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The Innovest Austrian Pension Fund Financial Planning Model InnoALM

Author

Listed:
  • Alois Geyer

    (University of Economics and Vienna Graduate School of Finance, Vienna, Austria)

  • William T. Ziemba

    (Sauder School of Business, University of British Columbia, Vancouver, British Columbia, Canada V6T 1Z2, and Visiting Professor, Mathematical Institute, Oxford University, Oxford, United Kingdom 0X1 3LB, and ICMA Centre, University of Reading, Reading, United Kingdom RG6 6BA)

Abstract

This paper describes the financial planning model InnoALM we developed at Innovest for the Austrian pension fund of the electronics firm Siemens. The model uses a multiperiod stochastic linear programming framework with a flexible number of time periods of varying length. Uncertainty is modeled using multiperiod discrete probability scenarios for random return and other model parameters. The correlations across asset classes, of bonds, stocks, cash, and other financial instruments, are state dependent using multiple correlation matrices that correspond to differing market conditions. This feature allows InnoALM to anticipate and react to severe as well as normal market conditions. Austrian pension law and policy considerations can be modeled as constraints in the optimization. The concave risk-averse preference function is to maximize the expected present value of terminal wealth at the specified horizon net of expected discounted convex (piecewise-linear) penalty costs for wealth and benchmark targets in each decision period. InnoALM has a user interface that provides visualization of key model outputs, the effect of input changes, growing pension benefits from increased deterministic wealth target violations, stochastic benchmark targets, security reserves, policy changes, etc. The solution process using the IBM OSL stochastic programming code is fast enough to generate virtually online decisions and results and allows for easy interaction of the user with the model to improve pension fund performance. The model has been used since 2000 for Siemens Austria, Siemens worldwide, and to evaluate possible pension fund regulation changes in Austria.

