自2000年網路科技泡沫起，全球利率開始下降。緊接而來的2008年金融海嘯，利率更持續了五年的低水準。進而分析美國債券市場也可以發現從2008年以來高收益債券出現爆發型成長。正因如此，投資者開始轉向以債券為主的共同基金為投資目標。本論文以此為背景探討美國高收益債券型基金之風險管理與擇時能力。首先，本文使用Copula函數檢驗高收益債券基金對於股票市場的風險與收益以及短期利率之影響。接著使用古典擇時模型以及ARMAX-GARCH模型檢驗高收益債券市場的系統性風險和基金擇時能力。
    實證結果顯示，高收益債券型基金與股票市場之超額報酬存在正相關，而與短期利率之相關性受到股票市場之影響有輕微正相關。擇時能力的部分我們發現多數高收益債券型基金經理人沒有擇時能力，而不論使用Copula函數還是擇時模型皆出現顯著正向的系統性風險。
    由於美國高收益債券市場完整且不缺乏流動性，高收益債券型基金經理可以靈活的調整自己的投資組合配置和系統性風險，但實證卻無法檢定出基金經理人有擇時能力。換句話說，美國的高收益債券市場雖然有足夠的規模和流動性，但本文仍然建議投資人在選擇美國高收益債券基金商品時要更注意系統性風險。

Since 2000, dot-com bubble, global interest rates began to decline. Immediately came the 2008 financial crisis, more sustained low level of interest rates for five years. Further analysis of the U.S. bond market can also be found explosive type growth since 2008 high yield bond. For this reason, investors are turning to the bond-based mutual funds as an investment target. In this paper, in order to investigate the background of risk management and timing ability of U.S. high yield bond funds. Firstly, we apply Copula function test the high yield bond funds for the equity markets affect the risk and benefit, and short-term interest rates affect. Then using timing ability classical model and ARMAX-GARCH model test high yield bond market systemic risk and fund timing ability.
    The empirical results show that high yield bond funds and stock market that excess returns there is a positive correlation, and short-term interest rates with a slight positive correlation by stock market the impact. As to the timing ability, we found that most high yield bond fund managers do not have the ability, and regardless of the model using Copula functions or market timing are still significantly positive systemic risk. 
    Because the robustand without lack of liquidity of the U.S. high yield bond market, fund managers have the flexibility to adjust their portfolio allocation and systemic risk, but did not test fund managers have the timing ability. In other words, the U.S. high yield bond market there is sufficient size and liquidity, but this paper is still the proposed investment choice of the high yield bond fund goods in U.S. should pay more attention to systemic risk.

目  錄

謝  辭	I
目  錄	IV
表目錄	VI
圖目錄	VII
第一章　緒論	1
第一節  研究背景與動機	1
  第二節  研究目的	4
  第三節  研究架構與流程	5
第二章　相關文獻探討	6
第一節	 美國債券及共同基金市場介紹	6
第二節	 風險管理相關文獻	12
第三節	 擇時能力相關文獻	15
第四節  Copula函數相關文獻	18
第三章  資料與研究方法	21
第一節  資料介紹	21
第二節  Copula模型	22
第三節  古典擇時與ARMAX-GARCH 以CAPM 模型為基礎	28
第四章　實證結果與分析	31
  第一節  Copula關聯性分析	31
第二節  擇時能力	40
第五章　結論與建議	44
第一節	 Copula關聯性分析	44
第二節  擇時能力	46
第三節	 建議	47
參考文獻	49


表目錄
表 1：美國各債券發行量(單位：10億美元)	6
表 2：美國各債券平均每日交易量(單位：10億美元)	7
表 3：美國公司債市場平均每日交易量(單位：10億美元)	9
表 4：美國各共同基金持有資產(單位：10億美元)	10
表 5：美國各共同基金新淨現金流動(單位：10億美元)	11
表 6：基金代碼與基金名稱	21
表 7：高收益債券型基金與道瓊工業指數超額報酬敘述統計	31
表 8：高收益債券型基金與道瓊工業指數超額報酬靜態相關性	32
表 9：高收益債券型基金績效與短期利率靜態相關性	36
表 10：基金淨值敘述統計量	40
表 11：T-M模型和T-M-ARMAX-GARCH模型結果	41
表 12：H-M模型和H-M-ARMAX-GARCH模型結果	42
表 13：C-L模型和C-L-ARMAX-GARCH模型結果	43


圖目錄
圖1：信用評等分類	1
圖2：美國公司債市場發行量	9
圖3：Gaussian Copula蒙地卡羅500次產生隨機變數分佈圖	23
圖4：Student-t Copula蒙地卡羅500次產生隨機變數分佈圖	24
圖5：Clayton Copula蒙地卡羅500次產生隨機變數分佈圖	25
圖6：Gumbel Copula蒙地卡羅500次產生隨機變數分佈圖	26
圖7：Frank Copula蒙地卡羅500次產生隨機變數分佈圖	27
圖8：高收益債券型基金與道瓊工業指數超額報酬動態相關性	33
圖9：高收益債券型基金績效與短期利率動態相關性	37

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三、相關網站
1.http://www.sifma.org/美國證券業與金融市場協會(Securities Industry andFinancialMarkets Association，SIFMA)
2.http://www.masterhsiao.com.tw/MyPages/homePages/home.php?navil1=10怪老子理財網