IDEAS home Printed from https://ideas.repec.org/p/ise/remwps/wp02882023.html
   My bibliography  Save this paper

An ‘Eiopean’ Tool to Project Post Retirement Income in Portuguese Defined Contribution Pension Schemes

Author

Listed:
  • Frederico Pinheiro
  • Onofre Alves Simões

Abstract

Ageing of the populations is leading to reforms in Social Security systems with a negative impact on post retirement income. One way to minimize this is to reinforce the role of complementary pension schemes, and pension projections can be an important tool to assist workers in making their decisions on saving for retirement. The topic has been discussed by the European Union (EU) and the European Insurance and Occupational Pensions Authority (EIOPA). This work focuses on a tool for making pension projections in the scope of occupational defined contribution pension schemes, based on EIOPA’s guidance. We aim to study the potential performance of different investment strategies using an Economic Scenario Generator framework and evaluate the impact on the retirement income that such investment strategies produce, under different assumptions. The model underlying the tool takes in three main risk factors: the financial market risk, which includes uncertainty over returns on investments, inflation and interest rates; the labor risk, originated from uncertainty over real wage growth paths; the demographic risk, as a result of the increasing life expectancy.

Suggested Citation

  • Frederico Pinheiro & Onofre Alves Simões, 2023. "An ‘Eiopean’ Tool to Project Post Retirement Income in Portuguese Defined Contribution Pension Schemes," Working Papers REM 2023/0288, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.
    • Handle: RePEc:ise:remwps:wp02882023
    as

