Tail value-at-risk-based expectiles for extreme risks and their application in distributionally robust portfolio selections

HIGHLIGHTS

  • who: Haoyu Chen and Kun Fan from the School of Data Science, University of Science and Technology of China, Hefei, China have published the research work: Tail Value-at-Risk-Based Expectiles for Extreme Risks and Their Application in Distributionally Robust Portfolio Selections, in the Journal: Mathematics 2023, 11, 91. of 26/Dec/2022
  • what: Motivated by recent advances in the generalized quantile risk measure the authors propose the tail value-at-risk (TVaR)based expectile which can capture the tail risk compared with the classic expectile. The authors investigate the asymptotic properties of eu03b1 . . .

     

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