IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v279y2019i1d10.1007_s10479-018-3100-z.html
   My bibliography  Save this article

A multistage risk-averse stochastic programming model for personal savings accrual: the evidence from Lithuania

Author

Listed:
  • Audrius Kabašinskas

    (Kaunas University of Technology)

  • Francesca Maggioni

    (University of Bergamo)

  • Kristina Šutienė

    (Kaunas University of Technology)

  • Eimutis Valakevičius

    (Kaunas University of Technology)

Abstract

In this paper we consider the problem of choosing the optimal pension fund in the second pillar of Lithuanian pension system by providing some guidelines to individuals with defined contribution pension plans. A multistage risk-averse stochastic optimization model is proposed that can be used to plan a long-term pension accrual under two different cases: minimum and maximum accumulation plans as possible options in the system. The investment strategy of personal savings is based on the optimal solutions over possible scenario realizations generated for a particular participant. The concept of the risk-averse decision-maker is implemented by choosing the conditional value at risk as the risk measure defined by a nested formulation that guarantees the time consistency in the multistage model. The paper focuses on three important decision-making moments corresponding to the duration of periods to be modelled. The first period is a short-term accumulation, while the second period is a long-term accumulation with possibly high deviation of objective function value. The third period is designed to implement the concept of target date fund in the second pillar pension scheme as the subsequent need to protect against potential losses at risky pension funds. The experimental findings of this research provide insights for individuals as decision-makers to select pension funds, as well as for policy-makers by revealing the vulnerability of pension system.

