Survivor bond models for securitizing longevity risk

<p>"Longevity risk is the risk that a reference population’s mortality rates deviate from what is projected from prior life tables. This is due to discoveries in biological sciences, improved public health measures, and nutrition, which have dramatically increased life expectancy. Longevity risk raises life insurers’ liability, increasing product costs and reserves. Securitization through longevity derivatives is a way of dealing with this risk.</p><p>To enhance the pricing of life contingent products, we present an additive type mortality model in the style of the Lee-Carter. This model incorporates policyholder covariates. By using counting processes and martingale machinery, we obtain close form representations for the model’s unknowns. We use the bond pricing approach from Wills and Sherris (2010) to price longevity bonds with this mortality model. Numerical studies suggest that asymptotic properties of model parameter estimators provide a close approximation of the true.</p><p>Pricing longevity derivatives uses a no-arbitrage approach by risk-adjusting the mortality and/or interest rate risks. There are various ways to calibrate the risk-adjusted probability measure. The risk neutral approach and the Wang transform are among the popular methods. In this work, we employ a mean-reverting Hull-White model with a moving target which was recently proposed by Zeddouk and Devolder (2020) for the mortality model and the Vasicek model for evolution of interest rate. We detail how to develop the risk-neutral measure in pricing longevity bonds"--Abstract, page iv.</p>

Name thesis:degree_name

Ph. D. in Mathematics

Grantor

Missouri University of Science and Technology

Author dc:creator

• Codjoe, Priscilla Mansah

• additive mortality
• longevity risk
• risk- neutral
• securitization
• stochastic interest rates
• Mathematics
• Statistics and Probability

Repository record dc:identifier

https://scholarsmine.mst.edu/doctoral_dissertations/3166

OAI identifier oai:identifier

oai:scholarsmine.mst.edu:doctoral_dissertations-4171