學術產出-學位論文
文章檢視/開啟
書目匯出
-
題名 台灣壽險業國外投資與績效之長期追蹤分析
The longitudinal approach to analyzing the foreign investment and performance for the life insurance industry in Taiwan作者 黃全利 貢獻者 鄭宗記
黃全利關鍵詞 長期資料
長期資料分量迴歸
國外投資
投資績效
longitudinal data
quantile regression for longitudinal data
foreign investment
investment performance日期 2011 上傳時間 30-十月-2012 10:58:22 (UTC+8) 摘要 自2003年起隨著台灣壽險業國外投資比率不斷提高，至2010年底國外投資比率已達34.47%，因此為了探討壽險業國外投資與績效並了解相關因素之影響，本研究檢視壽險公司之市占率和各險種保費收入比率與國外投資比率之間的關係，同時亦檢視美國政府十年期公債殖利率與投資報酬率之間是否具有正向關係。另一方面，探討已公開發行公司是否因需揭露財務報表而與未公開發行公司之間在國外投資比率和投資績效上有所差異。 本文以2004年至2008年台灣25家壽險公司的長期資料（longitudinal data），分析總合（pooled）、固定效果(fixed effects)和隨機效果（random effects）迴歸模型，並檢視模型之適合性檢定。另因反應變數之密度估計具長尾之特性，所以亦使用Koenker（2004）和Geraci and Bottai（2007）提出的長期資料分量迴歸（quantile regression for longitudinal data）分析作為探討。實證結果顯示，若壽險公司的市占率愈高，則其資產配置於國外的比重亦相對地提高，且壽險和年金險比率與國外投資比率之間呈現顯著地正相關；此外，公開發行公司的國外投資比率顯著高於未公開發行公司。在投資績效方面，美國政府十年期公債殖利率與投資報酬率之間為顯著的正相關。 長期資料分量迴歸分析實證結果顯示，當使用Koenker（2004）提出之方法時，則一般（ordinary）分量迴歸在50%、75%和90%條件分量下，隨著樣本期間年度的增加，壽險業的國外投資報酬率相對地上升；在10% 和25% 條件分量下，壽險公司市占率與國外投資報酬率之間是顯著的正相關。而使用Geraci and Bottai（2007）提出之隨機效果分量迴歸方法時，在50%條件分量下，國外投資比率與國外投資報酬率之間為顯著地正相關，再者匯率風險將降低台灣壽險業國外投資的意願，然而實行避險策略是有益於投資績效的提升。
The foreign investment ratio for the life insurance industry in Taiwan has risen constantly since 2003 and reached 34.47% in 2010. In order to explore foreign investment and performance, and understand the impact of relevant factors in the life insurance industry, this study examines the relationship between the market shares of life insurance companies, types of premium income ratio and the foreign investment ratio. Simultaneously, this study also examines the relationship between the 10-year US Treasury Bond Yield Currency and investment return.On the other hand, we explore whether the difference between the publicly traded companies and non-publicly traded companies on the foreign investment ratio and the investment performance. In this dissertation, we analyze 25 Taiwanese life insurance companies between 2004 and 2008 using the pooled, fixed effects and random effects regression model. Due to the distribution of the response variable is characterized by the long tail, we explore the use of the quantile regression for longitudinal data by Koenker(2004)and Geraci and Bottai(2007). The empirical results show that the more market share of life insurance companies, the higher foreign investment ratio and there is significantly positive correlation between the life insurance, annuity ratio and the foreign investment ratio. In addition, the publicly traded company`s foreign investment ratio is significantly higher than non-publicly traded company. In terms of investment performance, it’s significantly positive correlation between the U.S. 10-year Treasury Bond Yield Currency and return on investment. The empirical results about quantile regression for longitudinal data show that the return on foreign investment relatively enhance for the life insurance industry with the increase of the year during the sample period under the 50%,75% and 90% conditional qauntile when using the ordinary quantile regression proposed by Koenker(2004). There is significantly positive correlation between the market share and the return on foreign investment under the 10% and 25% conditional qauntile. When using the method proposed by Geraci and Bottai(2007), there is significantly positive correlation between the foreign investment ratio and the return on foreign investment under the 50% conditional qauntile. Furthermore, exchange rate risk will reduce the foreign investment willingness of the life insurance industry in Taiwan. However, the implementation of the hedging strategy is beneficial to enhance investment performance for the life insurance industry.