IDEAS home Printed from https://ideas.repec.org/a/sae/ausman/v42y2017i1p3-31.html

The Australian retirement lottery: A system failure

Author

Listed:
  • Amandha Ganegoda

    (ANZ Bank, Melbourne, VIC, Australia)

  • John Evans

    (Sydney Business School, University of Wollongong, Sydney, NSW, Australia)

Abstract

The purpose of this paper is to assess the adequacy of the Australian retirement system to fund the needs of retirees by taking into account both the Knightian risk arising from market volatility under normal market conditions as well as the Knightian uncertainty arising from rare but severe market shocks. We have also taken into account changes in employment during the pre-retirement phase. Given the low frequency, high impact of market shocks, the result is that cohorts of Australian retirees will enjoy very different levels of retirement income and there will be consequent shocks to the demand for the Age Pension supplement and potentially, significant variations in the standard of living in retirement for Australian employees. Whilst the Australian retirement system has been put forward as a model for other economies to follow, we find there is a fundamental flaw in the system.

Suggested Citation

  • Amandha Ganegoda & John Evans, 2017. "The Australian retirement lottery: A system failure," Australian Journal of Management, Australian School of Business, vol. 42(1), pages 3-31, February.
    • Handle: RePEc:sae:ausman:v:42:y:2017:i:1:p:3-31
    DOI: 10.1177/0312896214554267
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0312896214554267
    Download Restriction: no
    File URL: https://libkey.io/10.1177/0312896214554267?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
    ---><---

    References listed on IDEAS

    as
    1. Murphy, Kevin M & Welch, Finis, 1990. "Empirical Age-Earnings Profiles," Journal of Labor Economics, University of Chicago Press, vol. 8(2), pages 202-229, April.
    2. Sherris, M. & Tedesco, L. & Zehnwirth, B., 1999. "Investment Returns and Inflation Models: Some Australian Evidence," British Actuarial Journal, Cambridge University Press, vol. 5(1), pages 237-267, April.
    3. Thomas Mikosch & Cătălin Stărică, 2004. "Nonstationarities in Financial Time Series, the Long-Range Dependence, and the IGARCH Effects," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 378-390, February.
    4. Poterba, James & Rauh, Joshua & Venti, Steven & Wise, David, 2007. "Defined contribution plans, defined benefit plans, and the accumulation of retirement wealth," Journal of Public Economics, Elsevier, vol. 91(10), pages 2062-2086, November.
    5. Thomas Mikosch & Catalin Starica, 2004. "Non-stationarities in financial time series, the long range dependence and the IGARCH effects," Econometrics 0412005, University Library of Munich, Germany.
    6. Wilkie, A.D., 1995. "More on a Stochastic Asset Model for Actuarial Use," British Actuarial Journal, Cambridge University Press, vol. 1(5), pages 777-964, December.
    7. Blake, David & Cairns, Andrew J. G. & Dowd, Kevin, 2001. "Pensionmetrics: stochastic pension plan design and value-at-risk during the accumulation phase," Insurance: Mathematics and Economics, Elsevier, vol. 29(2), pages 187-215, October.
    8. Basu, Anup K. & Drew, Michael E., 2010. "The appropriateness of default investment options in defined contribution plans: Australian evidence," Pacific-Basin Finance Journal, Elsevier, vol. 18(3), pages 290-305, June.
    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. John Evans & Abdul Razeed, 2018. "An Analysis of the Australian Superannuation System's Taxes and Transfers," Economic Papers, The Economic Society of Australia, vol. 37(3), pages 299-312, September.

