IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v13y2025i18p2972-d1749120.html
   My bibliography  Save this article

Tail Conditional Expectation and Tail Variance for Extended Generalized Skew-Elliptical Distributions

Author

Listed:
  • Pin Wang

    (Center for Financial Engineering and Department of Mathematics, Soochow University, Suzhou 215006, China
    School of Mathematics and Statistics, Zaozhuang University, Zaozhuang 277160, China)

  • Guojing Wang

    (Center for Financial Engineering and Department of Mathematics, Soochow University, Suzhou 215006, China)

  • Yang Yang

    (Center for Financial Engineering and Department of Mathematics, Soochow University, Suzhou 215006, China)

  • Jing Yao

    (Center for Financial Engineering and Department of Mathematics, Soochow University, Suzhou 215006, China)

Abstract

This study derives explicit expressions for the Tail Conditional Expectation (TCE) and Tail Variance (TV) within the framework of the extended generalized skew-elliptical (EGSE) distribution. The EGSE family generalizes the class of elliptical distributions by incorporating a selection method, thereby allowing simultaneous and flexible control over location, scale, skewness, and tail heaviness in a unified parametric setting. As notable special cases, our results encompass the extended skew-normal, extended skew-Student- t , extended skew-logistic, and extended skew-Laplace distributions. The derived formulas extend existing results for generalized skew-elliptical distributions and reduce, to a considerable extent, the reliance on numerical integration, thus enhancing their tractability for actuarial and financial risk assessment. The practical utility of the proposed framework is further illustrated through an empirical analysis based on real stock market data, highlighting its effectiveness in quantifying and contrasting the heterogeneous tail risk profiles of financial assets.

