摘要翻译：
统计套利策略，如成对交易及其推广，依赖于构建具有一定可预测性的均值还原利差。高斯线性状态空间过程最近被提出作为这种扩展的模型，假设被观察的过程是一些隐藏状态的噪声实现。对未观察到的价差过程的实时估计可以揭示暂时的市场低效，然后可以利用这些低效来产生超额收益。在前人工作的基础上，我们采用状态空间框架来建模传播过程，并沿着三个不同的方向扩展该方法。首先，我们在模型参数中引入了时间依赖性，这使得在数据生成过程中能够快速地适应变化。其次，我们提供了一个在线估计算法，可以不断地实时运行。该算法计算速度快，特别适用于建立基于高频数据的激进交易策略，并可作为均值回归的监测装置。最后，我们的框架自然地提供了所有估计参数的信息不确定性度量。本文讨论了基于Monte Carlo模拟和历史股票数据的实验结果，包括两个交易所交易基金的协整关系。
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英文标题：
《Dynamic modeling of mean-reverting spreads for statistical arbitrage》
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作者：
Kostas Triantafyllopoulos and Giovanni Montana
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最新提交年份：
2009
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Statistical Finance        统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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一级分类：Quantitative Finance        数量金融学
二级分类：Portfolio Management        项目组合管理
分类描述：Security selection and optimization, capital allocation, investment strategies and performance measurement
证券选择与优化、资本配置、投资策略与绩效评价
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一级分类：Statistics        统计学
二级分类：Applications        应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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一级分类：Statistics        统计学
二级分类：Methodology        方法论
分类描述：Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计，调查，模型选择，多重检验，多元方法，信号和图像处理，时间序列，平滑，空间统计，生存分析，非参数和半参数方法
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英文摘要：
  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.
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PDF链接：
https://arxiv.org/pdf/0808.1710