配對交易為一種廣為人知的數量化投資策略，Liew and Wu (2013) 將關聯式結構模型應用在配對交易策略上，關聯式結構模型可以清楚地捕捉兩資產間的關係，不論資產報酬是否服從常態分配。
本文主要目的在於找尋適合關聯式結構模型的配對，來建構陪對交易策略的投資組合，以及提出一種停損機制來改善投資組合的績效。我們使用台灣證券交易所2003年1月到2013年6月的股價日資料進行實證研究，此資料經由台灣新報資料庫(TEJ)取得。我們發現選擇高一致性並且在同一產業的配對具有不錯的獲利表現，而加入停損機制後，平均年報酬將可以從7.6%提升至9.6%。此外，本文利用相同的標的股票，來比較採用距離法與關聯式結構模型的優劣在執行配對交易之上。

Pairs trading is a popular quantitative investment strategy in the finance industry. Liew and Wu (2013) propose a new approach for pairs trading. This study focuses on how to use a copula function to construct a pairs trading portfolio, including suitable pairs selection and stop loss framework.
This study applies a copula function to the pairs trading strategy with daily data over the period from January 2003 to June 2013 in the Taiwan Stock Exchange (TWSE). We choose high dependence pairs and restrict two stocks to belong to the same industry and use the copula approach that is proposed by Liew and Wu (2013) to identify the relative position between two stocks and decide the transaction threshold. By doing this, the return of our portfolio is 7.6% and standard deviation is 0.17 from January 2005 to June 2013.
Furthermore, we also test the stop loss framework. If the conditional probability stays in the extreme region too long, it will be considered that the stable relationship between two stocks dies away and we will close the position to avoid a huge loss. Stop loss framework can significantly increase profitability. The annual return increases from 7.6% to 9.6%.

CONTENTS
論文審定書 i
誌謝 ii
摘要 iii
ABSTRACT iv
Index of Tables v
Index of Figures vi
I. INTRODUCTION 1
1.1 Background information 1
1.2 Research objective 4
1.3 Research structure 5
II. LITERATURE REVIEW 7
2.1 Original strategy 7
2.2 Different scenario analysis 10
2.3 New approach in pairs trading 12
III. METHODOLOGY 14
3.1 Data 14
3.2 Copula approach 15
3.3 Pairs formation 17
3.4 Trading period 20
3.5 Performance evaluation 23
IV. EMPIRICAL RESULTS 25
4.1 Concordance analysis 26
4.2 Industry analysis 31
4.3 Stop loss analysis 34
4.4 Compare copula approach with traditional distance approach 39
V. CONCLUSIONS 42
5.1 Pairs selection analysis 43
5.2 Stop loss analysis 44
5.3 Comparison analysis 45
5.4 Recommendation for future research 45

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