Suggested Citation

  • Pin Wang & Guojing Wang & Yang Yang & Jing Yao, 2025. "Tail Conditional Expectation and Tail Variance for Extended Generalized Skew-Elliptical Distributions," Mathematics, MDPI, vol. 13(18), pages 1-24, September.
  • Handle: RePEc:gam:jmathe:v:13:y:2025:i:18:p:2972-:d:1749120
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/13/18/2972/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/13/18/2972/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Esmat Jamshidi Eini & Hamid Khaloozadeh, 2021. "Tail conditional moment for generalized skew-elliptical distributions," Journal of Applied Statistics, Taylor & Francis Journals, vol. 48(13-15), pages 2285-2305, November.
    2. Michael Bauer & Mikhail Chernov, 2024. "Interest Rate Skewness and Biased Beliefs," Journal of Finance, American Finance Association, vol. 79(1), pages 173-217, February.
    3. Zinoviy Landsman & Emiliano Valdez, 2003. "Tail Conditional Expectations for Elliptical Distributions," North American Actuarial Journal, Taylor & Francis Journals, vol. 7(4), pages 55-71.
    4. Alexander J. McNeil & Rüdiger Frey & Paul Embrechts, 2015. "Quantitative Risk Management: Concepts, Techniques and Tools Revised edition," Economics Books, Princeton University Press, edition 2, number 10496.
    5. Ignatieva, Katja & Landsman, Zinoviy, 2025. "Tail variance for generalised hyper-elliptical models," ASTIN Bulletin, Cambridge University Press, vol. 55(1), pages 144-167, January.
    6. Barry Arnold & Robert Beaver & A. Azzalini & N. Balakrishnan & A. Bhaumik & D. Dey & C. Cuadras & J. Sarabia & Barry Arnold & Robert Beaver, 2002. "Skewed multivariate models related to hidden truncation and/or selective reporting," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 11(1), pages 7-54, June.
    7. Esmat Jamshidi Eini & Hamid Khaloozadeh, 2022. "Tail variance for Generalized Skew-Elliptical distributions," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 51(2), pages 519-536, January.
    8. Landsman, Zinoviy & Makov, Udi & Shushi, Tomer, 2016. "Tail conditional moments for elliptical and log-elliptical distributions," Insurance: Mathematics and Economics, Elsevier, vol. 71(C), pages 179-188.
    9. Philippe Artzner & Freddy Delbaen & Jean‐Marc Eber & David Heath, 1999. "Coherent Measures of Risk," Mathematical Finance, Wiley Blackwell, vol. 9(3), pages 203-228, July.
    10. Yang, Yang & Wang, Guojing & Yao, Jing & Xie, Hengyue, 2025. "A generalized tail mean-variance model for optimal capital allocation," Insurance: Mathematics and Economics, Elsevier, vol. 122(C), pages 157-179.
    11. Mostafa Monzur Hasan & Grantley Taylor & Grant Richardson, 2022. "Brand Capital and Stock Price Crash Risk," Management Science, INFORMS, vol. 68(10), pages 7221-7247, October.
    12. Furman, Edward & Landsman, Zinoviy, 2006. "Tail Variance Premium with Applications for Elliptical Portfolio of Risks," ASTIN Bulletin, Cambridge University Press, vol. 36(2), pages 433-462, November.
    13. Shi, Yue & Punzo, Antonio & Otneim, Håkon & Maruotti, Antonello, 2025. "Hidden semi-Markov models for rainfall-related insurance claims," Insurance: Mathematics and Economics, Elsevier, vol. 120(C), pages 91-106.
    14. A. Azzalini & A. Capitanio, 1999. "Statistical applications of the multivariate skew normal distribution," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(3), pages 579-602.
    15. Branco, Márcia D. & Dey, Dipak K., 2001. "A General Class of Multivariate Skew-Elliptical Distributions," Journal of Multivariate Analysis, Elsevier, vol. 79(1), pages 99-113, October.
    16. Ignatieva, Katja & Landsman, Zinoviy, 2015. "Estimating the tails of loss severity via conditional risk measures for the family of symmetric generalised hyperbolic distributions," Insurance: Mathematics and Economics, Elsevier, vol. 65(C), pages 172-186.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Eini, Esmat Jamshidi & Khaloozadeh, Hamid, 2021. "The tail mean–variance optimal portfolio selection under generalized skew-elliptical distribution," Insurance: Mathematics and Economics, Elsevier, vol. 98(C), pages 44-50.
    2. Vernic, Raluca, 2006. "Multivariate skew-normal distributions with applications in insurance," Insurance: Mathematics and Economics, Elsevier, vol. 38(2), pages 413-426, April.
    3. Zinoviy Landsman & Udi Makov & Tomer Shushi, 2017. "Extended Generalized Skew-Elliptical Distributions and their Moments," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 79(1), pages 76-100, February.
    4. Furman, Edward & Wang, Ruodu & Zitikis, Ričardas, 2017. "Gini-type measures of risk and variability: Gini shortfall, capital allocations, and heavy-tailed risks," Journal of Banking & Finance, Elsevier, vol. 83(C), pages 70-84.
    5. Landsman, Zinoviy & Makov, Udi & Shushi, Tomer, 2018. "A multivariate tail covariance measure for elliptical distributions," Insurance: Mathematics and Economics, Elsevier, vol. 81(C), pages 27-35.
    6. Baishuai Zuo & Chuancun Yin & Jing Yao, 2023. "Multivariate range Value-at-Risk and covariance risk measures for elliptical and log-elliptical distributions," Papers 2305.09097, arXiv.org.
    7. Mohammed, Nawaf & Furman, Edward & Su, Jianxi, 2021. "Can a regulatory risk measure induce profit-maximizing risk capital allocations? The case of conditional tail expectation," Insurance: Mathematics and Economics, Elsevier, vol. 101(PB), pages 425-436.
    8. Yang, Yang & Wang, Guojing & Yao, Jing & Xie, Hengyue, 2025. "A generalized tail mean-variance model for optimal capital allocation," Insurance: Mathematics and Economics, Elsevier, vol. 122(C), pages 157-179.
    9. Baishuai Zuo & Chuancun Yin, 2022. "Doubly truncated moment risk measures for elliptical distributions," Papers 2203.01091, arXiv.org.
    10. Huang, Zhenzhen & Wei, Pengyu & Weng, Chengguo, 2024. "Tail mean-variance portfolio selection with estimation risk," Insurance: Mathematics and Economics, Elsevier, vol. 116(C), pages 218-234.
    11. Xiangyu Han & Chuancun Yin, 2022. "Tail Conditional Moments for Location-Scale Mixture of Elliptical Distributions," Mathematics, MDPI, vol. 10(4), pages 1-21, February.
    12. Kim, Joseph H.T. & Kim, So-Yeun, 2019. "Tail risk measures and risk allocation for the class of multivariate normal mean–variance mixture distributions," Insurance: Mathematics and Economics, Elsevier, vol. 86(C), pages 145-157.
    13. Panagiotelis, Anastasios & Smith, Michael, 2010. "Bayesian skew selection for multivariate models," Computational Statistics & Data Analysis, Elsevier, vol. 54(7), pages 1824-1839, July.
    14. Jiang, Chun-Fu & Peng, Hong-Yi & Yang, Yu-Kuan, 2016. "Tail variance of portfolio under generalized Laplace distribution," Applied Mathematics and Computation, Elsevier, vol. 282(C), pages 187-203.
    15. Nuerxiati Abudurexiti & Kai He & Dongdong Hu & Svetlozar T. Rachev & Hasanjan Sayit & Ruoyu Sun, 2024. "Portfolio analysis with mean-CVaR and mean-CVaR-skewness criteria based on mean–variance mixture models," Annals of Operations Research, Springer, vol. 336(1), pages 945-966, May.
    16. Shushi, Tomer, 2019. "The Minkowski length of a spherical random vector," Statistics & Probability Letters, Elsevier, vol. 153(C), pages 104-107.
    17. Karling, Maicon J. & Durante, Daniele & Genton, Marc G., 2024. "Conjugacy properties of multivariate unified skew-elliptical distributions," Journal of Multivariate Analysis, Elsevier, vol. 204(C).
    18. Ignatieva, Katja & Landsman, Zinoviy, 2021. "A class of generalised hyper-elliptical distributions and their applications in computing conditional tail risk measures," Insurance: Mathematics and Economics, Elsevier, vol. 101(PB), pages 437-465.
    19. Koike, Takaaki & Hofert, Marius, 2021. "Modality for scenario analysis and maximum likelihood allocation," Insurance: Mathematics and Economics, Elsevier, vol. 97(C), pages 24-43.
    20. Ji, Liuyan & Tan, Ken Seng & Yang, Fan, 2021. "Tail dependence and heavy tailedness in extreme risks," Insurance: Mathematics and Economics, Elsevier, vol. 99(C), pages 282-293.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    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:gam:jmathe:v:13:y:2025:i:18:p:2972-:d:1749120. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.