IDEAS home Printed from https://ideas.repec.org/a/gam/jjrfmx/v14y2021i5p202-d548135.html
   My bibliography  Save this article

Large Deviations for a Class of Multivariate Heavy-Tailed Risk Processes Used in Insurance and Finance

Author

Listed:
  • Miriam Hägele

    (Department of Mathematics and Statistics, University of Helsinki, P.O. Box 64 (Gustaf Hällströmin katu 2), FI-00014 Helsinki, Finland
    These authors contributed equally to this work.)

  • Jaakko Lehtomaa

    (Department of Mathematics and Statistics, University of Helsinki, P.O. Box 64 (Gustaf Hällströmin katu 2), FI-00014 Helsinki, Finland
    These authors contributed equally to this work.)

Abstract

Modern risk modelling approaches deal with vectors of multiple components. The components could be, for example, returns of financial instruments or losses within an insurance portfolio concerning different lines of business. One of the main problems is to decide if there is any type of dependence between the components of the vector and, if so, what type of dependence structure should be used for accurate modelling. We study a class of heavy-tailed multivariate random vectors under a non-parametric shape constraint on the tail decay rate. This class contains, for instance, elliptical distributions whose tail is in the intermediate heavy-tailed regime, which includes Weibull and lognormal type tails. The study derives asymptotic approximations for tail events of random walks. Consequently, a full large deviations principle is obtained under, essentially, minimal assumptions. As an application, an optimisation method for a large class of Quota Share (QS) risk sharing schemes used in insurance and finance is obtained.

