Longevity risk, the risk associated with people living too long, is an emerging issue in financial markets. Two major factors related to this are with regards to mortality modeling and pricing of life insurance instruments. We propose use of Gaussian process regression, a technique recently populuarized in machine learning, to aid in both of these problems. In particular, we present three works using Gaussian processes in longevity risk applications. The first is related to pricing, where Gaussian processes can serve as a surrogate for conditional expectation needed for Monte Carlo simulations. Second, we investigate value-at-risk calculations in a related framework, introducing a sequential algorithm allowing Gaussian processes to search for the quantile. Lastly, we use Gaussian processes as a spatial model to model mortality rates and improvement.
|Commitee:||Fouque, Jean-Pierre, Ichiba, Tomoyuki|
|School:||University of California, Santa Barbara|
|Department:||Statistics and Applied Probability|
|School Location:||United States -- California|
|Source:||DAI-B 79/02(E), Dissertation Abstracts International|
|Subjects:||Applied Mathematics, Statistics|
|Keywords:||Financial math, Gaussian processes, Longevity risk, Machine learning, Monte Carlo, Mortality modeling|
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