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Managing the risk based on entropic value-at-risk under a normal-Rayleigh distribution

Author

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  • Ahmed, Dilan
  • Soleymani, Fazlollah
  • Ullah, Malik Zaka
  • Hasan, Hataw

Abstract

Market observations basically reveal that the data do not follow a normal distribution and fat tails occur. On the other hand, the common measures of risk, like, value-at-risk (VaR) and conditional value-at-risk (CVaR) may not yield in reliable values in managing the risk of a portfolio under some conditions. To overcome these shortcomings, two ideas are furnished in this work. First, a mixture distribution is constructed based on the normal and Rayleigh distributions to provide fatter tails and to be more consistent on market data. And second, the entropic VaR (EVaR) is used to give reliable values for risk management. Finally, several simulation workouts on different stocks from real data are presented and compared to uphold the discussions of this work.

Suggested Citation

  • Ahmed, Dilan & Soleymani, Fazlollah & Ullah, Malik Zaka & Hasan, Hataw, 2021. "Managing the risk based on entropic value-at-risk under a normal-Rayleigh distribution," Applied Mathematics and Computation, Elsevier, vol. 402(C).
    • Handle: RePEc:eee:apmaco:v:402:y:2021:i:c:s0096300321001776
    DOI: 10.1016/j.amc.2021.126129
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    Cited by:

    1. Malik Zaka Ullah & Fouad Othman Mallawi & Mir Asma & Stanford Shateyi, 2022. "On the Conditional Value at Risk Based on the Laplace Distribution with Application in GARCH Model," Mathematics, MDPI, vol. 10(16), pages 1-13, August.
    2. Wang, Gang-Jin & Zhu, Chun-Long, 2021. "BP-CVaR: A novel model of estimating CVaR with back propagation algorithm," Economics Letters, Elsevier, vol. 209(C).

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