Dynamic modeling of mean-reverting spreads for statistical arbitrage
Dynamic modeling of mean-reverting spreads for statistical arbitrage
复制标题
统计套利均值回归利差的动态建模
DOI：
10.1007/s10287-009-0105-8
复制
发表时间：
2008
影响因子：
0.9
通讯作者：
G. Montana
中科院分区：
文献类型：
--
作者：
K. Triantafyllopoulos;G. Montana
统计套利策略，如配对交易及其推广依赖于构建具有一定程度可预测性的均值回复价差。高斯线性状态空间过程最近被提出作为这样的传播模型的假设下，观察到的过程是一个嘈杂的实现一些隐藏的状态。对未观察到的价差过程的实时估计可以揭示暂时的市场效率低下，然后可以利用这些市场效率低下来产生超额收益。我们拥抱的状态空间模型扩散过程的框架，并沿沿着三个不同的方向扩展这种方法。首先，我们在模型参数中引入时间依赖性，这允许快速适应数据生成过程中的变化。其次，我们提供了一个在线估计算法，可以不断运行在实时。由于计算速度快，该算法特别适合于基于高频数据建立激进的交易策略，并可用作均值回归的监控设备。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。基于蒙特卡罗模拟和历史股票数据的实验结果进行了讨论，包括涉及两个交易所交易基金的协整关系。
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. 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.