IDEAS home Printed from https://ideas.repec.org/p/fmg/fmgdps/dp551.html

Comparing Downside Risk Measures for Heavy Tailed Distributions

Author

Listed:
  • Casper G. de Vries
  • Bjørn N. Jorgensen
  • Sarma Mandira
  • Jon Danielsson

Abstract

Using regular variation to define heavy tailed distributions, we show that prominent downside risk measures produce similar and consistent ranking of heavy tailed risk. Thus regardless of the particular risk measure being used, assets will be ranked in a similar and consistent manner for heavy tailed assets.

Suggested Citation

  • Casper G. de Vries & Bjørn N. Jorgensen & Sarma Mandira & Jon Danielsson, 2005. "Comparing Downside Risk Measures for Heavy Tailed Distributions," FMG Discussion Papers dp551, Financial Markets Group.
    • Handle: RePEc:fmg:fmgdps:dp551
    as

    Download full text from publisher

    File URL: http://www.lse.ac.uk/fmg/workingPapers/discussionPapers/fmgdps/dp551.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    Citations

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


    Cited by:

    1. is not listed on IDEAS
    2. Danielsson, Jon & Zhou, Chen, 2015. "Why risk is so hard to measure," LSE Research Online Documents on Economics 62002, London School of Economics and Political Science, LSE Library.
    3. Marco Rocco, 2011. "Extreme value theory for finance: a survey," Questioni di Economia e Finanza (Occasional Papers) 99, Bank of Italy, Economic Research and International Relations Area.
    4. Tarasov, Arthur, . "Coherent Quantitative Analysis of Risks in Agribusiness: Case of Ukraine," AGRIS on-line Papers in Economics and Informatics, Czech University of Life Sciences Prague, Faculty of Economics and Management, vol. 3(4), pages 1-7.
    5. Dennis W. Jansen & Liqun Liu, 2022. "Portfolio choice in the model of expected utility with a safety-first component," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 45(1), pages 187-207, June.
    6. Pais, Amelia & Stork, Philip A., 2011. "Contagion risk in the Australian banking and property sectors," Journal of Banking & Finance, Elsevier, vol. 35(3), pages 681-697, March.
    7. James, Nick & Menzies, Max, 2023. "An exploration of the mathematical structure and behavioural biases of 21st century financial crises," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).
    8. Andre R. Neveu, 2018. "A survey of network-based analysis and systemic risk measurement," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 13(2), pages 241-281, July.
    9. Tavakoli Baghdadabad, Mohammad Reza, 2014. "Average drawdown risk reduction and risk tolerances," Research in Economics, Elsevier, vol. 68(3), pages 264-276.
    10. Felix, Luiz & Kräussl, Roman & Stork, Philip, 2017. "Single stock call options as lottery tickets," CFS Working Paper Series 566, Center for Financial Studies (CFS).
    11. Danielsson, Jon & Zhou, Chen, 2015. "Why risk is so hard to measure," LSE Research Online Documents on Economics 62002, London School of Economics and Political Science, LSE Library.
    12. Michael C. Nwogugu, 2020. "Decision-Making, Sub-Additive Recursive "Matching" Noise And Biases In Risk-Weighted Stock/Bond Index Calculation Methods In Incomplete Markets With Partially Observable Multi-Attribute Pref," Papers 2005.01708, arXiv.org.
    13. Nieto, Maria Rosa & Ruiz, Esther, 2016. "Frontiers in VaR forecasting and backtesting," International Journal of Forecasting, Elsevier, vol. 32(2), pages 475-501.
    14. Tee, Kai-Hong, 2009. "The effect of downside risk reduction on UK equity portfolios included with Managed Futures Funds," International Review of Financial Analysis, Elsevier, vol. 18(5), pages 303-310, December.
    15. Majumder, Debasish, 2023. "Subjectivity in conventional tail measures: An exploratory model with 'risks & biases’," Finance Research Letters, Elsevier, vol. 55(PB).
    16. Luiz Félix & Roman Kräussl & Philip Stork, 2019. "Single Stock Call Options as Lottery Tickets: Overpricing and Investor Sentiment," Journal of Behavioral Finance, Taylor & Francis Journals, vol. 20(4), pages 385-407, October.
    17. Antonio Di Cesare & Philip A. Stork & Casper G. de Vries, 2015. "Risk Measures for Autocorrelated Hedge Fund Returns," Journal of Financial Econometrics, Oxford University Press, vol. 13(4), pages 868-895.
    18. Adam, Alexandre & Houkari, Mohamed & Laurent, Jean-Paul, 2008. "Spectral risk measures and portfolio selection," Journal of Banking & Finance, Elsevier, vol. 32(9), pages 1870-1882, September.
    19. Ergun, Lerby & Molchanov, Alexander & Stork, Philip, 2023. "Technical trading rules, loss avoidance, and the business cycle," Pacific-Basin Finance Journal, Elsevier, vol. 82(C).
    20. Auer, Benjamin R., 2018. "A note on Guo and Xiao's (2016) results on monotonic functions of the Sharpe ratio," Finance Research Letters, Elsevier, vol. 24(C), pages 289-290.
    21. Gregory-Allen, Russell & Lu, Helen & Stork, Philip, 2012. "Asymmetric extreme tails and prospective utility of momentum returns," Economics Letters, Elsevier, vol. 117(1), pages 295-297.
    22. Carsten Lausberg & Felix Brandt, 2024. "Forecasting risk and return of listed real estate: [Die Prognose von Risiko und Rendite von Immobilienaktien:]," Zeitschrift für Immobilienökonomie (German Journal of Real Estate Research), Springer;Gesellschaft für Immobilienwirtschaftliche Forschung e. V., vol. 10(1), pages 1-38, December.
    23. Gonzalo, Jesús & Olmo, José, 2007. "The impact of heavy tails and comovements in downside-risk diversification," UC3M Working papers. Economics we20070208, Universidad Carlos III de Madrid. Departamento de Economía.
    24. Namwon Hyung & Casper G. de Vries, 2010. "The Downside Risk of Heavy Tails induces Low Diversification," Tinbergen Institute Discussion Papers 10-082/2, Tinbergen Institute.

    More about this item

    JEL classification:

    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

    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:fmg:fmgdps:dp551. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: The FMG Administration (email available below). General contact details of provider: http://www.lse.ac.uk/fmg/ .

    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.