IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2306.16563.html

Using Monte Carlo Methods for Retirement Simulations

Author

Listed:
  • Aditya Gupta
  • Vijay K. Tayal

Abstract

Retirement prediction helps individuals and institutions make informed financial, lifestyle, and workforce decisions based on estimated retirement portfolios. This paper attempts to predict retirement using Monte Carlo simulations, allowing one to probabilistically account for a range of possibilities. The authors propose a model to predict the values of the investment accounts IRA and 401(k) through the simulation of inflation rates, interest rates, and other pertinent factors. They provide a user case study to discuss the implications of the proposed model.

Suggested Citation

  • Aditya Gupta & Vijay K. Tayal, 2023. "Using Monte Carlo Methods for Retirement Simulations," Papers 2306.16563, arXiv.org, revised Nov 2023.
    • Handle: RePEc:arx:papers:2306.16563
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2306.16563
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Pollet, Joshua M. & Wilson, Mungo, 2010. "Average correlation and stock market returns," Journal of Financial Economics, Elsevier, vol. 96(3), pages 364-380, June.
    2. Fernando Alvarez & Robert E. Lucas & Warren E. Weber, 2001. "Interest Rates and Inflation," American Economic Review, American Economic Association, vol. 91(2), pages 219-225, May.
    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. Filippo Occhino, 2001. "Monetary Policy Shocks in an Economy with Segmented Markets," Departmental Working Papers 200108, Rutgers University, Department of Economics.
    2. Shaukat, Badiea & Zhu, Qigui & Khan, M. Ijaz, 2019. "Real interest rate and economic growth: A statistical exploration for transitory economies," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
    3. Mr. Michael Kumhof & Mr. Evan C Tanner, 2005. "Government Debt: A Key Role in Financial Intermediation," IMF Working Papers 2005/057, International Monetary Fund.
    4. Arısoy, Yakup Eser & Altay-Salih, Aslıhan & Akdeniz, Levent, 2015. "Aggregate volatility expectations and threshold CAPM," The North American Journal of Economics and Finance, Elsevier, vol. 34(C), pages 231-253.
    5. Dima, Bogdan & Dima, Ştefana Maria & Ioan, Roxana, 2025. "The short-run impact of investor expectations’ past volatility on current predictions: The case of VIX," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 98(C).
    6. Dror Y. Kenett & Xuqing Huang & Irena Vodenska & Shlomo Havlin & H. Eugene Stanley, 2015. "Partial correlation analysis: applications for financial markets," Quantitative Finance, Taylor & Francis Journals, vol. 15(4), pages 569-578, April.
    7. Rodriguez, Harold & Colombo, Jefferson, 2025. "Is bitcoin an inflation hedge?," Journal of Economics and Business, Elsevier, vol. 133(C).
    8. Peng, Yulei & Zervou, Anastasia, 2022. "Monetary policy rules and the equity premium in a segmented markets model," Journal of Macroeconomics, Elsevier, vol. 73(C).
    9. Claudiu Tiberiu ALBULESCU & Dominique Pepin, 2018. "Monetary Integration, Money-Demand Stability, and the Role of Monetary Overhang in Forecasting Inflation in CEE Countries," Journal of Economic Integration, Center for Economic Integration, Sejong University, vol. 33(4), pages 841-879.
    10. Sim, Min Kyu & Deng, Shijie & Huo, Xiaoming, 2021. "What can cluster analysis offer in investing? - Measuring structural changes in the investment universe," International Review of Economics & Finance, Elsevier, vol. 71(C), pages 299-315.
    11. Max Gillman & Michal Kejak, 2005. "Inflation and Balanced-Path Growth with Alternative Payment Mechanisms," Economic Journal, Royal Economic Society, vol. 115(500), pages 247-270, January.
    12. Guo, Li & Sang, Bo & Tu, Jun & Wang, Yu, 2024. "Cross-cryptocurrency return predictability," Journal of Economic Dynamics and Control, Elsevier, vol. 163(C).
    13. Cao, Sean & Jiang, Wei & Wang, Junbo & Yang, Baozhong, 2024. "From Man vs. Machine to Man + Machine: The art and AI of stock analyses," Journal of Financial Economics, Elsevier, vol. 160(C).
    14. Browne, Frank & Cronin, David, 2006. "A Segmented Markets Model of Inflation," Research Technical Papers 17/RT/06, Central Bank of Ireland.
    15. Rama K. Malladi, 2024. "Benchmark Analysis of Machine Learning Methods to Forecast the U.S. Annual Inflation Rate During a High-Decile Inflation Period," Computational Economics, Springer;Society for Computational Economics, vol. 64(1), pages 335-375, July.
    16. Chinn, Menzie D., 2006. "The (partial) rehabilitation of interest rate parity in the floating rate era: Longer horizons, alternative expectations, and emerging markets," Journal of International Money and Finance, Elsevier, vol. 25(1), pages 7-21, February.
    17. Nelson, Edward, 2003. "The future of monetary aggregates in monetary policy analysis," Journal of Monetary Economics, Elsevier, vol. 50(5), pages 1029-1059, July.
    18. Choi, Hyung Sun, 2013. "Money and risk of loss in an asset market segmentation model," International Review of Economics & Finance, Elsevier, vol. 25(C), pages 146-155.
    19. Metiu, Norbert & Prieto, Esteban, 2025. "Time-varying stock return correlation, news shocks, and business cycles," European Economic Review, Elsevier, vol. 172(C).
    20. Korn, Olaf & Kuntz, Laura-Chloé, 2015. "Low-beta investment strategies," CFR Working Papers 15-17, University of Cologne, Centre for Financial Research (CFR).

    More about this item

    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:arx:papers:2306.16563. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    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.