參考文獻 林金樹（2004），「壽險業資金運用效率與國外投資額度關係之研究」，國立政治大學風險管理與保險研究所碩士論文。張士傑、朱浩民、許素珠與黃雅文（2010），「資產配置之迷思或現實？台灣壽險業之實證研究」，風險管理學報，第十二卷第一期，5-32。張士傑（2010），「十字路口的抉擇：國際接軌或是分道揚鑣」，壽險季刊，第155期，1-10。賴本隊（2010），「壽險業「外匯價格變動準備金」評析」，壽險季刊，第155期，11-26。Abrevaya, J., and C.M. Dahl (2008).“The Effects of Birth Inputs on Birthweight：Evidence From Quantile Estimation on Panel Data”,Journal of Business and Economic Statistics, Vol. 26, No. 4, 379-397.Allayannis, G., and J.P. Weston (2001).“The Use of Foreign Currency Derivatives and Firm Market Value”,The Review of FinancialStudies, Vol. 14,No.1, 243-276.Baltagi, B.H. (2005). Econometric Analysis of Panel Data.John Wiley and Sons, Ltd.Breusch, T. S., and A. R. Pagan (1980).“The Lagrange Multiplier Test and its Applications to Model Specification in Econometrics”, The Review of Economic Studies, Vol. 47,No.1, 239-253.Campbell, J.Y., K. Serfaty-de Medeiros, and L.M. Viceira (2010).“Global Currency Hedging”,Journal of Finance, Vol. 65,No.1, 87-121.Chamberlain, G. (1982). “Multivariate Regression Models for Panel Data”, Journal of Econometrics, Vol. 18, 5-46.Chamberlain, G. (1984). “ Panel Data. In Z. Griliches and M. D. Intriligator (Eds.)”, Handbook of Econometrics, Vol. 2, 1247-1318.New York：Academic Press.Chang,S.C.,H.M.Chu, and S.C.Hsu (2011). “Striving for Home Advantages?An Empirical Study of Currency Hedging of Taiwan’s Life Insurers”,Middle Eastern Finance and Economics, Issue 12, 49-64.Chen, C.T.,and C.T.Liang (2010).“An Empirical Study on the Basket Hedge”,Journal of Risk Management, Vol.12,No.1, 133-153.Cummins, J.D., R.D. Phillips, and S.D. Smith (1997).“Corporate Hedging in the Insurance Industry：The Use of Financial Derivatives by U.S. Insurers”, The North American Actuarial Journal, Vol. 1, 13-49.Diggle, P.J., P.J. Heagerty, K.Y. Liang, and S.L. Zeger (2002).Analysis of Longitudinal Data. Oxford：Oxford University Press.Geraci, M. and M. Bottai (2007).“Quantile regression for longitudinal data using the asymmetric Laplace distribution”,Biostatistics, Vol. 8, No. 1, 140-154Glen, J. and P. Jorion (1993).“Currency Hedging for International Portfolios”,Journal of Finance, Vol. 48, No. 5, 1865-1886.Goetzmann, W.N., L. Li, and K.G. Rouwenhorst (2005).“Long-Term Global Market Correlations”,Journal of Business, Vol. 78, No. 1, 1-38.Graybill, F.A. (1969).Introduction to Matrices with Applications in Statistics. Belmont, CA：Wadsworth.Hausman,J. A. (1978). “Specification Tests in Econometrics”, Econometrica, Vol. 46, No. 6, 1251-1271.Hsiao, C. (2003). Analysis of Panel Data. Cambridge：Cambridge University Press.Klevmarken, N.A. (1989). “Panel studies：What can we learn from them？Introduction”, European Economic Review, Vol. 33, Issues 2-3, 523-529.Koenker, R., and G. Bassett (1978).“Regression Quantiles”, Econometrica, Vol. 46,No.1, 33-50.Koenker, R. (2004). “Quantile Regression for Longitudinal Data”, Journal of Multivariate Analysis, Vol. 91, Issues 1, 74-89.Koenker, R. (2005).Quantile Regression. New York：Cambridge University Press.Maddala, G.S. (1971). “The Use of Variance Components Models in Pooling Cross Section and Time Series Data”,Econometrica, Vol. 39, 341-358.Nerlove, M. (1971).“A Note on Error Components Models”,Econometrica, Vol. 39, 383-396.Odier, P.,and B. Solnik, (1993).