    Download full text from publisher

    File URL: https://rem.rc.iseg.ulisboa.pt/wps/pdf/REM_WP_0288_2023.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. A. R. Thatcher, 1999. "The long‐term pattern of adult mortality and the highest attained age," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 162(1), pages 5-43.
    2. K. Fergusson & E. Platen, 2015. "Application Of Maximum Likelihood Estimation To Stochastic Short Rate Models," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 10(02), pages 1-26, December.
    3. Vasicek, Oldrich, 1977. "An equilibrium characterization of the term structure," Journal of Financial Economics, Elsevier, vol. 5(2), pages 177-188, November.
    4. Vasicek, Oldrich Alfonso, 1977. "Abstract: An Equilibrium Characterization of the Term Structure," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 12(4), pages 627-627, November.
    5. de Jong, Frank, 2000. "Time Series and Cross-Section Information in Affine Term-Structure Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 18(3), pages 300-314, July.
    6. Carter, Lawrence R. & Lee, Ronald D., 1992. "Modeling and forecasting US sex differentials in mortality," International Journal of Forecasting, Elsevier, vol. 8(3), pages 393-411, November.
    7. Darrell Duffie & Rui Kan, 1996. "A Yield‐Factor Model Of Interest Rates," Mathematical Finance, Wiley Blackwell, vol. 6(4), pages 379-406, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Frank De Jong & Joost Driessen & Antoon Pelsser, 2001. "Libor Market Models versus Swap Market Models for Pricing Interest Rate Derivatives: An Empirical Analysis," Review of Finance, European Finance Association, vol. 5(3), pages 201-237.
    2. Juneja, Januj, 2014. "Term structure estimation in the presence of autocorrelation," The North American Journal of Economics and Finance, Elsevier, vol. 28(C), pages 119-129.
    3. Driessen, Joost & Melenberg, Bertrand & Nijman, Theo, 2005. "Testing affine term structure models in case of transaction costs," Journal of Econometrics, Elsevier, vol. 126(1), pages 201-232, May.
    4. Peter Hördahl & David Vestin, 2005. "Interpreting Implied Risk-Neutral Densities: The Role of Risk Premia," Review of Finance, European Finance Association, vol. 9(1), pages 97-137.
    5. Andrea Carriero, 2011. "Forecasting The Yield Curve Using Priors From No‐Arbitrage Affine Term Structure Models," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 52(2), pages 425-459, May.
    6. Diebold, Francis X. & Li, Canlin, 2006. "Forecasting the term structure of government bond yields," Journal of Econometrics, Elsevier, vol. 130(2), pages 337-364, February.
    7. Junker, Markus & Szimayer, Alex & Wagner, Niklas, 2006. "Nonlinear term structure dependence: Copula functions, empirics, and risk implications," Journal of Banking & Finance, Elsevier, vol. 30(4), pages 1171-1199, April.
    8. Raviv, Eran, 2015. "Prediction bias correction for dynamic term structure models," Economics Letters, Elsevier, vol. 129(C), pages 112-115.
    9. Eran Raviv, 2013. "Prediction Bias Correction for Dynamic Term Structure Models," Tinbergen Institute Discussion Papers 13-041/III, Tinbergen Institute.
    10. Christoph Berninger & Julian Pfeiffer, 2020. "The Gauss2++ Model -- A Comparison of Different Measure Change Specifications for a Consistent Risk Neutral and Real World Calibration," Papers 2006.08004, arXiv.org.
    11. Cortazar, Gonzalo & Schwartz, Eduardo S. & Naranjo, Lorezo, 2003. "Term Structure Estimation in Low-Frequency Transaction Markets: A Kalman Filter Approach with Incomplete Panel-Data," University of California at Los Angeles, Anderson Graduate School of Management qt56h775cz, Anderson Graduate School of Management, UCLA.
    12. Leunglung Chan & Eckhard Platen, 2016. "Pricing of long dated equity-linked life insurance contracts," Published Paper Series 2016-5, Finance Discipline Group, UTS Business School, University of Technology, Sydney.
    13. Wolfgang Lemke & Deutsche Bundesbank, 2006. "Term Structure Modeling and Estimation in a State Space Framework," Lecture Notes in Economics and Mathematical Systems, Springer, number 978-3-540-28344-7, July.
    14. Michael A H Dempster & Elena A Medova & Michael Villaverde, 2010. "Long-term interest rates and consol bond valuation," Journal of Asset Management, Palgrave Macmillan, vol. 11(2), pages 113-135, June.
    15. Dong Heon Kim, 2004. "Nonlinearity in the Term Structure," Econometric Society 2004 Far Eastern Meetings 440, Econometric Society.
    16. Coroneo, Laura & Nyholm, Ken & Vidova-Koleva, Rositsa, 2011. "How arbitrage-free is the Nelson-Siegel model?," Journal of Empirical Finance, Elsevier, vol. 18(3), pages 393-407, June.
    17. Collin-Dufresne, Pierre & Goldstein, Robert S. & Jones, Christopher S., 2009. "Can interest rate volatility be extracted from the cross section of bond yields?," Journal of Financial Economics, Elsevier, vol. 94(1), pages 47-66, October.
    18. Hwang Yawen & Huang Hong-Chih, 2012. "Modified Logistic Model for Mortality Forecasting and the Application of Mortality-Linked Securities," Asia-Pacific Journal of Risk and Insurance, De Gruyter, vol. 6(1), pages 1-20, February.
    19. Mei-Mei Kuo & Shih-Wen Tai & Bing-Huei Lin, 2012. "Forecasting Term Structure of HIBOR Swap Rates," The International Journal of Business and Finance Research, The Institute for Business and Finance Research, vol. 6(4), pages 87-100.
    20. Januj Amar Juneja, 2022. "A Computational Analysis of the Tradeoff in the Estimation of Different State Space Specifications of Continuous Time Affine Term Structure Models," Computational Economics, Springer;Society for Computational Economics, vol. 60(1), pages 173-220, June.

    More about this item

    Keywords

    Retirement income; Pension projection; Economic scenario generator; Life tables; Real-world valuation; EIOPA;
    All these keywords.

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ise:remwps:wp02882023. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sandra Araújo (email available below). General contact details of provider: https://rem.rc.iseg.ulisboa.pt/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.