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Comparing Downside Risk Measures for Heavy Tailed Distributions

Author

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  • 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
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. 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.
    3. Tarasov, Arthur, 2011. "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, December.
    4. 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.
    5. 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.
    6. 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).
    7. 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.
    8. Tavakoli Baghdadabad, Mohammad Reza, 2014. "Average drawdown risk reduction and risk tolerances," Research in Economics, Elsevier, vol. 68(3), pages 264-276.
    9. Felix, Luiz & Kräussl, Roman & Stork, Philip, 2017. "Single stock call options as lottery tickets," CFS Working Paper Series 566, Center for Financial Studies (CFS).
    10. 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.
    11. Gonzalo, J. & Olmo, J., 2007. "The impact of heavy tails and comovements in downside-risk diversification," Working Papers 07/02, Department of Economics, City University London.
    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. 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.
    15. 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.
    16. Majumder, Debasish, 2023. "Subjectivity in conventional tail measures: An exploratory model with 'risks & biases’," Finance Research Letters, Elsevier, vol. 55(PB).
    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. 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.

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    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

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