论文标题
与非线性和非高斯国家空间模型对交易
Pairs Trading with Nonlinear and Non-Gaussian State Space Models
论文作者
论文摘要
本文使用非线性和非高斯州空间模型框架研究了交易。我们将两个资产的价格之间的利差建模为不可观察的状态变量，并假定它遵循均值的过程。这个新模型具有两个独特的特征：（1）传播的创新是非高斯和异性恋。 （2）扩散的平均反向是非线性的。我们展示了如何将过滤的差价用作交易指标进行统计套利。我们还提出了一种新的交易策略，并提出了一种基于蒙特卡洛的方法来选择最佳交易规则。作为第一个经验应用，我们将新模型和新的交易策略应用于两个示例：PEP与KO和EWT vs EWH。结果表明，新方法可以为PEP/KO对获得21.86％的年度回报，而EWT/EWH对的年度回报率为31.84％。作为第二个经验应用，我们将所有可能的对在纽约证券交易所上市的最大和最小的五家银行中。对于这些对，我们将所提出的方法的性能与样本外和样本外的流行方法的性能进行了比较。有趣的是，我们发现几乎所有考虑的情况下，我们的方法可以显着提高回报和夏普比率。
This paper studies pairs trading using a nonlinear and non-Gaussian state-space model framework. We model the spread between the prices of two assets as an unobservable state variable and assume that it follows a mean-reverting process. This new model has two distinctive features: (1) The innovations to the spread is non-Gaussianity and heteroskedastic. (2) The mean reversion of the spread is nonlinear. We show how to use the filtered spread as the trading indicator to carry out statistical arbitrage. We also propose a new trading strategy and present a Monte Carlo based approach to select the optimal trading rule. As the first empirical application, we apply the new model and the new trading strategy to two examples: PEP vs KO and EWT vs EWH. The results show that the new approach can achieve a 21.86% annualized return for the PEP/KO pair and a 31.84% annualized return for the EWT/EWH pair. As the second empirical application, we consider all the possible pairs among the largest and the smallest five US banks listed on the NYSE. For these pairs, we compare the performance of the proposed approach with that of the existing popular approaches, both in-sample and out-of-sample. Interestingly, we find that our approach can significantly improve the return and the Sharpe ratio in almost all the cases considered.
