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2016 | 288 | 33-46

Article title

Modeling extreme mortality risk

Content

Title variants

Modelowanie ekstremalnego ryzyka umieralności

Languages of publication

EN

Abstracts

EN
The main aim of the paper is presentation some key aspects in modeling extreme mortality risk. We make a review and discuss measures of extreme mortality risk. Besides, we use approach proposed by J.M. Bravo et al. [2012], that is focused on using EVT to model the statistical behaviour of mortality rates over a given high threshold age. Insurers and reinsurers are interested in assessing the risk exposure to extreme mortality risk.
PL
Celem niniejszej pracy jest przedstawienie kluczowych aspektów w modelowaniu ekstremalnego ryzyka umieralności. Przedstawiamy dwa podejścia pomiaru ryzyka. Po pierwsze, dyskutujemy miary ryzyka ekstremalnego, które są wykorzystywane w pomiarze ryzyka umieralności. Po drugie, przedstawiamy podejście zaproponowane przez J.M. Bravo i innych [2012], polegające na wykorzystaniu EVT do modelowania umieralności powyżej pewnego wieku. Oceną ekstremalnego ryzyka umieralności są zainteresowani ubezpieczyciele i reasekuratorzy.

Year

Volume

288

Pages

33-46

Physical description

Contributors

  • University of Economics in Katowice. Faculty of Informatics and Communication. Department of Demography and Economic Statistics

References

  • Balkema A., Haan L. de (1974), Residual Life Times at Great Age, "Annals of Probability", No. 2, s. 792-804.
  • Bauer D., Bergmann D., Reuß A. (2009), Solvency II and Nested Simulations - a Least- Squares Monte Carlo Approach, Working Paper, Georgia State University and Ulm University.
  • Börger M., Fleischer D., Kuksin N. (2013), Modeling the Mortality Trend Under Modern Solvency Regimes, "ASTIN Bulletin", Vol. 44(1), s. 1-38.
  • Bravo J.M., Real P.C., Freitas P.M. (2012), Modeling and Forecasting Longevity Risk using Extreme Value Theory, http://www.ifd.dauphine.fr/fileadmin/mediatheque/ IFD/Cahiers_de_recherche/Bravo_ElMekkaoui_Corte_Modeling_Longevity_Risks ept2012.pdf (access: 25.05.2016).
  • Cairns A.J.G., Blake D., Dowd K., Coughlan G., Epstein D., Khallaf-Allah M. (2008), The Plausibility of Mortality Density Forecasts: An Analysis of Six Stochastic Mortality Models, Working paper, Heriot-Watt University and Pensions Institute Discussion Paper PI-0801.
  • CEIOPS, Choice of a Risk Measure for Supervisory Purposes: Possible Amendments to the Framework for Consultation, http://ec.europa.eu/internal_market/insurance/ docs/2006-markt-docs/2534-06-risk-measure_en.pdf (access: 1.06.2016).
  • CIA Factbook, https://www.cia.gov/library/publications/the-world-factbook/rankorder/ 2066rank.html (access: 26.05.2016).
  • Embrechts P., Klüppelberg C., Mikosch T. (2008), Modelling Extremal Events for Insurance and Finance, Springer, Berlin.
  • Eves M. (2013), How Swiss Re Manages Mortality Uncertainty, http://www.actuaries.org/ CTTEES_TFM/Documents/MWG_Singapore_Item12_SwissRe_Mortality_Uncertainty. pdf (access: 9.06.2016).
  • Gompertz B. (1825), On the Nature of the Function Expressive of the Law of Human Mortality and on a New Mode of Determining Life Contingencies. Royal Society of London, Philosophical Transactions, Series A 115, s. 513-585.
  • Human Mortality Database (2016), University of California, Berkeley and Max Planck Institute for Demographic Research (Germany), http://www.mortality.org/ (access: 16.06.2016).
  • Krotov A. (2010), Investing in Insurance Risk: Insurance-linked Securities: A Practitioner's Perspective, Risk Books, London.
  • Li S.H., Hardy M.R., Tan K.S. (2008), Threshold Life Tables and Their Applications, "North American Actuarial Journal", Vol. 12(2), s. 99-115.
  • R Development Core Team (2011), R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria, http://www.R-project.org/ (access: 01.06.2016)
  • Trzpiot G. (2015), Modeling Extreme Risk [w:] Modeling Multivariate Data Structures and Risk Analysis, G. Trzpiot (red.),Wydawnictwo Uniwersytetu Ekonomicznego w Katowicach, Katowice.
  • Yamai J., Yoshiba T. (2004), Value-at-risk Versus Expected Shortfall: A Practical Perspective, "Journal of Banking and Finance", No. 29, s. 997-1015.

Document Type

Publication order reference

Identifiers

ISSN
2083-8611

YADDA identifier

bwmeta1.element.cejsh-1aa436c4-462f-44ce-9e97-3724a282b087
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