IDEAS home Printed from https://ideas.repec.org/p/zbw/iwqwdp/032012.html
   My bibliography  Save this paper

A skew and leptokurtic distribution with polynomial tails and characterizing functions in closed form

Author

Listed:
  • Fischer, Matthias

Abstract

We introduce a new skewed and leptokurtic distribution derived from the hyperbolic secant distribution and Johnson's S transformation. Properties of this new distribution are given. Finally, we empirically demonstrate in the context of financial return data that its exibility is comparable to that of their most advanced peers.

Suggested Citation

  • Fischer, Matthias, 2012. "A skew and leptokurtic distribution with polynomial tails and characterizing functions in closed form," FAU Discussion Papers in Economics 03/2012, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    • Handle: RePEc:zbw:iwqwdp:032012
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/59601/1/719072522.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ivana Komunjer, 2007. "Asymmetric power distribution: Theory and applications to risk measurement," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(5), pages 891-921.
    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. Li, Xiao-Ming & Rose, Lawrence C., 2009. "The tail risk of emerging stock markets," Emerging Markets Review, Elsevier, vol. 10(4), pages 242-256, December.
    2. Bao, Te & Diks, Cees & Li, Hao, 2018. "A generalized CAPM model with asymmetric power distributed errors with an application to portfolio construction," Economic Modelling, Elsevier, vol. 68(C), pages 611-621.
    3. Vijverberg, Wim P. & Hasebe, Takuya, 2015. "GTL Regression: A Linear Model with Skewed and Thick-Tailed Disturbances," IZA Discussion Papers 8898, Institute of Labor Economics (IZA).
    4. Jin-Ray Lu & Chiang-Chang Hwang & Yi-Chun Chen & Chu-Ting Wen, 2013. "Including More Information Content to Enhance the Value at Risk Estimation for Real Estate Investment Trusts," International Journal of Financial Research, International Journal of Financial Research, Sciedu Press, vol. 4(3), pages 25-34, July.
    5. Bonato, Matteo & Gupta, Rangan & Lau, Chi Keung Marco & Wang, Shixuan, 2020. "Moments-based spillovers across gold and oil markets," Energy Economics, Elsevier, vol. 89(C).
    6. repec:wrk:wrkemf:28 is not listed on IDEAS
    7. Kaiping Wang, 2014. "Modeling Stock Index Returns using Semi-Parametric Approach with Multiplicative Adjustment," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 65-75, December.
    8. Lafaye de Micheaux, Pierre & Ouimet, Frédéric, 2018. "A uniform L1 law of large numbers for functions of i.i.d. random variables that are translated by a consistent estimator," Statistics & Probability Letters, Elsevier, vol. 142(C), pages 109-117.
    9. Mahdi Teimouri & Saralees Nadarajah, 2022. "Maximum Likelihood Estimation for the Asymmetric Exponential Power Distribution," Computational Economics, Springer;Society for Computational Economics, vol. 60(2), pages 665-692, August.
    10. Zhu, Dongming & Zinde-Walsh, Victoria, 2009. "Properties and estimation of asymmetric exponential power distribution," Journal of Econometrics, Elsevier, vol. 148(1), pages 86-99, January.
    11. Huber, Peter & Oberhofer, Harald & Pfaffermayr, Michael, 2017. "Who creates jobs? Econometric modeling and evidence for Austrian firm level data," European Economic Review, Elsevier, vol. 91(C), pages 57-71.
    12. Lee, Dong Jin & Kim, Tae-Hwan & Mizen, Paul, 2021. "Impulse response analysis in conditional quantile models with an application to monetary policy," Journal of Economic Dynamics and Control, Elsevier, vol. 127(C).
    13. de Castro, Luciano & Galvao, Antonio F. & Montes-Rojas, Gabriel, 2020. "Quantile selection in non-linear GMM quantile models," Economics Letters, Elsevier, vol. 195(C).
    14. Lu Ou & Zhibiao Zhao, 2021. "Value‐at‐risk forecasting via dynamic asymmetric exponential power distributions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(2), pages 291-300, March.
    15. Demetrescu, Matei & Hacıoğlu Hoke, Sinem, 2019. "Predictive regressions under asymmetric loss: Factor augmentation and model selection," International Journal of Forecasting, Elsevier, vol. 35(1), pages 80-99.
    16. Rombouts Jeroen V. K. & Bouaddi Mohammed, 2009. "Mixed Exponential Power Asymmetric Conditional Heteroskedasticity," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 13(3), pages 1-32, May.
    17. Douch, Mohamed & Farooq, Omar & Bouaddi, Mohammed, 2015. "Stock price synchronicity and tails of return distribution," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 37(C), pages 1-11.
    18. Yi-Ting Chen, 2016. "Testing for Granger Causality in Moments," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 78(2), pages 265-288, April.
    19. Vijverberg, Chu-Ping C. & Vijverberg, Wim P.M. & Taşpınar, Süleyman, 2016. "Linking Tukey’s legacy to financial risk measurement," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 595-615.
    20. Haas Markus, 2010. "Skew-Normal Mixture and Markov-Switching GARCH Processes," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 14(4), pages 1-56, September.
    21. Tae-Hwan Kim & Dong Jin Lee & Paul Mizen, 2020. "Impulse Response Analysis in Conditional Quantile Models and an Application to Monetary Policy," Working papers 2020rwp-164, Yonsei University, Yonsei Economics Research Institute.

    More about this item

    Keywords

    hyperbolic secant distribution; SU-transformation; skewness; leptokurtosis; polynomial tails;
    All these keywords.

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:zbw:iwqwdp:032012. 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: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://edirc.repec.org/data/vierlde.html .

    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.