Suggested Citation

  • Audrius Kabašinskas & Francesca Maggioni & Kristina Šutienė & Eimutis Valakevičius, 2019. "A multistage risk-averse stochastic programming model for personal savings accrual: the evidence from Lithuania," Annals of Operations Research, Springer, vol. 279(1), pages 43-70, August.
    • Handle: RePEc:spr:annopr:v:279:y:2019:i:1:d:10.1007_s10479-018-3100-z
    DOI: 10.1007/s10479-018-3100-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-018-3100-z
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10479-018-3100-z?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Homem-de-Mello, Tito & Pagnoncelli, Bernardo K., 2016. "Risk aversion in multistage stochastic programming: A modeling and algorithmic perspective," European Journal of Operational Research, Elsevier, vol. 249(1), pages 188-199.
    2. Konicz, Agnieszka Karolina & Mulvey, John M., 2015. "Optimal savings management for individuals with defined contribution pension plans," European Journal of Operational Research, Elsevier, vol. 243(1), pages 233-247.
    3. Andrzej Ruszczyński & Alexander Shapiro, 2006. "Conditional Risk Mappings," Mathematics of Operations Research, INFORMS, vol. 31(3), pages 544-561, August.
    4. Francesca Maggioni & Stein Wallace, 2012. "Analyzing the quality of the expected value solution in stochastic programming," Annals of Operations Research, Springer, vol. 200(1), pages 37-54, November.
    5. Blake, David & Wright, Douglas & Zhang, Yumeng, 2014. "Age-dependent investing: Optimal funding and investment strategies in defined contribution pension plans when members are rational life cycle financial planners," Journal of Economic Dynamics and Control, Elsevier, vol. 38(C), pages 105-124.
    6. Jackowicz, Krzysztof & Kowalewski, Oskar, 2012. "Crisis, internal governance mechanisms and pension fund performance: Evidence from Poland," Emerging Markets Review, Elsevier, vol. 13(4), pages 493-515.
    7. G. Consigli & M. Dempster, 1998. "Dynamic stochastic programmingfor asset-liability management," Annals of Operations Research, Springer, vol. 81(0), pages 131-162, June.
    8. A. Fiori Maccioni & A. Bitinas, 2013. "Lithuanian pension system's reforms following demographic and social transitions," Working Paper CRENoS 201315, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    9. Francesca Maggioni & Elisabetta Allevi & Marida Bertocchi, 2014. "Bounds in Multistage Linear Stochastic Programming," Journal of Optimization Theory and Applications, Springer, vol. 163(1), pages 200-229, October.
    10. Francesca Maggioni & Elisabetta Allevi & Marida Bertocchi, 2016. "Monotonic bounds in multistage mixed-integer stochastic programming," Computational Management Science, Springer, vol. 13(3), pages 423-457, July.
    11. Youngjun Yoon, 2010. "Glide path and dynamic asset allocation of target date funds," Journal of Asset Management, Palgrave Macmillan, vol. 11(5), pages 346-360, December.
    12. Olga Rajevska, 2015. "Sustainability of Pension Systems in the Baltic States," Entrepreneurial Business and Economics Review, Centre for Strategic and International Entrepreneurship at the Cracow University of Economics., vol. 3(4), pages 139-153.
    13. Laun, Tobias & Wallenius, Johanna, 2015. "A life cycle model of health and retirement: The case of Swedish pension reform," Journal of Public Economics, Elsevier, vol. 127(C), pages 127-136.
    14. T. Gudaitis & A. Fiori Maccioni, 2014. "Optimal Individual Choice of Contribution to Second Pillar Pension System in Lithuania," Working Paper CRENoS 201402, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    15. Thomas, Ashok & Spataro, Luca & Mathew, Nanditha, 2014. "Pension funds and stock market volatility: An empirical analysis of OECD countries," Journal of Financial Stability, Elsevier, vol. 11(C), pages 92-103.
    16. Andrea Consiglio & Flavio Cocco & Stavros Zenios, 2007. "Scenario optimization asset and liability modelling for individual investors," Annals of Operations Research, Springer, vol. 152(1), pages 167-191, July.
    17. Jitka Dupačová & Giorgio Consigli & Stein Wallace, 2000. "Scenarios for Multistage Stochastic Programs," Annals of Operations Research, Springer, vol. 100(1), pages 25-53, December.
    18. Rudloff, Birgit & Street, Alexandre & Valladão, Davi M., 2014. "Time consistency and risk averse dynamic decision models: Definition, interpretation and practical consequences," European Journal of Operational Research, Elsevier, vol. 234(3), pages 743-750.
    19. repec:wsd:irgpim:v:86:y:2011:i:1:p:185-199 is not listed on IDEAS
    20. Rockafellar, R. Tyrrell & Uryasev, Stanislav, 2002. "Conditional value-at-risk for general loss distributions," Journal of Banking & Finance, Elsevier, vol. 26(7), pages 1443-1471, July.
    21. Philpott, A.B. & de Matos, V.L., 2012. "Dynamic sampling algorithms for multi-stage stochastic programs with risk aversion," European Journal of Operational Research, Elsevier, vol. 218(2), pages 470-483.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Mohammed H. Alamoudi & Omer A. Bafail, 2022. "BWM—RAPS Approach for Evaluating and Ranking Banking Sector Companies Based on Their Financial Indicators in the Saudi Stock Market," JRFM, MDPI, vol. 15(10), pages 1-20, October.
    2. 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.

