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A Robust Optimization Approach to Pension Fund Management

  • Garud Iyengar
  • Alfred Ka Chun Ma
Chapter
  • 2.1k Downloads

Abstract

Pension plans in the United States come in two varieties. Defined contribution pension plans specify the contribution of the Corporation. The employees have the right to invest the corporation’s contribution and their own contribution in a limited set of funds. The participants in a defined contribution pension plan are responsible for making all the investment decisions and bear all the risks associated with these decisions; thus, the benefit to the participants is uncertain. In contrast, defined benefit pension plans specify the benefits due to plan participants. The plan Sponsor, that is the corporation, makes all the Investment decisions in a defined benefit pension plan and bears all the investment risk. Defined benefit plans have been in the news in the past few years because some firms face the prospect of bankruptcy over severely underfunded pension plans. Consequently there is a need to develop models that account for uncertainty in future market conditions and plan accordingly.

Keywords

Pension Fund Credit Rating Robust Optimization Pension Plan Equity Ratio 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. Alizadeh, F. and Goldfarb, D. (2003) Second-order cone programming. Mathematical Programming 95: 3–51.CrossRefGoogle Scholar
  2. Andersen, E.D. and Andersen, K.D. (2006) The MOSEK optimization toolbox for MATLAB manual Version 4.0, http://www.mosek.com/products/4 0/tools/ help/index.html.Google Scholar
  3. Barrett, W.B., Gosnell, T. and Heuson, A. (1995) Yield curve shifts and the selection of immunization strategies. Journal of Fixed Income 5(2): 53–64.CrossRefGoogle Scholar
  4. Ben-Tal, A. and Nemirovski, A. (2001) Lectures on Modern Convex Optimization: Analysis, Algorithms, and Engineering Applications. Philadelphia, USA: Society for Industrial & Applied Math.CrossRefGoogle Scholar
  5. Ben-Tal, A. and Nemirovski, A. (2002) Robust optimization – Methodology and applications. Mathematical Programming 92(3): 453–480.CrossRefGoogle Scholar
  6. Ben-Tal, A., Margalit, T. and Nemirovski, A. (2000) Robust modeling of multistage portfolio problems. In: H. Frenk (ed.) High Performance Optimization,. Dordrecht, The Netherlands: Kluwer Academic Publishers, pp. 303–328.CrossRefGoogle Scholar
  7. Ben-Tal, A., Goryashko, A., Guslitzer, E. and Nemirovski, A. (2004) Adjustable robust solutions of uncertain linear programs. Mathematical Programming 99(2): 351–376.CrossRefGoogle Scholar
  8. Boros, E. and Prékopa, A. (1989) Closed form two-sided bounds for probabilities that at least r and exactly r out of n events occur. Mathematics of Operations Research 14(2): 317–342.CrossRefGoogle Scholar
  9. Chang, F. (2004) Stochastic Optimization in Continuous Time. Cambridge, UK: Cambridge University Press.CrossRefGoogle Scholar
  10. Consigli, G. and Dempster, M. (1998) Dynamic stochastic programming for asset-liability management. Annals of Operations Research, 81: 131–161.CrossRefGoogle Scholar
  11. Damodaran, A. (2004) Applied Corporate Finance: A User’s Manual, 2nd edn. Hoboken, NJ: Wiley.Google Scholar
  12. Drijver, S., Haneveld, W. and Vlerk, M. (2000) Asset Liability Management Modeling Using Multistage Mixed-integer Stochastic Programming. University of Groningen. Technical Report.Google Scholar
  13. Fabozzi, F., Focardi, S. and Jonas, C. (2004) Can Modeling Help Deal with the Pension Funding Crisis. Houston, TX: The Intertek Group.Google Scholar
  14. Fabozzi, F., Martellini, L. and Priaulet, P. (2005) Predictability in the shape of the term structure of interest rates. Journal of Fixed Income 15(1): 40–53.CrossRefGoogle Scholar
  15. Goldfarb, D. and Iyengar, G. (2003) Robust convex quadratically constrained programs. Mathematical Programming Ser B 97(3): 495–515.CrossRefGoogle Scholar
  16. Hull, J. and White, A. (1990) Pricing interest rate derivative securities. Review of Financial Studies 3(4): 573–592.CrossRefGoogle Scholar
  17. Jin, L., Merton, R. and Bodie, Z. (2006) Do a firm’s equity returns reflect the risk of its pension plan? Journal of Financial Economics 81(1): 1–26.CrossRefGoogle Scholar
  18. Klaassen, P. (1998) Financial asset-pricing theory and stochastic programming models for asset/liability management: A synthesis. Management Science 44(1): 31–48.CrossRefGoogle Scholar
  19. Myers, S. and Majluf, N. (1984) Corporate financing and investment decisions when firms have information that investors do not have. Journal of Financial Economics 13(2): 155–295.CrossRefGoogle Scholar
  20. Nelson, C. and Siegel, A. (1987) Parsimonious modeling of yield curves. Journal of Business 60(4): 473–489.CrossRefGoogle Scholar
  21. Sodhi, M. (2005) LP modeling for asset-liability management: A survey of choices and simplifications. Operations Research 53(2): 181–196.CrossRefGoogle Scholar

Copyright information

© The Editor(s) 2016

Authors and Affiliations

  • Garud Iyengar
    • 1
  • Alfred Ka Chun Ma
    • 2
  1. 1.Industrial Engineering and Operations Research DepartmentColumbia UniversityUSA
  2. 2.Department of FinanceThe Chinese University of Hong KongHong Kong

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