隨著科技及軟硬體應用的興起，使用統計模型套利的方法逐漸普及，導致配對交易的獲利性也隨著廣泛的使用而逐漸下滑。以往執行配對交易皆假設在單一區間的假設下進行交易，因此在市場波動幅度較大的的情況將容易造成損失。本研究欲結合馬可夫轉換模型與最小平方法選擇配對的策略來探討是否能創造相對於傳統配對交易法較佳的報酬。本研究選擇配對樣本為台灣電子零組件產業之上市公司，進行自2008年至2013年底之實證研究，配對樣本逐季調整。研究中，除了以模型預測區間作為交易準則外，同時在配對選擇方法上，自最小平方法選出之配對中，挑選位於高波動度的區間之配對進行交易，以期能夠提升配對交易之報酬。結果顯示6年的交易期間內，累積報酬在考量交易成本後達270%，顯著高於傳統配對交易法之累積報酬。

With the technological innovation and booming application of computing equipment, the complex statistical models have become popular and low-entrance level tools towards arbitrage, therefore decreasing the profitability of pair trading. Previous studies always implement pair trading under the assumption of single regime. This kind of traditional trading approach will cause huge loss whenever the market is under volatility anomaly. This paper combines the Markov regime-switching model with the least squares method to match pairs, testifying whether these pairs could create abnormal returns. The samples are chosen from the listing companies in Taiwan's electronic components industry, from 2008 to 2013, and adjust pairs in each quarter. We not only utilize the model prediction interval criterion as trading principle, but also incorporate the minimum squared distance method with high-volatility regime deciding by ex ante probability, expecting to enhance returns from pairs trading. Empirical results show that, during the 6-year transaction periods, the cumulative return for our pairs is up to 270% after considering transaction costs. The outcome is significantly higher than the cumulative return of Taiwan Electronic Components Index and traditional pairs trading strategy.

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