Solving Life Cycle Problems With Biometric Risk by Artificial Insurance Markets

28 Pages Posted: 14 Dec 2018 Last revised: 20 Apr 2021

See all articles by Christoph Hambel

Christoph Hambel

Goethe University Frankfurt

Holger Kraft

Goethe University Frankfurt

Claus Munk

Copenhagen Business School

Date Written: April 20, 2021

Abstract

We study canonical consumption-savings problems of an individual involving uninsurable biometric risk. These problems are important in many applications from insurance economics and actuarial science. Since biometric risk is uninsurable, closed-form solutions do not exist and thus the problems must be approached by numerical methods. We propose a powerful approach where the solution is obtained by optimizing over a parametrized family of consumption strategies. In settings with mortality risk, critical illness risk, and habit formation, our solution method outperforms the well-established finite-difference approach both in run time and in precision. Our method also delivers a precision measure and closed-form representations of the optimal controls.

Keywords: dynamic programming, life-cycle models, biometric risk, insurance, habit formation

JEL Classification: G10, D14, D91, E21, R21

Suggested Citation

Hambel, Christoph and Kraft, Holger and Munk, Claus, Solving Life Cycle Problems With Biometric Risk by Artificial Insurance Markets (April 20, 2021). Available at SSRN: https://ssrn.com/abstract=3289246 or http://dx.doi.org/10.2139/ssrn.3289246

Christoph Hambel

Goethe University Frankfurt ( email )

Faculty of Economics and Business
Theodor-W.-Adorno-Platz 3
Frankfurt am Main, 60323
Germany

Holger Kraft (Contact Author)

Goethe University Frankfurt ( email )

Faculty of Economics and Business
Theodor-W.-Adorno-Platz 3
Frankfurt am Main, 60323
Germany

Claus Munk

Copenhagen Business School ( email )

Department of Finance
Solbjerg Plads 3
Frederiksberg, DK-2000
Denmark

HOME PAGE: http://sites.google.com/view/clausmunk/home

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
80
Abstract Views
739
rank
411,352
PlumX Metrics