本文著重於股票市場，而股票指數的目標國家為葡萄牙，義大利，愛爾蘭，希臘和西班牙，而這五個國家被經濟學家稱為歐豬五國。此外，我們添加其他三個國家，分別為美國、英國和德國。本文目的是想要探討在2001到2011的樣本期間內這些國家之間的股市相關結構的變化。
我們所使用的模型稱為copula模型，而本文的copula模型是奠基於Gaussian copula方法論，在邊際分配上本文是依據GARCH模型規範，而在聯合分配上是使用Gaussian copula模型。
將相依性參數建構在Copula函數上可以使得依賴性參數成為一個奠基於現在所擁有的信息的動態過程，而這一個動態過程允許時間的變化和非線性的關係。我們的方法結合Copula函數和regime-switching模型，並進一步去證明在這些國家的股市之間存在著相依性的結構。
最後，我們對國際投資者有兩點報告。首先，如果相依性結構隨著時間而變化，那麼在金融危機時期，國際投資者依據多角化理論所建構的投資組合將會出現多角化的災害，而多角化程度越高將會導致更高的系統性風險。其次，根據實證的結果，不對稱的相依性結構確實存在於金融市場，這個現象印證了多角化理論所建構的投資組合將會導致更高的系統性風險，因此我們得出這樣的結論：在金融危機時期，非多角化理論所建構的投資組合可能優於多角化理論所建構的投資組合。

This paper focuses on stock markets, including Portugal、Italy、Ireland、Greece and Spain, and these are named PIGS by economists. Furthermore, we add the other three countries, U.S.A.、U.K. and Germany in this paper for investigating the dependence structure in the stock markets between these countries during the period 2001-2011. We implement a regime-switching copula model based on Gaussian copula, which uses a GARCH specification for the marginal distributions and the Gaussian copula for the joint distribution. Our method combines copulas and regime-switching models to demonstrate dependence sructures in stock markets between these countries.
Based on this paper, we have two reports for international investors. First, if the dependency changes over time, the returns of portfolio diversification may be prone to diversification disasters, and the international investors' degrees of diversification can cause higher systemic risk in the period of financial crisis. Second, the phonomenon of the asymmetric dependence exists in financial markets, and we conclude that non-diversification may be better than diversification in the period of financial crisis.

論文審定書 i
摘要 ii
Abstract iii
Tables v
Figures vi
1. Introduction 1
2. Causes of European sovereign debt crisis 7
3. Methodology 10
3.1. Conditional copula 10
3.2. Regime switches in copula 11
3.3. Estimation of parameters 13
3.4. Models for Marginal Distributions and Bivariate Distributions 14
3.5. Models for Bivariate Distributions 15
4. Data and Descriptive Statistics 16
5. Conclusion 23
Reference 25

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