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衡量金融市场间的线性与非线性因果关系

文章 arXiv papers · 作者: Haochun Ma et al.
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总结

本文提出一个框架，用于研究金融工具之间超越传统相关性的依赖关系。该框架采用传递熵和收敛交叉映射识别因果关系，再利用傅里叶变换替代序列区分线性与非线性贡献。研究动机是：相关性虽能较好反映线性因果关系，却可能遗漏非线性依赖，从而低估市场之间相互影响的程度。

作者报告德国和美国股票指数之间存在显著的非线性因果关系。他们讨论了衡量这两类因果关系如何支持市场信号研究、配对交易和投资组合风险管理，包括提供异常行为的早期预警信号。这些发现为进一步应用提供了动机，但并未证明此类信号能够可靠地改善交易结果。所述证据仅针对被研究的指数；摘录未提供样本、稳健性检验或基于这些度量构建的策略的实际表现细节。

核心观点

  • 皮尔逊相关性能够捕捉线性依赖，但可能遗漏非线性关系。
  • 该框架结合传递熵、收敛交叉映射与替代序列分析。
  • 研究报告德国和美国股票指数之间存在非线性因果关系。
  • 因果关系度量可能有助于配对交易、信号研究和投资组合风险管理。
  • 摘录未证明基于这些度量构建的策略具有何种交易表现。

标签

股票统计学配对交易风险管理
全文
# Linear and nonlinear causality in financial markets


# Linear and nonlinear causality in financial markets









Identifying and quantifying co-dependence between financial instruments is a key challenge for researchers and practitioners in the financial industry. Linear measures such as the Pearson correlation are still widely used today, although their limited explanatory power is well known. In this paper we present a much more general framework for assessing co-dependencies by identifying and interpreting linear and nonlinear causalities in the complex system of financial markets. To do so, we use two different causal inference methods, transfer entropy and convergent cross-mapping, and employ Fourier transform surrogates to separate their linear and nonlinear contributions. We find that stock indices in Germany and the U.S. exhibit a significant degree of nonlinear causality and that correlation, while a very good proxy for linear causality, disregards nonlinear effects and hence underestimates causality itself. The presented framework enables the measurement of nonlinear causality, the correlation-causality fallacy, and motivates how causality can be used for inferring market signals, pair trading, and risk management of portfolios. Our results suggest that linear and nonlinear causality can be used as early warning indicators of abnormal market behavior, allowing for better trading strategies and risk management.

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。