A evolução do aumento da expectativa de vida registrada nas últimas décadas foi uma conquista significativa para a sociedade e trouxe novos desafios em diversas áreas do conhecimento humano. Dentre os impactos do aumento da longevidade, destaca-se sua influência no equilíbrio técnico dos planos previdenciários. Nas entidades de previdência complementar, a identificação oportuna de possíveis desvios da premissa da mortalidade à realidade subjacente visa garantir a solvência e a manutenção dos benefícios de longo prazo. Assim, o presente estudo tem por objetivo estimar os fatores de improvement (fator redutor de mortalidade) para a população coberta por planos privados de aposentadoria, com base no método Lee-Carter e na abordagem CMI (Continuous Mortality Investigation), bem como analisar o impacto da incorporação da estimativa do aumento da expectativa de vida no fluxo de caixa atuarial em uma carteira de benefício definido. Em virtude da carência de informações históricas de tábuas de mortalidade para o Brasil, fez-se uso da técnica de pareamento (propensity score), o qual consiste na identificação do país que mais se assemelha ao Brasil no que se refere às variáveis socioeconômicas relevantes para prever a evolução da expectativa de vida. Essa técnica foi aplicada para uma amostra de 21 países da OCDE. As variáveis socioeconômicas consideradas no estudo foram: Fertilidade, PIB per capita, Crescimento anual do PIB, Saúde, Desemprego, Gini, Analfabetismo e Escolaridade. Diante dos testes efetuados, Portugal foi escolhido para servir de base para as projeções da mortalidade e obtenção dos fatores de improvement, em decorrência da técnica de pareamento e do teste de aderência realizado. Comparando-se as médias dos fluxos de caixa da AT-2000 com e sem improvement e levando-se em consideração os cenários de taxas de juros de 3%, 4%, 5% e 6% ao ano, observou-se que, não considerar o improvement, gera uma elevação do fluxo atuarial entre 7,15% a 10,51% para a carteira simulada. A projeção pelo método CMI forneceu resultado semelhante, sendo que o impacto variou entre 7,05% a 10,32%. Embora os métodos de improvement sejam bem diferentes, é importante destacar que os resultados foram bem semelhantes. Um ponto que merece preocupação é a questão da taxa de juros, pois com a tendência de queda, no longo prazo, maior será a sensibilidade do impacto da projeção do risco de longevidade. Adicionalmente, compararam-se os resultados obtidos com a Tábua Geracional RP-2000 e a Tábua SUSEP BR-EMS. Assim, os resultados anteriores mostram que não considerar a tendência de aumento da expectativa de vida na constituição das provisões técnicas pode expor as entidades de previdência a riscos pouco suportáveis no longo prazo. / The evolution of increased life expectancy recorded in recent decades has been a significant achievement for the society and brought new challenges in various areas of human knowledge. Among those, living longer has impacted the technical balance of the pension plans. In the private pension entities, the timely identification of possible deviations from the assumption of mortality to the underlying reality is to ensure the solvency and the maintenance of long-term benefits. Thus, based on Lee-Carter method and approach CMI (Continuous Mortality Investigation Bureau), this study aims to estimate the factors of improvement (reduction factor of mortality) for the population covered by pension plans as well as analyze the impact of incorporating an estimated longer life expectancy on actuarial cash flow into a portfolio of defined benefits. Due to a lack of historical information about mortality tables of Brazil, the matching technique (propensity score) was used to identify the country which is the most similar to Brazil concerning relevant socioeconomic variables, in order to predict the evolution of life expectancy. This technique was applied on 21 OECD sample countries. Socioeconomic variables considered were: Fertility, GDP per capita, annual growth of GDP, Health, Unemployment, Gini, Illiteracy and Schooling. According to test results, Portugal was chosen as the basis for projections of mortality and acquisition of factors of improvement, due to the matching technique and the adherence test performed. Comparing the averages of the cash flows of the AT-2000 with and without improvement and taking into account the scenarios of interest rates of 3%, 4%, 5% and 6% a year, it was observed that not considering the improvement generates an increased actuarial flow between 7.15% and 10.51% for the simulated portfolio. The CMI method provided similar projection, and the impact varied from 7.05% to 10.32%. Even though the methods of improvement are quite different, it is important to emphasize that the results were much the same. One point that deserves concern is the issue of interest rate since, due to the declining trend in the long run more sensitive will be the impact of the projection of longevity risk. Additionally, those results were compared with the table Generational RP-2000 and BRTable SUSEP EMS. Thus, previous results show that not considering the trend of increasing life expectancy in the establishment of technical provisions can expose the private pension entities to a little bearable risk in the long term.

