IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v12y2024i9p1397-d1388285.html
   My bibliography  Save this article

Scale Mixture of Gleser Distribution with an Application to Insurance Data

Author

Listed:
  • Neveka M. Olmos

    (Departamento de Estadística y Ciencias de Datos, Facultad de Ciencias Básicas, Universidad de Antofagasta, Antofagasta 1240000, Chile)

  • Emilio Gómez-Déniz

    (Department of Quantitative Methods in Economics, TIDES Institute, University of Las Palmas de Gran Canaria, 35017 Las Palmas de Gran Canaria, Spain)

  • Osvaldo Venegas

    (Departamento de Ciencias Matemáticas y Físicas, Facultad de Ingeniería, Universidad Católica de Temuco, Temuco 4780000, Chile)

Abstract

In this paper, the scale mixture of the Gleser (SMG) distribution is introduced. This new distribution is the product of a scale mixture between the Gleser (G) distribution and the Beta ( a , 1 ) distribution. The SMG distribution is an alternative to distributions with two parameters and a heavy right tail. We study its representation and some basic properties, maximum likelihood inference, and Fisher’s information matrix. We present an application to a real dataset in which the SMG distribution shows a better fit than two other known distributions.

Suggested Citation

  • Neveka M. Olmos & Emilio Gómez-Déniz & Osvaldo Venegas, 2024. "Scale Mixture of Gleser Distribution with an Application to Insurance Data," Mathematics, MDPI, vol. 12(9), pages 1-12, May.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:9:p:1397-:d:1388285
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/12/9/1397/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/12/9/1397/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Aban, Inmaculada B. & Meerschaert, Mark M. & Panorska, Anna K., 2006. "Parameter Estimation for the Truncated Pareto Distribution," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 270-277, March.
    2. Resnick, Sidney I., 1997. "Discussion of the Danish Data on Large Fire Insurance Losses," ASTIN Bulletin, Cambridge University Press, vol. 27(1), pages 139-151, May.
    3. Paul Embrechts & Sidney Resnick & Gennady Samorodnitsky, 1999. "Extreme Value Theory as a Risk Management Tool," North American Actuarial Journal, Taylor & Francis Journals, vol. 3(2), pages 30-41.
    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. Rand Kwong Yew Low, 2018. "Vine copulas: modelling systemic risk and enhancing higher‐moment portfolio optimisation," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 58(S1), pages 423-463, November.
    2. Castillo, Joan del & Serra, Isabel, 2015. "Likelihood inference for generalized Pareto distribution," Computational Statistics & Data Analysis, Elsevier, vol. 83(C), pages 116-128.
    3. Shahzad Hussain & Sajjad Haider Bhatti & Tanvir Ahmad & Muhammad Ahmed Shehzad, 2021. "Parameter estimation of the Pareto distribution using least squares approaches blended with different rank methods and its applications in modeling natural catastrophes," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 107(2), pages 1693-1708, June.
    4. Blázquez de Paz, Mario, 2018. "Electricity auctions in the presence of transmission constraints and transmission costs," Energy Economics, Elsevier, vol. 74(C), pages 605-627.
    5. S. A. Abu Bakar & Saralees Nadarajah & Z. A. Absl Kamarul Adzhar, 2018. "Loss modeling using Burr mixtures," Empirical Economics, Springer, vol. 54(4), pages 1503-1516, June.
    6. J. Park & T. P. Seager & P. S. C. Rao & M. Convertino & I. Linkov, 2013. "Integrating Risk and Resilience Approaches to Catastrophe Management in Engineering Systems," Risk Analysis, John Wiley & Sons, vol. 33(3), pages 356-367, March.
    7. Kwame Boamah‐Addo & Tomasz J. Kozubowski & Anna K. Panorska, 2023. "A discrete truncated Zipf distribution," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 77(2), pages 156-187, May.
    8. Wang, Yulong & Xiao, Zhijie, 2022. "Estimation and inference about tail features with tail censored data," Journal of Econometrics, Elsevier, vol. 230(2), pages 363-387.
    9. Carlin C. F. Chu & Simon S. W. Li, 2024. "A multiobjective optimization approach for threshold determination in extreme value analysis for financial time series," Computational Management Science, Springer, vol. 21(1), pages 1-14, June.
    10. Pawel Siarka, 2012. "Implementation of the Stress Test Methods in the Retail Portfolio," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 2(6), pages 1-2.
    11. Amira Dridi & Mohamed El Ghourabi & Mohamed Limam, 2012. "On monitoring financial stress index with extreme value theory," Quantitative Finance, Taylor & Francis Journals, vol. 12(3), pages 329-339, March.
    12. Gaglianone, Wagner Piazza & Lima, Luiz Renato & Linton, Oliver & Smith, Daniel R., 2011. "Evaluating Value-at-Risk Models via Quantile Regression," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(1), pages 150-160.
    13. Ghosh Indranil, 2019. "On the Reliability for Some Bivariate Dependent Beta and Kumaraswamy Distributions: A Brief Survey," Stochastics and Quality Control, De Gruyter, vol. 34(2), pages 115-121, December.
    14. Lillo, Fabrizio & Livieri, Giulia & Marmi, Stefano & Solomko, Anton & Vaienti, Sandro, 2023. "Unimodal maps perturbed by heteroscedastic noise: an application to a financial systems," LSE Research Online Documents on Economics 120290, London School of Economics and Political Science, LSE Library.
    15. Zhang, Zhengjun & Zhu, Bin, 2016. "Copula structured M4 processes with application to high-frequency financial data," Journal of Econometrics, Elsevier, vol. 194(2), pages 231-241.
    16. Zhao, Zifeng & Zhang, Zhengjun & Chen, Rong, 2018. "Modeling maxima with autoregressive conditional Fréchet model," Journal of Econometrics, Elsevier, vol. 207(2), pages 325-351.
    17. 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.
    18. Gencay, Ramazan & Selcuk, Faruk & Ulugulyagci, Abdurrahman, 2003. "High volatility, thick tails and extreme value theory in value-at-risk estimation," Insurance: Mathematics and Economics, Elsevier, vol. 33(2), pages 337-356, October.
    19. Kathrin Kirchen & William Harbert & Jay Apt & M. Granger Morgan, 2020. "A Solar‐Centric Approach to Improving Estimates of Exposure Processes for Coronal Mass Ejections," Risk Analysis, John Wiley & Sons, vol. 40(5), pages 1020-1039, May.
    20. Silvia Figini & Ron Kenett & SILVIA SALINI, 2010. "Integrating Operational and Financial Risk Assessments," UNIMI - Research Papers in Economics, Business, and Statistics unimi-1099, Universitá degli Studi di Milano.

    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:12:y:2024:i:9:p:1397-:d:1388285. 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.