Mortality : a statistical approach to detect model misspecification
The Solvency 2 advent and the best-estimate methodology in future cash-ﬂows valuation lead insurers to focus particularly on their assumptions. In mortality, hypothesis are critical as insurers use best-estimate laws instead of standard mortality tables. Backtesting methods, i.e. ex-post modelling validation processes, are encouraged by regulators and rise an increasing interest among practitioners and academics. In this paper, we propose a statistical approach (both parametric and non-parametric models compliant) for mortality laws backtesting under model risk. Afterwards, we'll introduce a speciﬁcation risk supposing the mortality law true in average but subject to random variations. Finally, the suitability of our method will be assessed within this framework.
|Date of creation:||23 Jun 2013|
|Date of revision:|
|Publication status:||Published in AFIR Colloquium, Jun 2013, Lyon, France|
|Note:||View the original document on HAL open archive server: https://hal.archives-ouvertes.fr/hal-00839339|
|Contact details of provider:|| Web page: https://hal.archives-ouvertes.fr/|
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Booth, H. & Tickle, L., 2008. "Mortality Modelling and Forecasting: a Review of Methods," Annals of Actuarial Science, Cambridge University Press, vol. 3(1-2), pages 3-43, September.
When requesting a correction, please mention this item's handle: RePEc:hal:journl:hal-00839339. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (CCSD)
If references are entirely missing, you can add them using this form.