IDEAS home Printed from https://ideas.repec.org/a/bla/scjsta/v41y2014i2p382-393.html
   My bibliography  Save this article

Methods to Distinguish Between Polynomial and Exponential Tails

Author

Listed:
  • Joan Del Castillo
  • Jalila Daoudi
  • Richard Lockhart

Abstract

type="main" xml:id="sjos12037-abs-0001"> Two methods to distinguish between polynomial and exponential tails are introduced. The methods are based on the properties of the residual coefficient of variation for the exponential and non-exponential distributions. A graphical method, called a CV-plot, shows departures from exponentiality in the tails. The plot is applied to the daily log-returns of exchange rates of US dollar and Japanese yen. New statistics are introduced for testing the exponentiality of tails using multiple thresholds. They give better control of the significance level than previous tests. The powers of the new tests are compared with those of some others for various sample sizes.

Suggested Citation

  • Joan Del Castillo & Jalila Daoudi & Richard Lockhart, 2014. "Methods to Distinguish Between Polynomial and Exponential Tails," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 41(2), pages 382-393, June.
    • Handle: RePEc:bla:scjsta:v:41:y:2014:i:2:p:382-393
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/sjos.12037
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. del Castillo, Joan & Daoudi, Jalila, 2009. "Estimation of the generalized Pareto distribution," Statistics & Probability Letters, Elsevier, vol. 79(5), pages 684-688, March.
    2. Gel, Yulia R. & Miao, Weiwen & Gastwirth, Joseph L., 2007. "Robust directed tests of normality against heavy-tailed alternatives," Computational Statistics & Data Analysis, Elsevier, vol. 51(5), pages 2734-2746, February.
    3. Gel, Yulia R., 2010. "Test of fit for a Laplace distribution against heavier tailed alternatives," Computational Statistics & Data Analysis, Elsevier, vol. 54(4), pages 958-965, April.
    4. Ghosh, Souvik & Resnick, Sidney, 2010. "A discussion on mean excess plots," Stochastic Processes and their Applications, Elsevier, vol. 120(8), pages 1492-1517, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Castillo, Joan del & Serra, Isabel, 2015. "Likelihood inference for generalized Pareto distribution," Computational Statistics & Data Analysis, Elsevier, vol. 83(C), pages 116-128.
    2. Marek Arendarczyk & Tomasz J. Kozubowski & Anna K. Panorska, 2022. "The Greenwood statistic, stochastic dominance, clustering and heavy tails," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(1), pages 331-352, March.
    3. Matteo Fusi & Fabio Mazzocchetti & Albert Farres & Leonidas Kosmidis & Ramon Canal & Francisco J. Cazorla & Jaume Abella, 2020. "On the Use of Probabilistic Worst-Case Execution Time Estimation for Parallel Applications in High Performance Systems," Mathematics, MDPI, vol. 8(3), pages 1-21, March.
    4. Albrecher, Hansjörg & García Flores, Brandon, 2022. "Asymptotic analysis of generalized Greenwood statistics for very heavy tails," Statistics & Probability Letters, Elsevier, vol. 185(C).
    5. Søren Asmussen & Jaakko Lehtomaa, 2017. "Distinguishing Log-Concavity from Heavy Tails," Risks, MDPI, vol. 5(1), pages 1-14, February.