“Lessons from International Asset Allocation”, Financial Analysts Journal, Vol. 49,No.2, 63-77.Pinheiro, J.C.,and D.J. Bates (2000).Mixed-Effects Models in S and S-PLUS. New York：Springer.Solnik, B.H. (1995). “Why not Diversify Internationally rather than Domestically?”,Financial Analysts Journal, Vol. 51,No.1, 89-94.Shi P., and E.W. Frees (2010). “Long-tail longitudinal modeling of insurance company expenses”, Insurance：Mathematics and Economics, Vol. 47, 303-314.Ware, J.H., D. Dockery, T.A. Louis, et al. (1990). “Longitudinal and cross-sectional estimates of pulmonary function decline in never-smoking adults”,AmericanJournal of Epidemiology, Vol. 132,Issue 4,685-700.Wallace, T.D., and A. Hussain (1969).“The Use of Error Components Models in Combining Cross-Section with Time Series Data”,Econometrica, Vol. 37, 55-72. 描述 碩士
國立政治大學
統計研究所
99354020
100資料來源 http://thesis.lib.nccu.edu.tw/record/#G0099354020 資料類型 thesis dc.contributor.advisor 鄭宗記 zh_TW dc.contributor.author (作者) 黃全利 zh_TW dc.creator (作者) 黃全利 zh_TW dc.date (日期) 2011 en_US dc.date.accessioned 30-十月-2012 10:58:22 (UTC+8) - dc.date.available 30-十月-2012 10:58:22 (UTC+8) - dc.date.issued (上傳時間) 30-十月-2012 10:58:22 (UTC+8) - dc.identifier (其他 識別碼) G0099354020 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/54411 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 統計研究所 zh_TW dc.description (描述) 99354020 zh_TW dc.description (描述) 100 zh_TW dc.description.abstract (摘要) 自2003年起隨著台灣壽險業國外投資比率不斷提高，至2010年底國外投資比率已達34.47%，因此為了探討壽險業國外投資與績效並了解相關因素之影響，本研究檢視壽險公司之市占率和各險種保費收入比率與國外投資比率之間的關係，同時亦檢視美國政府十年期公債殖利率與投資報酬率之間是否具有正向關係。另一方面，探討已公開發行公司是否因需揭露財務報表而與未公開發行公司之間在國外投資比率和投資績效上有所差異。 本文以2004年至2008年台灣25家壽險公司的長期資料（longitudinal data），分析總合（pooled）、固定效果(fixed effects)和隨機效果（random effects）迴歸模型，並檢視模型之適合性檢定。另因反應變數之密度估計具長尾之特性，所以亦使用Koenker（2004）和Geraci and Bottai（2007）提出的長期資料分量迴歸（quantile regression for longitudinal data）分析作為探討。實證結果顯示，若壽險公司的市占率愈高，則其資產配置於國外的比重亦相對地提高，且壽險和年金險比率與國外投資比率之間呈現顯著地正相關；此外，公開發行公司的國外投資比率顯著高於未公開發行公司。在投資績效方面，美國政府十年期公債殖利率與投資報酬率之間為顯著的正相關。 長期資料分量迴歸分析實證結果顯示，當使用Koenker（2004）提出之方法時，則一般（ordinary）分量迴歸在50%、75%和90%條件分量下，隨著樣本期間年度的增加，壽險業的國外投資報酬率相對地上升；在10% 和25% 條件分量下，壽險公司市占率與國外投資報酬率之間是顯著的正相關。而使用Geraci and Bottai（2007）提出之隨機效果分量迴歸方法時，在50%條件分量下，國外投資比率與國外投資報酬率之間為顯著地正相關，再者匯率風險將降低台灣壽險業國外投資的意願，然而實行避險策略是有益於投資績效的提升。 zh_TW dc.description.abstract (摘要) The foreign investment ratio for the life insurance industry in Taiwan has risen constantly since 2003 and reached 34.47% in 2010. In order to explore foreign investment and performance, and understand the impact of relevant factors in the life insurance industry, this study examines the relationship between the market shares of life insurance companies, types of premium income ratio and the foreign investment ratio. Simultaneously, this study also examines the relationship between the 10-year US Treasury Bond Yield Currency and investment return.On the other hand, we explore whether the difference between the publicly traded companies and non-publicly traded companies on the foreign investment ratio and the investment performance. In this dissertation, we analyze 25 Taiwanese life insurance companies between 2004 and 2008 using the pooled, fixed effects and random effects regression model. Due to the distribution of the response variable is characterized by the long tail, we explore the use of the quantile regression for longitudinal data by Koenker(2004)and Geraci and Bottai(2007). The empirical results show that the more market share of life insurance companies, the higher foreign investment ratio and there is significantly positive correlation between the life insurance, annuity ratio and the foreign investment ratio. In addition, the publicly traded company`s