本論文是由三篇關於金融市場的實證研究組合而成。第一篇以權益存續期間為主題，主要是利用迴歸模型估計台灣上市產業指數的實證權益存續期間，以探討股票報酬率的利率敏感度。迴歸模型中控制了三個重要的股票風險因子─市場因子、規模因子與價值因子。但其中，我們改以正交市場因子代替市場因子，以避免因為利率變動與市場報酬間存在共線性，而造成權益存續期間有可能錯估的問題。此外，基於權益存續期間具有會隨時間改變的動態特性，本文亦對各產業指數最近一次結構性變化的發生時點進行偵測，並據以推估最近期的權益存續期間。實證結果顯示：除了鋼鐵業的權益存續期間不顯著之外，其他所有產業指數皆具有負的權益存續期間，表示其報酬率與利率變動呈現出正向關係。在程度上，則以營建類指的利率敏感度最大，汽車類指最小。 第二篇應用了Diebold and Yilmaz (2009)的外溢指標分析台灣上市產業指數間的連動性，其優點是可以瞭解到產業間相互影響的方向以及程度。實證結果顯示：台灣上市產業指數間的外溢程度頗高，並以營建業為最主要的影響者，而相反地，鋼鐵業則是主要的被影響者。外溢指標具有隨時間改變的動態特性，而且透過動態外溢指標可觀察到次貸風暴蔓延的嚴重性。 第三篇應用了Goyal, Perignon and Villa (2008)所提出的多群組因素分析法，檢測美國總人口死亡率的共同因子個數。該方法最大的優點是能夠有效地辨識出真正的共同因子，避免了一般因素分析容易將解釋能力高的群組內獨特因子誤認為共同因子的缺點。根據檢測結果顯示，美國總人口死亡率的共同因子共有兩個，而且第二個因子的重要性隨時間愈來愈明顯。 / This thesis consists of three empirical essays about financial markets. The first essay analyzes the sensitivity of stock returns to changes in interest rates by estimating empirical equity duration of 18 industrial indices in the Taiwan Stock Exchange. In the regression models, we also control for the market excess return and the Fama-French mimicking returns for size and book-to-market factors. To avoid the effects of the multicolinearity between the market excess return and the interest rate changes, we replace the market excess return by the orthogonalized market factor. In addition, considering the time-varying pattern of empirical equity duration, we further adopt the reversed ordered Cusum test proposed by Pesaran and Timmermann (2002) to identify the most recent break of the regression relationship, and then extract the post-break data to re-estimate the up-to-date empirical equity duration. The result shows that except the Steel index, all industrial indices exhibit significantly negative equity durations, indicating a positive relationship between industrial index returns and interest rate changes in Taiwan. Among them, the Construction index has the largest interest rate sensitivity, while the lowest one is for the Automobile index. The second essay focuses on the nature of financial market interdependence, both in terms of returns and returns volatilities. Being capable of identifying the direction and magnitude of linkages among financial markets, the spillover index proposed by Diebold and Yilmaz (2009) is used to measure return and volatility spillovers between the top eight industrial indices based on market value in the Taiwan Stock Exchange. We find that for both returns and volatilities, the spillover effects among industrial indices in Taiwan are substantial. In particular, the Construction index is the major transmitter of shocks to other industries, and the Steel index, in contrast, suffers the most shocks from others. The spillover index fluctuates over time and indeed detects the severity of subprime mortgage crisis. The third essay adopts the multigroup factor analysis proposed by Goyal, Perignon and Villa (2008) to estimate the number of common pervasive factors for annual age-specific mortality for the entire U.S. populations. While the standard principal component analysis easily treats any group-specific factor as pervasive one due to its high contribution to total system variance, this methodology is able to estimate the space spanned by common and group-specific pervasive factors and recognize the true common factors. Empirical result shows that there are only two common pervasive factors governing the death rates in the United States; in particular, the importance of the second factor increases over time.

