EUR/USD数据的应用报告了更高的可预测性。

标签

趋势跟踪均值回归统计学股票外汇
全文
# Trading styles and long-run variance of asset prices


# Trading styles and long-run variance of asset prices









Trading styles can be classified into either trend-following or mean-reverting. If the net trading style is trend-following the traded asset is more likely to move in the same direction it moved previously (the opposite is true if the net style is mean-reverting). The result of this is to introduce positive (or negative) correlations into the time series. We here explore the effect of these correlations on the long-run variance of the series through probabilistic models designed to explicitly capture the direction of trading. Our theoretical insights suggests that relative to random walk models of asset prices the long-run variance is increased under trend-following strategies and can actually be reduced under mean-reversal conditions. We apply these models to some of the largest US stocks by market capitalisation as well as high-frequency EUR/USD data and show that in both these settings, the ability to predict the asset price is generally increased relative to a random walk.

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。