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VaR Risk Measures versus Traditional Risk Measures: an Analysis and Survey

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  • Kaplanski, Guy
  • Kroll, Yoram

Abstract

The article presents an analysis and survey regarding the validity of VaR risk measures in comparison to traditional risk measures. Individuals are assumed to either maximize their expected utility or possess a lexicographic utility function. The analysis is carried out for generally distributed functions and for the normal and lognormal distributions. The main conclusion is that although VaR is an inadequate measure within the expected utility framework, it is at least as good as other traditional risk measures. Moreover, it can be improved by modified versions such as the Accumulated-VaR (Mean-Shortfall) Assuming a lexicographic expected utility strengthens the argument for using AVaR as a legitimate risk measure especially in the case of a regulated firm.

Suggested Citation

  • Kaplanski, Guy & Kroll, Yoram, 2002. "VaR Risk Measures versus Traditional Risk Measures: an Analysis and Survey," MPRA Paper 80070, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:80070
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    References listed on IDEAS

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    Cited by:

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    2. Jin Peng, 2011. "Credibilistic value and average value at risk in fuzzy risk analysis," Fuzzy Information and Engineering, Springer, vol. 3(1), pages 69-79, March.
    3. Danielsson, Jon & Zigrand, Jean-Pierre & Jorgensen, Bjørn N. & Sarma, Mandira & de Vries, C. G., 2006. "Consistent measures of risk," LSE Research Online Documents on Economics 24517, London School of Economics and Political Science, LSE Library.
    4. Kaplanski, Guy, 2004. "Traditional beta, downside risk beta and market risk premiums," The Quarterly Review of Economics and Finance, Elsevier, vol. 44(5), pages 636-653, December.
    5. Al Janabi, Mazin A.M., 2014. "Optimal and investable portfolios: An empirical analysis with scenario optimization algorithms under crisis market prospects," Economic Modelling, Elsevier, vol. 40(C), pages 369-381.
    6. Gong, Pu & He, Xubiao, 2005. "A risk hedging strategy under the nonparallel-shift yield curve," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 354(C), pages 450-462.
    7. Demirer, Riza, 2013. "Can advanced markets help diversify risks in frontier stock markets? Evidence from Gulf Arab stock markets," Research in International Business and Finance, Elsevier, vol. 29(C), pages 77-98.
    8. Onur Babat & Juan C. Vera & Luis F. Zuluaga, 2021. "Computing near-optimal Value-at-Risk portfolios using Integer Programming techniques," Papers 2107.07339, arXiv.org.
    9. Olkhov, Victor, 2021. "To VaR, or Not to VaR, That is the Question," MPRA Paper 105458, University Library of Munich, Germany.
    10. Babat, Onur & Vera, Juan C. & Zuluaga, Luis F., 2018. "Computing near-optimal Value-at-Risk portfolios using integer programming techniques," European Journal of Operational Research, Elsevier, vol. 266(1), pages 304-315.
    11. Kaplanski, Guy, 2005. "Analytical Portfolio Value-at-Risk," MPRA Paper 80216, University Library of Munich, Germany.
    12. Brianna Cain & Ralf Zurbruegg, 2010. "Can switching between risk measures lead to better portfolio optimization?," Journal of Asset Management, Palgrave Macmillan, vol. 10(6), pages 358-369, February.

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    More about this item

    Keywords

    Value-at-Risk; Risk management; Risk measures; Mean-Shortfall;
    All these keywords.

    JEL classification:

    • C0 - Mathematical and Quantitative Methods - - General
    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C65 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Miscellaneous Mathematical Tools
    • C69 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Other

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