foreign investment ratio is significantly higher than non-publicly traded company. In terms of investment performance, it’s significantly positive correlation between the U.S. 10-year Treasury Bond Yield Currency and return on investment. The empirical results about quantile regression for longitudinal data show that the return on foreign investment relatively enhance for the life insurance industry with the increase of the year during the sample period under the 50%,75% and 90% conditional qauntile when using the ordinary quantile regression proposed by Koenker(2004). There is significantly positive correlation between the market share and the return on foreign investment under the 10% and 25% conditional qauntile. When using the method proposed by Geraci and Bottai(2007), there is significantly positive correlation between the foreign investment ratio and the return on foreign investment under the 50% conditional qauntile. Furthermore, exchange rate risk will reduce the foreign investment willingness of the life insurance industry in Taiwan. However, the implementation of the hedging strategy is beneficial to enhance investment performance for the life insurance industry. en_US dc.description.tableofcontents 目錄.................................................... Ⅰ表目錄.................................................. Ⅱ圖目錄.................................................. Ⅴ第一章 緒論...............................................1第二章 文獻回顧...........................................6第一節 長期資料分析........................................6壹、 長期資料分析之特點...................................6貳、 長期資料迴歸模型....................................10參、 模型檢定方法.......................................17第二節 長期資料分量迴歸分析................................20第三章 實證分析..........................................25第一節 資料與變數.........................................25壹、 資料來源...........................................25貳、 變數定義...........................................26參、 變數敘述統計量及相關性分析...........................28肆、 變數對觀察年度之分析................................32第二節 實證模型...........................................39第三節 實證結果...........................................44壹、 長期資料分析結果與討論..............................44貳、 長期資料分量迴歸分析結果.............................91第四章 結論.............................................108參考文獻..................................................111附錄.....................................................113 zh_TW dc.language.iso en_US - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0099354020 en_US dc.subject (關鍵詞) 長期資料 zh_TW dc.subject (關鍵詞) 長期資料分量迴歸 zh_TW dc.subject (關鍵詞) 國外投資 zh_TW dc.subject (關鍵詞) 投資績效 zh_TW dc.subject (關鍵詞) longitudinal data en_US dc.subject (關鍵詞) quantile regression for longitudinal data en_US dc.subject (關鍵詞) foreign investment en_US dc.subject (關鍵詞) investment performance en_US dc.title (題名) 台灣壽險業國外投資與績效之長期追蹤分析 zh_TW dc.title (題名) The longitudinal approach to analyzing the foreign investment and performance for the life insurance industry in Taiwan en_US dc.type (資料類型) thesis en dc.relation.reference (參考文獻) 林金樹（2004），「壽險業資金運用效率與國外投資額度關係之研究」，國立政治大學風險管理與保險研究所碩士論文。張士傑、朱浩民、許素珠與黃雅文（2010），「資產配置之迷思或現實？台灣壽險業之實證研究」，風險管理學報，第十二卷第一期，5-32。張士傑（2010），「十字路口的抉擇：國際接軌或是分道揚鑣」，壽險季刊，第155期，1-10。賴本隊（2010），「壽險業「外匯價格變動準備金」評析」，壽險季刊，第155期，11-26。Abrevaya, J., and C.M. Dahl (2008).“The Effects of Birth Inputs on Birthweight：Evidence From Quantile Estimation on Panel Data”,Journal of Business and Economic Statistics, Vol. 26, No. 4, 379-397.Allayannis, G., and J.P. Weston (2001).