英文标题：
《A General Framework for Pairs Trading with a Control-Theoretic Point of
  View》
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作者：
Atul Deshpande and B. Ross Barmish
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最新提交年份：
2016
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英文摘要：
  Pairs trading is a market-neutral strategy that exploits historical correlation between stocks to achieve statistical arbitrage. Existing pairs-trading algorithms in the literature require rather restrictive assumptions on the underlying stochastic stock-price processes and the so-called spread function. In contrast to existing literature, we consider an algorithm for pairs trading which requires less restrictive assumptions than heretofore considered. Since our point of view is control-theoretic in nature, the analysis and results are straightforward to follow by a non-expert in finance. To this end, we describe a general pairs-trading algorithm which allows the user to define a rather arbitrary spread function which is used in a feedback context to modify the investment levels dynamically over time. When this function, in combination with the price process, satisfies a certain mean-reversion condition, we deem the stocks to be a tradeable pair. For such a case, we prove that our control-inspired trading algorithm results in positive expected growth in account value. Finally, we describe tests of our algorithm on historical trading data by fitting stock price pairs to a popular spread function used in literature. Simulation results from these tests demonstrate robust growth while avoiding huge drawdowns.
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中文摘要：
配对交易是一种市场中性策略，利用股票之间的历史相关性实现统计套利。文献中现有的成对交易算法要求对潜在的随机股票价格过程和所谓的价差函数进行相当严格的假设。与现有文献相比，我们考虑了一种配对交易算法，该算法需要的限制性假设比之前考虑的更少。由于我们的观点本质上是控制论的，因此非金融专家很容易理解分析和结果。为此，我们描述了一种通用的配对交易算法，该算法允许用户定义一个任意的价差函数，该函数在反馈上下文中用于随时间动态修改投资水平。当该函数与价格过程相结合，满足某种均值回归条件时，我们将股票视为可交易对。在这种情况下，我们证明了我们的控制激励交易算法会导致账户价值的正预期增长。最后，我们通过将股票价格对拟合到文献中常用的价差函数，描述了我们的算法在历史交易数据上的测试。这些测试的模拟结果表明，在避免大幅下降的同时，增长强劲。
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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：Computational Finance        计算金融学
分类描述：Computational methods, including Monte Carlo, PDE, lattice and other numerical methods with applications to financial modeling
计算方法，包括蒙特卡罗，偏微分方程，格子和其他数值方法，并应用于金融建模
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