A evolução do aumento da expectativa de vida registrada nas últimas décadas foi uma conquista significativa para a sociedade e trouxe novos desafios em diversas áreas do conhecimento humano. Dentre os impactos do aumento da longevidade, destaca-se sua influência no equilíbrio técnico dos planos previdenciários. Nas entidades de previdência complementar, a identificação oportuna de possíveis desvios da premissa da mortalidade à realidade subjacente visa garantir a solvência e a manutenção dos benefícios de longo prazo. Assim, o presente estudo tem por objetivo estimar os fatores de improvement (fator redutor de mortalidade) para a população coberta por planos privados de aposentadoria, com base no método Lee-Carter e na abordagem CMI (Continuous Mortality Investigation), bem como analisar o impacto da incorporação da estimativa do aumento da expectativa de vida no fluxo de caixa atuarial em uma carteira de benefício definido. Em virtude da carência de informações históricas de tábuas de mortalidade para o Brasil, fez-se uso da técnica de pareamento (propensity score), o qual consiste na identificação do país que mais se assemelha ao Brasil no que se refere às variáveis socioeconômicas relevantes para prever a evolução da expectativa de vida. Essa técnica foi aplicada para uma amostra de 21 países da OCDE. As variáveis socioeconômicas consideradas no estudo foram: Fertilidade, PIB per capita, Crescimento anual do PIB, Saúde, Desemprego, Gini, Analfabetismo e Escolaridade. Diante dos testes efetuados, Portugal foi escolhido para servir de base para as projeções da mortalidade e obtenção dos fatores de improvement, em decorrência da técnica de pareamento e do teste de aderência realizado. Comparando-se as médias dos fluxos de caixa da AT-2000 com e sem improvement e levando-se em consideração os cenários de taxas de juros de 3%, 4%, 5% e 6% ao ano, observou-se que, não considerar o improvement, gera uma elevação do fluxo atuarial entre 7,15% a 10,51% para a carteira simulada. A projeção pelo método CMI forneceu resultado semelhante, sendo que o impacto variou entre 7,05% a 10,32%. Embora os métodos de improvement sejam bem diferentes, é importante destacar que os resultados foram bem semelhantes. Um ponto que merece preocupação é a questão da taxa de juros, pois com a tendência de queda, no longo prazo, maior será a sensibilidade do impacto da projeção do risco de longevidade. Adicionalmente, compararam-se os resultados obtidos com a Tábua Geracional RP-2000 e a Tábua SUSEP BR-EMS. Assim, os resultados anteriores mostram que não considerar a tendência de aumento da expectativa de vida na constituição das provisões técnicas pode expor as entidades de previdência a riscos pouco suportáveis no longo prazo. / The evolution of increased life expectancy recorded in recent decades has been a significant achievement for the society and brought new challenges in various areas of human knowledge. Among those, living longer has impacted the technical balance of the pension plans. In the private pension entities, the timely identification of possible deviations from the assumption of mortality to the underlying reality is to ensure the solvency and the maintenance of long-term benefits. Thus, based on Lee-Carter method and approach CMI (Continuous Mortality Investigation Bureau), this study aims to estimate the factors of improvement (reduction factor of mortality) for the population covered by pension plans as well as analyze the impact of incorporating an estimated longer life expectancy on actuarial cash flow into a portfolio of defined benefits. Due to a lack of historical information about mortality tables of Brazil, the matching technique (propensity score) was used to identify the country which is the most similar to Brazil concerning relevant socioeconomic variables, in order to predict the evolution of life expectancy. This technique was applied on 21 OECD sample countries. Socioeconomic variables considered were: Fertility, GDP per capita, annual growth of GDP, Health, Unemployment, Gini, Illiteracy and Schooling. According to test results, Portugal was chosen as the basis for projections of mortality and acquisition of factors of improvement, due to the matching technique and the adherence test performed. Comparing the averages of the cash flows of the AT-2000 with and without improvement and taking into account the scenarios of interest rates of 3%, 4%, 5% and 6% a year, it was observed that not considering the improvement generates an increased actuarial flow between 7.15% and 10.51% for the simulated portfolio. The CMI method provided similar projection, and the impact varied from 7.05% to 10.32%. Even though the methods of improvement are quite different, it is important to emphasize that the results were much the same. One point that deserves concern is the issue of interest rate since, due to the declining trend in the long run more sensitive will be the impact of the projection of longevity risk. Additionally, those results were compared with the table Generational RP-2000 and BRTable SUSEP EMS. Thus, previous results show that not considering the trend of increasing life expectancy in the establishment of technical provisions can expose the private pension entities to a little bearable risk in the long term.

