在過去很長的一段時間，由於投資債券需要有較大的資本，因此交易對象主要為機構法人與大戶，隨著債券市場蓬勃的發展，市場上逐漸產生關於債券的投資商品，一般的大眾也有越來越多投資債券的機會，如債券基金、債券ETF等。
為了因應新冠疫情帶來的衝擊，聯準會祭出一連串貨幣政策，使市場維持低利率的環境。此外，疫苗出世後市場趨於穩定、利差逐漸收斂，在股市狂熱不斷創高之際，投資人可適度在投資組合中納入高收益債券資產，除了定期領取較高利息外更有望賺取價差。
本研究透過Barra建構風險模型，將市場風險做良好的歸因，且依據純因子模型觀察因子表現，發現Momentum、Value為具有穩定獲利的因子，再透過分層抽樣選取指數中的成分債用以追蹤標竿指數，將追蹤誤差穩定控制在0.9%左右，最後藉由從風險模型發現的風格因子將我們的追蹤投組做增強，將追蹤誤差控制在4%以內且平均Information約為1.2。

Institutions, legal persons, and large investors have been mainly used as transaction objects of bonds for a long time due to the significant capital required for bond investments. With the rapid development of the bond market, many bond investment products emerged on the market. The general public also gained increasing investment opportunities in bonds, such as bond funds and bond ETFs.
To cope with the impacts of the COVID-19 pandemic, the Federal Reserve has launched a series of monetary policies to maintain a low-interest-rate environment in the market. Furthermore, after the release of COVID-19 vaccines, the market stabilized, and interest spreads gradually narrowed. When the stock market frenzy continues to hit highs, investors can moderately include high-yield bond assets in their investment portfolios. In addition to regularly receiving increased interest rates, they are expected to earn spreads.
This study uses the Barra risk factor analysis to construct a risk model to attribute market risks and observe the performance of factors based on a pure factor model. Findings show that momentum and value are stable factors. Constituents in the index are selected through stratified sampling to track the benchmark, and tracking error is stably controlled at approximately 0.9%. Finally, our tracking portfolio is enhanced by the style factor found in the risk model. The tracking error is controlled within 4%, and the average information ratio is approximately 1.2.

論文審定書 i
摘要 ii
Abstract iii
Content iv
List of Figures vi
List of Tables vii
1. Introduction 1
1.1. Background 1
1.2. Motivation for Research 2
1.3. Research Purpose 3
2. Literature Review 5
2.1. Modern Portfolio 5
2.2. Factor of Fixed-Income 6
2.3. Multi Factor Model 7
2.4. Track and Enhanced Index 8
3. Data and Methodology 10
3.1. Data Description 10
3.2. Multi-factor Model Construction 13
3.2.1. Missing Value Process 13
3.2.2. Eliminating Outliers 14
3.2.3. Standardizing Descriptor 15
3.2.4. Compressing Outliers 15
3.2.5. Calculation of Factor Definition 16
3.2.6. Multi-model Factor: Return 20
3.2.7. Common Factor Risk 23
3.2.8. Modeling Covariance Matrices 24
3.2.9. Bias Test 25
3.3. Index Tracking 27
3.4. Index Enhancing 29
4. Empirical Results 31
4.1. Multi Factor Model Result 31
4.1.1. Factor Result 31
4.1.2. Explanatory Power of the Model 35
4.1.3. Empirical Result of Factor Risk 36
4.2. Tracking Index Result 37
4.3. Enhanced Portfolio 39
5. Conclusions and Suggestions 47
5.1. Conclusions 47
5.2. Suggestions 48
Reference 51

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