本研究選取兩種資料：單純的美國信用違約交換市場買賣報價資料；另外就是結合美國信用違約交換買賣報價資料與相關公司債殖利率的資料。透過無損卡爾曼濾波估計法，在平方根隨機過程的信用風險模型設定下，我們估計出兩種違約因子。之後，再將信用違約交換資料進行主成分分析，然後用第一主成分與兩種違約因子進行迴歸，而萃取出兩個新流動性風險因子。經過實證分析結果得知：這兩個新流動性風險因子確實為可作為流動性風險代理變數，但是結合信用違約交換報價與債券殖利率所求算出來的新流動性因子，其解釋能力比單純透過信用違約交換報價所萃取出來的新流動性風險因子高。本研究也發現：透過信用違約交換資料及公司債殖利率資料兩者聯合估計之新流動性風險因子對市場利率的解釋能力，比單純透過信用違約交換報價所萃取出來的新流動性風險因子要來得高；即使在加入總體經濟因素當作控制變數以後，結果仍然一樣，顯示透過聯合估計新流動性風險因子將是較好之選項。

This paper employs two kinds of data. One is Credit Default Swap (CDS) market quotations in the United States; the other is the corporate bond yield data matched with related CDS market quotations. We estimate the hazard rates of the one-factor squared root process designed for credit risk model with an unscented Kalman filter on two different sets of data. We conduct principal components analysis of the CDS premiums across different reference entities. Then, we conduct the regression tests of the first principal component on two kinds of hazard rates estimated and subsequently, we extract two liquidity factors by calculating the residuals of each regression equation. Empirical examination indicates that the liquidity risk factors estimated in this paper can be good proxies for liquidity risk. We discover that the liquidity risk factor extracted from CDS market quotations combined with corporate bond yield rates has more goodness of fit than the other factor extracted purely from CDS market quotations. It is demonstrated that the liquidity factor extracted from CDS market quotations combined with corporate bond yield rates is more significantly related to interest rate measures than the one extracted from pure CDS market quotations. The results are still the same even after we add in some macroeconomic variables as control variables. Therefore, we conclude that the liquidity factor extracted from CDS market quotations combined with corporate bond yield rates may be a better alternative.