Suggested Citation

  • Miriam Hägele & Jaakko Lehtomaa, 2021. "Large Deviations for a Class of Multivariate Heavy-Tailed Risk Processes Used in Insurance and Finance," JRFM, MDPI, vol. 14(5), pages 1-18, May.
    • Handle: RePEc:gam:jjrfmx:v:14:y:2021:i:5:p:202-:d:548135
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1911-8074/14/5/202/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1911-8074/14/5/202/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mary Hardy, 2001. "A Regime-Switching Model of Long-Term Stock Returns," North American Actuarial Journal, Taylor & Francis Journals, vol. 5(2), pages 41-53.
    2. Lehtomaa, Jaakko & Resnick, Sidney I., 2020. "Asymptotic independence and support detection techniques for heavy-tailed multivariate data," Insurance: Mathematics and Economics, Elsevier, vol. 93(C), pages 262-277.
    3. Mark J. Jensen & John M. Maheu, 2018. "Risk, Return and Volatility Feedback: A Bayesian Nonparametric Analysis," JRFM, MDPI, vol. 11(3), pages 1-29, September.
    4. Martin Tegnér & Rolf Poulsen, 2018. "Volatility Is Log-Normal—But Not for the Reason You Think," Risks, MDPI, vol. 6(2), pages 1-16, April.
    5. Cline, Daren B. H. & Resnick, Sidney I., 1992. "Multivariate subexponential distributions," Stochastic Processes and their Applications, Elsevier, vol. 42(1), pages 49-72, August.
    6. Søren Asmussen & Jaakko Lehtomaa, 2017. "Distinguishing Log-Concavity from Heavy Tails," Risks, MDPI, vol. 5(1), pages 1-14, February.
    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. Miriam Hägele & Jaakko Lehtomaa, 2023. "On the Identification of the Riskiest Directional Components from Multivariate Heavy-Tailed Data," Risks, MDPI, vol. 11(7), pages 1-18, July.
    2. Donelli, Nicola & Peluso, Stefano & Mira, Antonietta, 2021. "A Bayesian semiparametric vector Multiplicative Error Model," Computational Statistics & Data Analysis, Elsevier, vol. 161(C).
    3. Yizhan Shu & Chenyu Yu & John M. Mulvey, 2024. "Downside risk reduction using regime-switching signals: a statistical jump model approach," Journal of Asset Management, Palgrave Macmillan, vol. 25(5), pages 493-507, September.
    4. Wu, Ji & Guo, Mengmeng & Chen, Minghua & Jeon, Bang Nam, 2019. "Market power and risk-taking of banks: Some semiparametric evidence from emerging economies," Emerging Markets Review, Elsevier, vol. 41(C).
    5. Jin, Xin & Maheu, John M., 2016. "Bayesian semiparametric modeling of realized covariance matrices," Journal of Econometrics, Elsevier, vol. 192(1), pages 19-39.
    6. Gao, Huan & Mamon, Rogemar & Liu, Xiaoming & Tenyakov, Anton, 2015. "Mortality modelling with regime-switching for the valuation of a guaranteed annuity option," Insurance: Mathematics and Economics, Elsevier, vol. 63(C), pages 108-120.
    7. Kobor, Adam & Szekely, Istvan P., 2004. "Foreign exchange market volatility in EU accession countries in the run-up to Euro adoption: weathering uncharted waters," Economic Systems, Elsevier, vol. 28(4), pages 337-352, December.
    8. Godin, Frédéric & Lai, Van Son & Trottier, Denis-Alexandre, 2019. "Option pricing under regime-switching models: Novel approaches removing path-dependence," Insurance: Mathematics and Economics, Elsevier, vol. 87(C), pages 130-142.
    9. Tian, Ping & Zhou, Hang & Zhou, Duotai, 2023. "Analysis about the Black-Scholes asset price under the regime-switching framework," International Review of Financial Analysis, Elsevier, vol. 88(C).
    10. Holzmann, Hajo & Schwaiger, Florian, 2016. "Testing for the number of states in hidden Markov models," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 318-330.
    11. Yizhan Shu & Chenyu Yu & John M. Mulvey, 2024. "Downside Risk Reduction Using Regime-Switching Signals: A Statistical Jump Model Approach," Papers 2402.05272, arXiv.org, revised Sep 2024.
    12. Alexandre Carbonneau & Fr'ed'eric Godin, 2021. "Deep equal risk pricing of financial derivatives with non-translation invariant risk measures," Papers 2107.11340, arXiv.org.
    13. van Haastrecht, Alexander & Plat, Richard & Pelsser, Antoon, 2010. "Valuation of guaranteed annuity options using a stochastic volatility model for equity prices," Insurance: Mathematics and Economics, Elsevier, vol. 47(3), pages 266-277, December.
    14. Alaeddine Faleh & Fr'ed'eric Planchet & Didier Rulli`ere, 2009. "Les G\'en\'erateurs de Sc\'enarios \'Economiques : quelle utilisation en assurance?," Papers 0911.3472, arXiv.org.
    15. Shin-Yun Wang & Ming-Che Chuang & Shih-Kuei Lin & So-De Shyu, 2021. "Option pricing under stock market cycles with jump risks: evidence from the S&P 500 index," Review of Quantitative Finance and Accounting, Springer, vol. 56(1), pages 25-51, January.
    16. Siu, Tak Kuen, 2023. "European option pricing with market frictions, regime switches and model uncertainty," Insurance: Mathematics and Economics, Elsevier, vol. 113(C), pages 233-250.
    17. Karl Demers‐Bélanger & Van Son Lai, 2020. "Diversification benefits of cat bonds: An in‐depth examination," Financial Markets, Institutions & Instruments, John Wiley & Sons, vol. 29(5), pages 165-228, December.
    18. Fard, Farzad Alavi & Siu, Tak Kuen, 2013. "Pricing participating products with Markov-modulated jump–diffusion process: An efficient numerical PIDE approach," Insurance: Mathematics and Economics, Elsevier, vol. 53(3), pages 712-721.
    19. Battulga Gankhuu, 2021. "Equity-Linked Life Insurances on Maximum of Several Assets," Papers 2111.04038, arXiv.org, revised Sep 2024.
    20. Gan, Guojun & Lin, X. Sheldon, 2015. "Valuation of large variable annuity portfolios under nested simulation: A functional data approach," Insurance: Mathematics and Economics, Elsevier, vol. 62(C), pages 138-150.

    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:jjrfmx:v:14:y:2021:i:5:p:202-:d:548135. 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.