Pairs trading with general state space models
研究用一般状态空间模型进行配对交易，将价差视为不可观测的均值回复状态变量，并考虑非高斯性、异方差性和非线性均值回复，提出基于蒙特卡洛的最优交易规则，实证显示年化收益率达21.86%至31.84%。
This study examines pairs trading using a general state space model framework. It models the spread between the prices of two assets as an unobservable state variable assuming that it follows a mean-reverting process. This new model has two distinctive features: the (1) non-Gaussianity and heteroscedasticity of innovations to the spread, and (2) nonlinearity of the mean reversion of the spread. It shows how to use the filtered spread as the trading indicator in carrying out statistical arbitrage and proposes a new trading strategy which uses a Monte Carlo-based approach to selecting the optimal trading rule. The new model and trading strategy are illustrated by two examples: PEP vs. KO and EWT vs. EWH. The empirical results show that the new approach can achieve 21.86% (31.84%) annualized return for the PEP-KO (EWT-EWH) pair. Then all the possible pairs among the five largest and the five smallest U.S. banks listed on the NYSE are considered. For these pairs, the performance of the proposed approach with that of the existing popular approaches, are compared both in-sample and out-of-sample. In almost all the cases considered, our approach can significantly improve the return and the Sharpe ratio.