Abstract
S Statistical arbitrage is one of the key topics of research today due to its significance statistical features and the ability to control the drawdown. Previous researchers have implemented statistical arbitrage based on various approaches. However, some of these approaches lack depth and coherence. This paper therefore provides an in-depth assessment of the two scenarios. Specifically, the two approaches are distance and time-series models. The former one uses non-parametric distance indicators to identify pair trading opportunities, while the latter seeks to find optimal trading rules by examining and combing through the relevant references. The basic principles as well as procedures of them are presented and clarified. According to the analysis, this paper reveals future research directions and the strengths and weaknesses of the approaches. In addition, the current limitations as well as prospects for the improvements of the models are given. These results shed light on guiding further exploration of pairs trading in statistical arbitrage.
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