    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. Hong‐Chih Huang, 2010. "Optimal Multiperiod Asset Allocation: Matching Assets to Liabilities in a Discrete Model," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 77(2), pages 451-472, June.
    2. Niklas Ahlgren & Alexander Back & Timo Terasvirta, 2025. "Testing parametric additive time-varying GARCH models," Papers 2506.23821, arXiv.org.
    3. Juan Carlos Cuestas & Luis A. Gil-Alana & Karl Taylor, 2016. "Inflation convergence in Central and Eastern Europe vs. the Eurozone: Non-linearities and long memory," Scottish Journal of Political Economy, Scottish Economic Society, vol. 63(5), pages 519-538, November.
    4. Funke, Michael & Shu, Chang & Cheng, Xiaoqiang & Eraslan, Sercan, 2015. "Assessing the CNH–CNY pricing differential: Role of fundamentals, contagion and policy," Journal of International Money and Finance, Elsevier, vol. 59(C), pages 245-262.
    5. Ewing, Bradley T. & Malik, Farooq, 2005. "Re-examining the asymmetric predictability of conditional variances: The role of sudden changes in variance," Journal of Banking & Finance, Elsevier, vol. 29(10), pages 2655-2673, October.
    6. Sibbertsen, Philipp & Leschinski, Christian & Busch, Marie, 2018. "A multivariate test against spurious long memory," Journal of Econometrics, Elsevier, vol. 203(1), pages 33-49.
    7. Guglielmo Caporale & Luis Gil-Alana, 2013. "Long memory in US real output per capita," Empirical Economics, Springer, vol. 44(2), pages 591-611, April.
    8. Andreï Kostyrka & Dmitry Malakhov, 2021. "Was there ever a shift: Empirical analysis of structural-shift tests for return volatility," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 61, pages 110-139.
    9. Malmsten, Hans & Teräsvirta, Timo, 2004. "Stylized Facts of Financial Time Series and Three Popular Models of Volatility," SSE/EFI Working Paper Series in Economics and Finance 563, Stockholm School of Economics, revised 03 Sep 2004.
    10. WenShwo Fang & Stephen M. Miller, 2014. "Output Growth and its Volatility: The Gold Standard through the Great Moderation," Southern Economic Journal, John Wiley & Sons, vol. 80(3), pages 728-751, January.
    11. Frédy Pokou & Jules Sadefo Kamdem & François Benhmad, 2024. "Hybridization of ARIMA with Learning Models for Forecasting of Stock Market Time Series," Computational Economics, Springer;Society for Computational Economics, vol. 63(4), pages 1349-1399, April.
    12. Castillo, Brenda & León, Ángel & Ñíguez, Trino-Manuel, 2021. "Backtesting VaR under the COVID-19 sudden changes in volatility," Finance Research Letters, Elsevier, vol. 43(C).
    13. Caroline Michere Ndei & Stephen Muchina & Kennedy Waweru, 2019. "Modeling stock market return volatility in the presence of structural breaks: Evidence from Nairobi Securities Exchange, Kenya," International Journal of Research in Business and Social Science (2147-4478), Center for the Strategic Studies in Business and Finance, vol. 8(5), pages 156-171, September.
    14. Luc, BAUWENS & Arie, PREMINGER & Jeroen, ROMBOUTS, 2006. "Regime switching GARCH models," Discussion Papers (ECON - Département des Sciences Economiques) 2006006, Université catholique de Louvain, Département des Sciences Economiques.
    15. Chen, Ying & Härdle, Wolfgang Karl & Pigorsch, Uta, 2010. "Localized Realized Volatility Modeling," Journal of the American Statistical Association, American Statistical Association, vol. 105(492), pages 1376-1393.
    16. Arago-Manzana, Vicent & Fernandez-Izquierdo, Maria Angeles, 2007. "Influence of structural changes in transmission of information between stock markets: A European empirical study," Journal of Multinational Financial Management, Elsevier, vol. 17(2), pages 112-124, April.
    17. AUGUSTYNIAK, Maciej & BAUWENS, Luc & DUFAYS, Arnaud, 2016. "A New Approach to Volatility Modeling : The High-Dimensional Markov Model," LIDAM Discussion Papers CORE 2016042, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    18. Darko B. Vuković & Sonja D. Radenković & Ivana Simeunović & Vyacheslav Zinovev & Milan Radovanović, 2024. "Predictive Patterns and Market Efficiency: A Deep Learning Approach to Financial Time Series Forecasting," Mathematics, MDPI, vol. 12(19), pages 1-26, September.
    19. Billio, Monica & Casarin, Roberto & Osuntuyi, Anthony, 2016. "Efficient Gibbs sampling for Markov switching GARCH models," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 37-57.
    20. Amélie Charles & Olivier Darné & Laurent Ferrara, 2018. "Does The Great Recession Imply The End Of The Great Moderation? International Evidence," Economic Inquiry, Western Economic Association International, vol. 56(2), pages 745-760, April.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    JEL classification:

    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • I31 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - General Welfare, Well-Being
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G18 - Financial Economics - - General Financial Markets - - - Government Policy and Regulation

    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:sae:ausman:v:42:y:2017:i:1:p:3-31. 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: SAGE Publications (email available below). General contact details of provider: http://www.agsm.edu.au .

    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.