统计套利均值回归价差的在线状态空间估计
文章 arXiv papers · 作者: Kostas Triantafyllopoulos et al.
总结
这篇论文提出一种状态空间方法，用于对统计套利中的均值回归价差建模，包括配对交易。该方法将观测到的价差视为隐藏状态的噪声读数，并使用这些状态的实时估计来识别暂时性的市场低效。该框架通过允许参数随时间变化，扩展了早期的高斯线性状态空间模型，使估计能够适应数据生成过程的变化。
作者还提出一种在线估计算法，旨在持续运行，包括处理高频数据，并生成参数估计的不确定性度量。论文讨论了使用蒙特卡洛模拟和历史股票数据进行的实验，其中包括两只交易所交易基金之间的协整关系。文档没有提供绩效数据或交易成本分析，因此不能证明识别出的低效能够转化为可执行的超额收益。文中仅概述了证据，细节不足以评估其稳健性。
核心观点
- 该框架将观测价差建模为高斯线性状态空间过程中隐藏状态的噪声实现值。
- 允许模型参数随时间变化，有助于适应数据变化。
- 在线算法实时估计潜在价差和参数。
- 参数不确定性度量可用于监测均值回归。
- 文中介绍了蒙特卡洛模拟和历史股票数据案例，包括两只ETF之间的协整关系。
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# Dynamic modeling of mean-reverting spreads for statistical arbitrage # Dynamic modeling of mean-reverting spreads for statistical arbitrage Statistical arbitrage strategies, such as pairs trading and its generalizations, rely on the construction of mean-reverting spreads enjoying a certain degree of predictability. Gaussian linear state-space processes have recently been proposed as a model for such spreads under the assumption that the observed process is a noisy realization of some hidden states. Real-time estimation of the unobserved spread process can reveal temporary market inefficiencies which can then be exploited to generate excess returns. Building on previous work, we embrace the state-space framework for modeling spread processes and extend this methodology along three different directions. First, we introduce time-dependency in the model parameters, which allows for quick adaptation to changes in the data generating process. Second, we provide an on-line estimation algorithm that can be constantly run in real-time. Being computationally fast, the algorithm is particularly suitable for building aggressive trading strategies based on high-frequency data and may be used as a monitoring device for mean-reversion. Finally, our framework naturally provides informative uncertainty measures of all the estimated parameters. Experimental results based on Monte Carlo simulations and historical equity data are discussed, including a co-integration relationship involving two exchange-traded funds.
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。