“The Use of Foreign Currency Derivatives and Firm Market Value”,The Review of FinancialStudies, Vol. 14,No.1, 243-276.Baltagi, B.H. (2005). Econometric Analysis of Panel Data.John Wiley and Sons, Ltd.Breusch, T. S., and A. R. Pagan (1980).“The Lagrange Multiplier Test and its Applications to Model Specification in Econometrics”, The Review of Economic Studies, Vol. 47,No.1, 239-253.Campbell, J.Y., K. Serfaty-de Medeiros, and L.M. Viceira (2010).“Global Currency Hedging”,Journal of Finance, Vol. 65,No.1, 87-121.Chamberlain, G. (1982). “Multivariate Regression Models for Panel Data”, Journal of Econometrics, Vol. 18, 5-46.Chamberlain, G. (1984). “ Panel Data. In Z. Griliches and M. D. Intriligator (Eds.)”, Handbook of Econometrics, Vol. 2, 1247-1318.New York：Academic Press.Chang,S.C.,H.M.Chu, and S.C.Hsu (2011). “Striving for Home Advantages?An Empirical Study of Currency Hedging of Taiwan’s Life Insurers”,Middle Eastern Finance and Economics, Issue 12, 49-64.Chen, C.T.,and C.T.Liang (2010).“An Empirical Study on the Basket Hedge”,Journal of Risk Management, Vol.12,No.1, 133-153.Cummins, J.D., R.D. Phillips, and S.D. Smith (1997).“Corporate Hedging in the Insurance Industry：The Use of Financial Derivatives by U.S. Insurers”, The North American Actuarial Journal, Vol. 1, 13-49.Diggle, P.J., P.J. Heagerty, K.Y. Liang, and S.L. Zeger (2002).Analysis of Longitudinal Data. Oxford：Oxford University Press.Geraci, M. and M. Bottai (2007).“Quantile regression for longitudinal data using the asymmetric Laplace distribution”,Biostatistics, Vol. 8, No. 1, 140-154Glen, J. and P. Jorion (1993).“Currency Hedging for International Portfolios”,Journal of Finance, Vol. 48, No. 5, 1865-1886.Goetzmann, W.N., L. Li, and K.G. Rouwenhorst (2005).“Long-Term Global Market Correlations”,Journal of Business, Vol. 78, No. 1, 1-38.Graybill, F.A. (1969).Introduction to Matrices with Applications in Statistics. Belmont, CA：Wadsworth.Hausman,J. A. (1978). “Specification Tests in Econometrics”, Econometrica, Vol. 46, No. 6, 1251-1271.Hsiao, C. (2003). Analysis of Panel Data. Cambridge：Cambridge University Press.Klevmarken, N.A. (1989). “Panel studies：What can we learn from them？Introduction”, European Economic Review, Vol. 33, Issues 2-3, 523-529.Koenker, R., and G. Bassett (1978).“Regression Quantiles”, Econometrica, Vol. 46,No.1, 33-50.Koenker, R. (2004). “Quantile Regression for Longitudinal Data”, Journal of Multivariate Analysis, Vol. 91, Issues 1, 74-89.Koenker, R. (2005).Quantile Regression. New York：Cambridge University Press.Maddala, G.S. (1971). “The Use of Variance Components Models in Pooling Cross Section and Time Series Data”,Econometrica, Vol. 39, 341-358.Nerlove, M. (1971).“A Note on Error Components Models”,Econometrica, Vol. 39, 383-396.Odier, P.,and B. Solnik, (1993).“Lessons from International Asset Allocation”, Financial Analysts Journal, Vol. 49,No.2, 63-77.Pinheiro, J.C.,and D.J. Bates (2000).Mixed-Effects Models in S and S-PLUS. New York：Springer.Solnik, B.H. (1995). “Why not Diversify Internationally rather than Domestically?”,Financial Analysts Journal, Vol. 51,No.1, 89-94.Shi P., and E.W. Frees (2010). “Long-tail longitudinal modeling of insurance company expenses”, Insurance：Mathematics and Economics, Vol. 47, 303-314.Ware, J.H., D. Dockery, T.A. Louis, et al. (1990). “Longitudinal and cross-sectional estimates of pulmonary function decline in never-smoking adults”,AmericanJournal of Epidemiology, Vol. 132,Issue 4,685-700.Wallace, T.D., and A. Hussain (1969).“The Use of Error Components Models in Combining Cross-Section with Time Series Data”,Econometrica, Vol. 37, 55-72. zh_TW
