研究使用由Wang et at. (2012)等人提出的方法，結合馬可夫狀態轉換模型與Mean-CVaR架構進行動態資產配置為研究主體。當市場處於高風險的狀態時，本研究將下方風險控制於3%內；若市場維持在一般風險狀態，則開放下方風險至7%以下，以此原則建立狀態相依的投資組合。此外，我們將比較以Clayton copula與t- copula兩種不同關聯結構作為資產之間聯合分配所估計出的市場風險預測指標(CVaR)在預測市場狀態時的準確度。
根據研究實證結果顯示，由t-copula方法建構出的投資組合累積報酬率高出標竿投資組合累積報酬率58.27 %，其在金融危機時的跌幅也較標竿投資組合來的小。除此之外，由Clayton copula建構出的投資組合的累積報酬率則較t-copula投資組合高出13.4 %。

This study use the methodology proposed by Wang et al. (2012) which integrates the mean-CVaR framework with Markov regime-switching model to conduct dynamic assets allocation as our main research framework. We control the downside risk at lower than 3% to build a regime-dependent portfolio when the market is in a high risk regime. Additionally, we employ the Clayton copula and compare in to t-copula as a joint distribution to estimate the CVaR as risk indicators to forecast the market regime.
The empirical results of our study show that the cumulative return of a portfolio which is constructed with t-copula increases by 58.27% and the drawdown is reduced successfully compared to the benchmark. Apart from this, the cumulative return of the portfolio constructed with Clayton copula is far higher than the cumulative return of the portfolio constructed with t-copula by 13.4%.

論文審定書 i
摘要 ii
ABSTRACT iii
List of Tables vi
List of Figures vii

I. INTRODUCTION 1
II. LITERATURE REVIEW 3
2.1 The Definition of Value-at-Risk (VaR) 3
2.2 The Introduction and Definition of Conditional Value-at-Risk (CVaR) 4
2.3 Asset Allocation 5
2.4 Application on Portfolio Asset Allocation Using Markov Regime-Switching Model 6
III. METHODOLOGY 9
3.1 Forecasting Market Risk 9
3.2 Forecasting Market Risk Environment 16
3.3 Dynamic Asset Allocation 18
IV. EMPIRICAL RESULTS 21
4.1 Data 21
4.2 Analysis of Markov Regime-Switching Process Results 32
4.3 Analysis of Dynamic Asset Allocation Results 42
V. CONCLUSION 47
VI. REFERENCES 49

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