Suggested Citation

  • Alois Geyer & William T. Ziemba, 2008. "The Innovest Austrian Pension Fund Financial Planning Model InnoALM," Operations Research, INFORMS, vol. 56(4), pages 797-810, August.
  • Handle: RePEc:inm:oropre:v:56:y:2008:i:4:p:797-810
    DOI: 10.1287/opre.1080.0564
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    2. Ferstl, Robert & Weissensteiner, Alex, 2011. "Asset-liability management under time-varying investment opportunities," Journal of Banking & Finance, Elsevier, vol. 35(1), pages 182-192, January.
    3. Maram Alwohaibi & Diana Roman, 2018. "ALM models based on second order stochastic dominance," Computational Management Science, Springer, vol. 15(2), pages 187-211, June.
    4. Francisco Rivadeneyra & Oumar Dissou, 2011. "A Model of the EFA Liabilities," Discussion Papers 11-11, Bank of Canada.
    5. Staino, Alessandro & Russo, Emilio, 2015. "A moment-matching method to generate arbitrage-free scenarios," European Journal of Operational Research, Elsevier, vol. 246(2), pages 619-630.
    6. Robert Ferstl & Alex Weissensteiner, 2010. "Cash management using multi-stage stochastic programming," Quantitative Finance, Taylor & Francis Journals, vol. 10(2), pages 209-219.
    7. Agnieszka Karolina Konicz & David Pisinger & Alex Weissensteiner, 2016. "Optimal retirement planning with a focus on single and joint life annuities," Quantitative Finance, Taylor & Francis Journals, vol. 16(2), pages 275-295, February.
    8. Davide Lauria & Giorgio Consigli & Francesca Maggioni, 2022. "Optimal chance-constrained pension fund management through dynamic stochastic control," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 44(3), pages 967-1007, September.
    9. Lijian Wang & Daniel Béland, 2014. "Assessing the Financial Sustainability of China’s Rural Pension System," Sustainability, MDPI, vol. 6(6), pages 1-20, May.
    10. Di Giacinto, Marina & Federico, Salvatore & Gozzi, Fausto & Vigna, Elena, 2014. "Income drawdown option with minimum guarantee," European Journal of Operational Research, Elsevier, vol. 234(3), pages 610-624.
    11. Alaeddine Faleh, 2011. "Un modèle de programmation stochastique pour l'allocation stratégique d'actifs d'un régime de retraite partiellement provisionné," Working Papers hal-00561965, HAL.
    12. Sebastiano Vitali & Vittorio Moriggia & Miloš Kopa, 2017. "Optimal pension fund composition for an Italian private pension plan sponsor," Computational Management Science, Springer, vol. 14(1), pages 135-160, January.
    13. Sebastiano Vitali & Vittorio Moriggia, 2021. "Pension fund management with investment certificates and stochastic dominance," Annals of Operations Research, Springer, vol. 299(1), pages 273-292, April.
    14. Gerrard, Russell & Hiabu, Munir & Kyriakou, Ioannis & Nielsen, Jens Perch, 2019. "Communication and personal selection of pension saver’s financial risk," European Journal of Operational Research, Elsevier, vol. 274(3), pages 1102-1111.
    15. Chul Jang & Andrew Clare & Iqbal Owadally, 2024. "Liability-driven investment for pension funds: stochastic optimization with real assets," Risk Management, Palgrave Macmillan, vol. 26(3), pages 1-32, September.
    16. Christina Erlwein & Gautam Mitra & Diana Roman, 2012. "HMM based scenario generation for an investment optimisation problem," Annals of Operations Research, Springer, vol. 193(1), pages 173-192, March.
    17. Geyer, Alois & Hanke, Michael & Weissensteiner, Alex, 2010. "No-arbitrage conditions, scenario trees, and multi-asset financial optimization," European Journal of Operational Research, Elsevier, vol. 206(3), pages 609-613, November.
    18. Costanza Torricelli, 2009. "Models For Household Portfolios And Life-Cycle Allocations In The Presence Of Labour Income And Longevity Risk," Centro Studi di Banca e Finanza (CEFIN) (Center for Studies in Banking and Finance) 0017, Universita di Modena e Reggio Emilia, Dipartimento di Economia "Marco Biagi".
    19. Moriggia, Vittorio & Kopa, Miloš & Vitali, Sebastiano, 2019. "Pension fund management with hedging derivatives, stochastic dominance and nodal contamination," Omega, Elsevier, vol. 87(C), pages 127-141.
    20. Francesco Menoncin & Elena Vigna, 2013. "Mean-variance target-based optimisation in DC plan with stochastic interest rate," Carlo Alberto Notebooks 337, Collegio Carlo Alberto.
    21. William Ziemba, 2009. "Use of stochastic and mathematical programming in portfolio theory and practice," Annals of Operations Research, Springer, vol. 166(1), pages 5-22, February.
    22. Robert Ferstl & Alexander Weissensteiner, 2011. "Backtesting Short-Term Treasury Management Strategies Based on Multi-Stage Stochastic Programming," Palgrave Macmillan Books, in: Gautam Mitra & Katharina Schwaiger (ed.), Asset and Liability Management Handbook, chapter 19, pages 469-494, Palgrave Macmillan.
    23. Oikonomou, Ioannis & Platanakis, Emmanouil & Sutcliffe, Charles, 2018. "Socially responsible investment portfolios: Does the optimization process matter?," The British Accounting Review, Elsevier, vol. 50(4), pages 379-401.
    24. Audrius Kabašinskas & Kristina Šutienė & Miloš Kopa & Kęstutis Lukšys & Kazimieras Bagdonas, 2020. "Dominance-Based Decision Rules for Pension Fund Selection under Different Distributional Assumptions," Mathematics, MDPI, vol. 8(5), pages 1-26, May.
    25. Daniel Giamouridis & Athanasios Sakkas & Nikolaos Tessaromatis, 2017. "Dynamic Asset Allocation with Liabilities," European Financial Management, European Financial Management Association, vol. 23(2), pages 254-291, March.

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