貨幣政策的執行與最終目標的達成往往會產生時間落差的問題，本文旨在提供貨幣當局一短期指標作為中間目標之輔助工具。首先，選取台灣金融部門外匯、貨幣、債券與股票市場相關變數1996年1月至2017年12月之月資料，透過主成分分析法以及德意志銀行分析法建構出金融情勢指數(Financial Condition Index,FCI)，兩種方法最大的差異在於前者利用大量金融變數萃取出主要共同性來建構指標，達到簡化資訊的目的，而後者則是利用線性迴歸以符合經濟直覺的方式建構指標。接著利用Granger因果關係檢定、VAR衝擊反應以及樣本外預測表現等，比較兩種FCI對於工業生產指數、失業率、房價指數以及通膨率等與最終目標相關之總體變數間的關聯性，以協助央行快速掌握金融市場的狀況以及評估貨幣政策效果。
實證結果發現，主成分分析法相較於德意志銀行分析法，在Granger因果檢定中對於大部分總體變數具有領先或是反饋關係，除此之外，主成分分析法之FCI對於失業率以及通膨率之樣本外預測也顯著優於德意志銀行分析法之FCI以及AR模型，因此簡化大量資訊之主成分分析法之FCI確實能作為央行中間目標之短期參考指標。

The implementation of monetary policy and the final goal will result in the problem of time lag. The purpose of this article is to provide monetary authorities a short-term indicator as an aid to the intermediate goal. First, we choose monthly data from the foreign exchange market, money market, bond market and stock market in financial sector, then construct financial condition index(FCI) using principal component analysis and Deutsche Bank analysis, the most difference between the two approaches is that the former uses a large number of financial variables to extract the covariation or comovement and achieve the purpose of simplifying information while the latter constructs indicators with a linear regression model but consistent with economic intuition and theoretical expectations. Next, using the Granger causality test, VAR impulse response, and out-of-sample forecast to compare which FCI is more relevant to macroeconomic variables such as industrial production index, unemployment rate, house price index, and inflation rate.
The empirical results show that FCI using principal component analysis has a leading or feedback relationship with most of the macroeconomic variables in the Granger causality compared with Deutsche Bank's analysis. In addition, the principal component analysis method for the unemployment rate and the inflation rate on out-of-sample forecasts outperform Deutsche Bank's analytical method and AR models, so FCI using principal component analysis that simplifies large amounts of information can indeed serve as a short-term indicator for the central bank's intermediate targets.

第一章 緒論 1
第二章 文獻回顧 4
第三章 模型設定 7
第一節 主成分分析法 7
第二節 德意志銀行之分析法 10
第四章 變數選取、處理以及實證結果 11
第一節 變數選取 11
第二節 時間序列變數處理 12
第三節 實證結果 14
1. 金融情勢指數之建構 14
2. 樣本內表現 16
3. 樣本外預測 18
4. 穩健性檢驗 21
第五章 結論 23
參考文獻 25

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