    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. Søren Asmussen & Jaakko Lehtomaa, 2017. "Distinguishing Log-Concavity from Heavy Tails," Risks, MDPI, vol. 5(1), pages 1-14, February.
    2. Arthur Charpentier & Emmanuel Flachaire, 2022. "Pareto models for top incomes and wealth," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 20(1), pages 1-25, March.
    3. Arthur Charpentier & Emmanuel Flachaire, 2019. "Pareto Models for Top Incomes," Working Papers hal-02145024, HAL.
    4. González-Estrada, Elizabeth & Villaseñor, José A., 2016. "A ratio goodness-of-fit test for the Laplace distribution," Statistics & Probability Letters, Elsevier, vol. 119(C), pages 30-35.
    5. Castillo, Joan del & Serra, Isabel, 2015. "Likelihood inference for generalized Pareto distribution," Computational Statistics & Data Analysis, Elsevier, vol. 83(C), pages 116-128.
    6. Gala, Kaushik & Schwab, Andreas & Mueller, Brandon A., 2024. "Star entrepreneurs on digital platforms: Heavy-tailed performance distributions and their generative mechanisms," Journal of Business Venturing, Elsevier, vol. 39(1).
    7. Vladimir Hlasny, 2021. "Parametric representation of the top of income distributions: Options, historical evidence, and model selection," Journal of Economic Surveys, Wiley Blackwell, vol. 35(4), pages 1217-1256, September.
    8. Peter Grundke & Kamil Pliszka, 2018. "A macroeconomic reverse stress test," Review of Quantitative Finance and Accounting, Springer, vol. 50(4), pages 1093-1130, May.
    9. Marek Arendarczyk & Tomasz J. Kozubowski & Anna K. Panorska, 2022. "The Greenwood statistic, stochastic dominance, clustering and heavy tails," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(1), pages 331-352, March.
    10. Cirillo, Pasquale, 2013. "Are your data really Pareto distributed?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(23), pages 5947-5962.
    11. Jurgita Arnastauskaitė & Tomas Ruzgas & Mindaugas Bražėnas, 2021. "An Exhaustive Power Comparison of Normality Tests," Mathematics, MDPI, vol. 9(7), pages 1-20, April.
    12. repec:jss:jstsof:28:i03 is not listed on IDEAS
    13. Liu, Shengli & Liang, Yongtu, 2021. "Statistics of catastrophic hazardous liquid pipeline accidents," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    14. Arthur Charpentier & Emmanuel Flachaire, 2021. "Pareto Models for Risk Management," Dynamic Modeling and Econometrics in Economics and Finance, in: Gilles Dufrénot & Takashi Matsuki (ed.), Recent Econometric Techniques for Macroeconomic and Financial Data, pages 355-387, Springer.
    15. Belkhir, Mohamed & Saad, Mohsen & Samet, Anis, 2020. "Stock extreme illiquidity and the cost of capital," Journal of Banking & Finance, Elsevier, vol. 112(C).
    16. Taillardat, Maxime & Fougères, Anne-Laure & Naveau, Philippe & de Fondeville, Raphaël, 2023. "Evaluating probabilistic forecasts of extremes using continuous ranked probability score distributions," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1448-1459.
    17. Antonio Díaz & Gonzalo García-Donato & Andrés Mora-Valencia, 2017. "Risk quantification in turmoil markets," Risk Management, Palgrave Macmillan, vol. 19(3), pages 202-224, August.
    18. M. Roth & G. Jongbloed & T.A. Buishand, 2016. "Threshold selection for regional peaks-over-threshold data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(7), pages 1291-1309, July.
    19. Conte, Marc N. & Kelly, David L., 2018. "An imperfect storm: Fat-tailed tropical cyclone damages, insurance, and climate policy," Journal of Environmental Economics and Management, Elsevier, vol. 92(C), pages 677-706.
    20. Katarzyna Iwińska & Athanasios Kampas & Kerry Longhurst, 2019. "Interactions between Democracy and Environmental Quality: Toward a More Nuanced Understanding," Sustainability, MDPI, vol. 11(6), pages 1-17, March.
    21. Martina Sartori & Stefano Schiavo, 2014. "Virtual Water Trade and Country Vulnerability: A network perspective," IEFE Working Papers 73, IEFE, Center for Research on Energy and Environmental Economics and Policy, Universita' Bocconi, Milano, Italy.

    More about this item

    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:bla:scjsta:v:41:y:2014:i:2:p:382-393. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0303-6898 .

    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.