Dissertation/Thesis Abstract

Three Applications of Gaussian Process Modeling in Evaluation of Longevity Risk Management
by Risk, James Kenneth, Ph.D., University of California, Santa Barbara, 2017, 245; 10620897
Abstract (Summary)

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.

Indexing (document details)
Advisor: Ludkovski, Michael
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
Publication Number: 10620897
ISBN: 978-0-355-42935-0
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