In Taiwan area, the mortality rates at all ages have decreased since the end of World War II, and the life expectancy of people has increased from 62 in 1950's to 75 in 2000, which is an increase of 21%. The mortality improvement of the elderly (i.e. people ages 65 and over) is especially significant, which effects in the rapid population aging in Taiwan area. For example, the proportion of the elderly has increased from 6.14%in 1990 to 8.52% in 2000. On one hand, the prolonged life span for an individual means a longer period of retirement life and thus a larger retirement fund. On the other hand, a longer life for the government is equivalent to a more thorough social system for the elderly. Therefore, a reliable mortality rates projection is essential to both personal financial and social welfare planning. 　　In this study, we have two main objectives: First, we explore some frequent used models, such as Lee-Carter, multivariate regression and principal component methods. We use the data between 1950 to 1995 as the pilot data and 1996 to 2000 as the test data to judge which method has the smallest prediction error. In addition, based on computer simulation, we also evaluate the performance of the estimation methods for the Lee-Carter method. The second part (and the other objective) of this study is to explore the effect of mortality improvement on the pure premium of annuity insurance. In particular, we calculate the pure premium of the annuity under the best model acquired from the first part, and compare those under 1989 TSO and other life tables. We found that the pure premiums under current life tables are under estimated, which may cause the insolvency of insurance companies.

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.

傳統上國人寄望養兒防老，但面臨少子化及壽命延長，家庭已無法獨力負擔照顧老年人的責任，必須仰賴個人（老年人自己）、國家及政府分擔人口老化造成的需求，這也是政府在過去二十年來積極投入更多資源，制訂與老年人有關的社會保險、福利及政策的原因。像是1995年開辦的全民健康保險提升了全民健康，其中老年人受惠尤多；2005年的勞工退休金條例、2008年的國民年金保險等，則是因應我國國民壽命延長的社會保險制度。對於未來費用的需求估算，需要依賴可靠的死亡率預測，但大多數預測並沒有將死亡率改善列入考量，勢必低估長壽風險的衝擊，影響個人的財務規劃、增加國家負債。 有鑑於此，本文研究常用的死亡率模型，評估哪些適合用於描述高齡死亡率的變化，且能用於計算年金商品的定價。本文考量的模型大致分成兩類：關係模型(Relational Models)及隨機模型(Stochastic Models)，第一類包括常用於高齡的Gompertz、Coale-Kisker模型，以及Discount Sequence模型，第二類則有Lee-Carter及CBD等模型。模型比較的方式以長期預測和短期預測，選用交叉驗證的方式驗證死亡率模型的預測結果與觀察值之間的差異。研究結果顯示Discount Sequence、Lee-Carter、CBD隨機模型較能準確描述台灣、日本與美國等三個國家的死亡率特性；但這三個模型在年金險保費並沒有很明顯的訂價差異。另外，若用於短期預測、長期預測比較，又以Discount Sequence的預測結果優於Lee-Carter模型的預測。 / Traditionally in Asia, families played the main role in caring their own elderly (i.e., parents and grand-parents), but the declining fertility rates and longer life expectancy make it difficult for the families to take care of the elderly alone. The elderly themselves and the government need to share the burden caused by the aging population. In fact, most Taiwan’s major social policies in the past 20 years are targeting the elderly, such as National Health Insurance, Labor Pension Act and National Pension Insurance. Their planning and financial solvency rely on reliable mortality models and their projections for the elderly population. However, many mortality models do not take into account the mortality improvements and thus underestimate the cost. In this study, we look for elderly mortality models which can reflect the mortality improvements in recent years and use them to price the annuity products. Two types of mortality models are of interest: relational models and stochastic models. The first group includes the Gompertz model, Coale-Kisker model and Discount Sequence; the other group includes the Lee-Carter and CBD models. We utilize these mortality models to project future mortality rates in Taiwan, Japan and U.S., along with the block bootstrap and ARIMA for projection. The model comparison is based on cross-validation, and both short-term and long-term projections are considered. The results show that the Discount Sequence, Lee-Carter model and CBD model have the best model fits for mortality rates and, for the short-term and long-term forecasts, the Discount Sequence is better than the Lee-Carter model.

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.