    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. İ. Esra Büyüktahtakın, 2022. "Stage-t scenario dominance for risk-averse multi-stage stochastic mixed-integer programs," Annals of Operations Research, Springer, vol. 309(1), pages 1-35, February.
    2. Davi Valladão & Thuener Silva & Marcus Poggi, 2019. "Time-consistent risk-constrained dynamic portfolio optimization with transactional costs and time-dependent returns," Annals of Operations Research, Springer, vol. 282(1), pages 379-405, November.
    3. Francesca Maggioni & Matteo Cagnolari & Luca Bertazzi, 2019. "The value of the right distribution in stochastic programming with application to a Newsvendor problem," Computational Management Science, Springer, vol. 16(4), pages 739-758, October.
    4. Andre Luiz Diniz & Maria Elvira P. Maceira & Cesar Luis V. Vasconcellos & Debora Dias J. Penna, 2020. "A combined SDDP/Benders decomposition approach with a risk-averse surface concept for reservoir operation in long term power generation planning," Annals of Operations Research, Springer, vol. 292(2), pages 649-681, September.
    5. Schur, Rouven & Gönsch, Jochen & Hassler, Michael, 2019. "Time-consistent, risk-averse dynamic pricing," European Journal of Operational Research, Elsevier, vol. 277(2), pages 587-603.
    6. Daniel R. Jiang & Warren B. Powell, 2018. "Risk-Averse Approximate Dynamic Programming with Quantile-Based Risk Measures," Mathematics of Operations Research, INFORMS, vol. 43(2), pages 554-579, May.
    7. Francesca Maggioni & Elisabetta Allevi & Asgeir Tomasgard, 2020. "Bounds in multi-horizon stochastic programs," Annals of Operations Research, Springer, vol. 292(2), pages 605-625, September.
    8. Bakker, Hannah & Dunke, Fabian & Nickel, Stefan, 2020. "A structuring review on multi-stage optimization under uncertainty: Aligning concepts from theory and practice," Omega, Elsevier, vol. 96(C).
    9. Das, Sanjiv R. & Ostrov, Daniel & Radhakrishnan, Anand & Srivastav, Deep, 2022. "Dynamic optimization for multi-goals wealth management," Journal of Banking & Finance, Elsevier, vol. 140(C).
    10. Mahmutoğulları, Ali İrfan & Çavuş, Özlem & Aktürk, M. Selim, 2018. "Bounds on risk-averse mixed-integer multi-stage stochastic programming problems with mean-CVaR," European Journal of Operational Research, Elsevier, vol. 266(2), pages 595-608.
    11. Bushaj, Sabah & Büyüktahtakın, İ. Esra & Haight, Robert G., 2022. "Risk-averse multi-stage stochastic optimization for surveillance and operations planning of a forest insect infestation," European Journal of Operational Research, Elsevier, vol. 299(3), pages 1094-1110.
    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. Gambella, Claudio & Maggioni, Francesca & Vigo, Daniele, 2019. "A stochastic programming model for a tactical solid waste management problem," European Journal of Operational Research, Elsevier, vol. 273(2), pages 684-694.
    14. Ching-Hui Tang, 2018. "Two-stage stochastic modeling of transportation outsourcing plans for transshipment centers," 4OR, Springer, vol. 16(1), pages 67-94, March.
    15. Thuener Silva & Davi Valladão & Tito Homem-de-Mello, 2021. "A data-driven approach for a class of stochastic dynamic optimization problems," Computational Optimization and Applications, Springer, vol. 80(3), pages 687-729, December.
    16. Pisciella, P. & Vespucci, M.T. & Bertocchi, M. & Zigrino, S., 2016. "A time consistent risk averse three-stage stochastic mixed integer optimization model for power generation capacity expansion," Energy Economics, Elsevier, vol. 53(C), pages 203-211.
    17. Fattahi, Mohammad & Keyvanshokooh, Esmaeil & Kannan, Devika & Govindan, Kannan, 2023. "Resource planning strategies for healthcare systems during a pandemic," European Journal of Operational Research, Elsevier, vol. 304(1), pages 192-206.
    18. Bomze, Immanuel M. & Gabl, Markus & Maggioni, Francesca & Pflug, Georg Ch., 2022. "Two-stage stochastic standard quadratic optimization," European Journal of Operational Research, Elsevier, vol. 299(1), pages 21-34.
    19. Alois Pichler & Ruben Schlotter, 2020. "Quantification of Risk in Classical Models of Finance," Papers 2004.04397, arXiv.org, revised Feb 2021.
    20. Kostrova, Alisa & Britz, Wolfgang & Djanibekov, Utkur & Finger, Robert, 2016. "Monte-Carlo Simulation and Stochastic Programming in Real Options Valuation: the Case of Perennial Energy Crop Cultivation," Discussion Papers 250253, University of Bonn, Institute for Food and Resource Economics.

    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:spr:annopr:v:279:y:2019:i:1:d:10.1007_s10479-018-3100-z. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.