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  1. NTU Theses and Dissertations Repository
  2. 管理學院
  3. 財務金融學系
請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/42995
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dc.contributor.advisor葉小蓁(Hsiaw-Chan Yeh),曾郁仁(Yu-Ren Tzeng)
dc.contributor.authorHsi-Ling Yangen
dc.contributor.author楊錫霖zh_TW
dc.date.accessioned2021-06-15T01:32:01Z-
dc.date.available2010-07-24
dc.date.copyright2009-07-24
dc.date.issued2009
dc.date.submitted2009-07-20
dc.identifier.citationBooth, G. G., Kaen, F. R., and Koveos, P. E. (1982), R/S analysis of foreign exchange rates under two international monetary regimes, Journal of Monetary Economics, 10, 407-415.
Box, G. E. P. and Jenkins, G. M. (2008), Time Series Analysis, Forecasting and Control 4th edition, San Francisco: Holden-Day.
Duan, J. C. and Jacobs, K. (1996), A simple long-memory equilibrium interest rate model, Economics Letters, 53, 3, 317-321.
Geweke, J. and Porter-Hudak, S. (1983), The estimation and application of long-memory time series models, Journal of Time Series Analysis, 4, 221-238.
Greene, M. T. and Fielitz, B. D. (1977), Long-term dependence in common stock returns, Journal of Financial Economics, 5, 339-349.
Gil-Alana, L. A. (2003), A fractional multivariate long memory model for the US and the Canadian real output, Economics Letters, 81, 355-359.
Gil-Alana, L. A. (2004), A fractional integrated model for the Spanish real GDP, Economics Bulletin, 3, No.8, 1-6.
Granger, C. W. J. and Joyeux, R. (1980), An introduction to long-memory time series models and fractional differencing, Journal of Time Series Analysis, 1, 15-29.
Helms, B. P., Kaen, F. R., and Rosenman, R. E. (1984), Memory in commodity futures contracts, Journal of Futures Markets, 4, 559-567.
Hosking, J. R. M. (1981), Fractional differencing, Biometrika, 68, 165-176.
Lo, A. W. (1991), Long-term memory in stock market prices, Econometrica, 59, 1279-1313.
Rao, C. R. (1973), Linear Statistical Inference and its Applications, New York: John Wiley.
Robinson, P. M. (1994), Efficient tests of nonstationary hypotheses, Journal of the American Statistical Association, 89, 1420-1437.
Sowell, F. (1992), Maximum likelihood estimation of stationary univariate fractionally integrated time series models, Journal of Econometrics, 53, 165-188.
Whittle, P. (1953), Estimation and information in stationary time series, Arkiv för Matematik, 2, 423-434.
dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/42995-
dc.description.abstract近年來,長記憶性(Long-memory property)在經濟與財務金融領域受到高度的關注,也因此在長記憶性的研究發展中,許多經濟與財務金融的時間序列均被視為研究對象,然而,保險業相當關注的風險之一—人類死亡率卻尚未被考慮。本篇研究以探討人類死亡率是否具有長記憶性為出發點,利用ARFIMA模型、Whittle概似函數與Robinson檢定統計量研究美國1933年至2005年中25歲至85歲的人類死亡率,發現男性和女性隨年齡增加所造成的死亡率成長具有長記憶性,且若是以傳統的ARIMA模型進行評估,將會造成商品評價與風險控管上的誤判。此外,在研究的過程中亦發現女性隨年齡增加所造成之死亡率成長的平均成長速率有逐年上升的趨勢,相對的,男性卻維持在一固定水準,此現象與女性平均餘命逐年增加的現象並不違背,主要原因為女性25歲的死亡率下降速度較男性為快。zh_TW
dc.description.abstractIn recent years, long-memory property has gained considerable attention in economic and financial fields. However, although many economic and financial time series have been studied, human mortality, which plays an important role in insurance has not been considered. In this thesis we will analyze the human mortality of 25-year-old to 85-year-old females and males in the U.S. to find out whether it is a long-memory process. After building the ARFIMA model and considering the Whittle likelihood function along with the Robinson test statistic, we found out that both females’ and males’ annual mortality growth rates are indeed long-memory processes. Additionally, if the ARFIMA model is to be replaced by the ARIMA model it would result in misunderstandings in product pricing and risk control. In our thesis, we also found out that females’ mean of annual mortality growth rates increases by generation while males’ remains stable. Nevertheless, this result doesn’t contradict the fact that females’ average residual life becomes longer because females’ 25-year-old mortality rate decreases by generation.en
dc.description.provenanceMade available in DSpace on 2021-06-15T01:32:01Z (GMT). No. of bitstreams: 1
ntu-98-R96723034-1.pdf: 357164 bytes, checksum: e2428a91a2084aa45aeb661175499045 (MD5)
Previous issue date: 2009
en
dc.description.tableofcontentsTABLES V
FIGURES VI
CHAPTER 1. INTRODUCTION 1
1.1 MOTIVATION AND LITERATURE REVIEW 1
1.2 RESEARCH STRUCTURE 1
CHAPTER 2. HUMAN MORTALITY DATA 3
2.1 DATA INTRODUCTION AND DESCRIPTION 3
2.2 IDENTIFY THE ARIMA MODEL IN HUMAN MORTALITY 6
CHAPTER 3. LONG-MEMORY TIME SERIES MODEL 8
3.1 THE ARFIMA MODEL 8
3.2 THE NORMAL LIKELIHOOD FUNCTION 8
CHAPTER 4. HYPOTHESIS TESTING 10
4.1 BUILDING THE ARFIMA MODEL 10
4.2 PARAMETERS ESTIMATION 10
4.3 DIAGNOSTIC CHECKING 12
4.4 HYPOTHESIS TESTING 13
CHAPTER 5. LONG-MEMORY PROPERTY IN HUMAN MORTALITY 17
5.1 LONG-MEMORY PROPERTY IN ANNUAL MORTALITY GROWTH RATES 17
5.2 ANNUAL MORTALITY GROWTH RATE 22
CHAPTER 6. CONCLUSION 25
6.1 CONCLUSION 25
6.2 RESEARCH LIMITATION 26
6.3 FURTHER RESEARCH 26
REFERENCE 27
APPENDIX A: ACF GRAPHS OF ANNUAL MORTALITY GROWTH RATE 29
APPENDIX B: MAXIMUM APPROXIMATED LIKELIHOOD ESTIMATORS 33
APPENDIX C: DATA 35
dc.language.isoen
dc.title人類死亡率的長記憶性分析zh_TW
dc.titleLong Memory Analysis in Human Mortalityen
dc.typeThesis
dc.date.schoolyear97-2
dc.description.degree碩士
dc.contributor.advisor-orcid,曾郁仁(tzeng@ntu.edu.tw)
dc.contributor.oralexamcommittee何淮中,謝淑貞
dc.subject.keyword長記憶性,ARFIMA模型,人類死亡率,Whittle概似函數,Robinson檢定統計量,zh_TW
dc.subject.keywordLong-memory,ARFIMA model,Human mortality,Whittle likelihood function,Robinson test statistic,en
dc.relation.page38
dc.rights.note有償授權
dc.date.accepted2009-07-20
dc.contributor.author-college管理學院zh_TW
dc.contributor.author-dept財務金融學研究所zh_TW
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