臺灣的男女死亡率皆呈現逐年遞減的趨勢，自1993年進入高齡化社會後，預計將會在2018年進入高齡社會；人口不斷老化的結果讓社會上不論人民或是如保險公司等年金提供者皆面臨愈來愈嚴重的長壽風險；目前現有文獻提出了許多方式以解決長壽風險，其中多數的方法皆需使用到對未來死亡率之預估。 本研究為了能夠更準確的預估未來死亡率的趨勢，參考了Lee Carter (1992)所提出之模型以及Li and Lee (2005)、Li (2013)提出之共同改善趨勢效果，提出考慮商品與商品間以及商品與整體人口間共同改善趨勢之死亡率模型；本研究利用臺灣之保險公司壽險及年金業務經驗死亡率和Human Mortality Database之臺灣人口資料對模型進行配適，並以MAE、MAPE、RMSE三項指標比較與Lee Carter模型之優劣。 最後，本研究利用所配適之模型進行預測，模擬自然避險之效果，檢視臺灣保險業進行自然避險的可能效益，並對決策者在於決定是否要進行自然避險方面給出建議。 / Taiwan became an aging society in 1993 and is expected to become an aged society in 2018. The progressive decrease in Taiwan mortality since the 20th century for both genders has made longevity risk a serious problem for both people and annuity provider in Taiwan. So far, the literature has discussed about how to deal with longevity risk and came out with several solutions which can be categorize as “industry self-insurance”, “ mortality projection improvement” and “capital market solutions” , most of them are related to the projection of mortality. In order to provide a more precise projection of future mortality trend, this article designs several models which collaborates Lee Carter Model (1992) and the common improvement trend suggested by Li and Lee (2005). Based on our models, the Taiwan insurance industry experience mortality data and the Taiwan population mortality data, we test the performance of our models and make comparison. Lastly, we use the model we find to project future mortality trend and try to make a simulation of natural hedging strategy in Taiwan. The purpose we do this is to test the performance of natural hedging method and give suggestion for the decision-maker when they are considering whether to execute a natural hedging strategy.

對於商業保險公司和政府單位而言，死亡率的改善和未來死亡率的預估一直是一大重要議題。特別是對於退休金相關的社會保險、勞退或是商業年金、壽險等等，如何找尋一個準確的預估模式對未來的死亡率改善情況進行預測，並釐訂合理的保費及提列適當的準備金，是對於一個保險制度能否永續經營的重要因素。過去所使用的配適方法，大多僅以單一群體的過去資料輔助未來的預測，例如 Li and Carter (1992)所提出的 Lee-Carter Model，或是 Bell (1997)使用主成分分析法 (Principal Component Analysis, PCA)等僅針對單一群體本身變數進行分析之方式。然而綜觀全球死亡率改善趨勢，可發現國與國間、組與祖間雖有不同，但仍具備共同的趨勢。因此在考慮未來的死亡率配適方面，應加入組與組間的共同因子 (common factors) 進行考量。 Li and Lee (2005)曾提出 Augmented Lee-Carter Model，即對原本的Lee-Carter Model進行修正，加入共同因素項，並且得到更好的預測效果。 本文則採用考慮共同因子之主成分分析原理建構多重群體死亡率模型，即透過主成分分析法，同時考慮不同群體間的死亡率，並以台灣男性和女性1970年至2010年的死亡率資料，做為兩個子群體進行分析。本文使用之主成分分析法模式，和 Lee-Carter Model (Li and Carter, 1992) 和 Augmented Lee-Carter Model (Li and Lee, 2005)，以MAPE法對個別的預測能力進行分析，並得出採用PCA的模式，在預測男性短年期(5年)內的預估能力屬精確(MAPE 介於10%~20%之間)，然而在長期預估下容易失準，且所有使用的模型，在配適台灣資料時皆發生無法準確預估嬰幼兒期(0~3歲)和老年期(80歲以上)之情形。本文並以所有模型預估之死亡率計算保險公司之準備金與保費提列，並與第五回經驗生命表進行比較。 / For governments and life insurance companies, mortality rates are one of the key factors in determining premiums and reserves. Ignoring or miscalculating mortality rates might have negative influences in pricing. However, most of the mortality models do not consider the common trends between groups. In this article, we try to construct the mortality structure which considering common trends of multi-groups populations with principal component analysis (PCA) method. We choose 9 factors to set up our model and fit with the actual data in Taiwan’s gender mortality. We also compare the Lee-Carter Model (Lee and Carter, 1992) and the augmented Lee-Carter Model (Li and Hardy, 2012) with our common factors PCA model, and we find that the PCA model has the least MAPE than other model in five years forecasting in both genders. After finishing basic analysis, we use the mortality data of Taiwan (1970 to 2010) from human mortality database to construct the life expectancy model. We adopt the same criteria to choose the components we need. We also compare the level premium and reserves by different forecasting mortality rates. All of the models indicate life insurance companies to provide higher reserves and level premium than using the 5th TSO experience mortality rare. We will do following research by using company-specific data to construct unique life expectancy model.