Universität zu Köln

Krah, Anne-Sophie, Nikolic, Zoran and Korn, Ralf ORCID: 0000-0002-9123-3883 (2020). Machine Learning in Least-Squares Monte Carlo Proxy Modeling of Life Insurance Companies. Risks, 8 (1). BASEL: MDPI. ISSN 2227-9091

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Abstract

Under the Solvency II regime, life insurance companies are asked to derive their solvency capital requirements from the full loss distributions over the coming year. Since the industry is currently far from being endowed with sufficient computational capacities to fully simulate these distributions, the insurers have to rely on suitable approximation techniques such as the least-squares Monte Carlo (LSMC) method. The key idea of LSMC is to run only a few wisely selected simulations and to process their output further to obtain a risk-dependent proxy function of the loss. In this paper, we present and analyze various adaptive machine learning approaches that can take over the proxy modeling task. The studied approaches range from ordinary and generalized least-squares regression variants over generalized linear model (GLM) and generalized additive model (GAM) methods to multivariate adaptive regression splines (MARS) and kernel regression routines. We justify the combinability of their regression ingredients in a theoretical discourse. Further, we illustrate the approaches in slightly disguised real-world experiments and perform comprehensive out-of-sample tests.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Krah, Anne-Sophie UNSPECIFIED UNSPECIFIED UNSPECIFIED Nikolic, Zoran UNSPECIFIED UNSPECIFIED UNSPECIFIED Korn, Ralf UNSPECIFIED orcid.org/0000-0002-9123-3883 UNSPECIFIED
URN:	urn:nbn:de:hbz:38-342088
DOI:	10.3390/risks8010021
Journal or Publication Title:	Risks
Volume:	8
Number:	1
Date:	2020
Publisher:	MDPI
Place of Publication:	BASEL
ISSN:	2227-9091
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language CONFIDENCE-INTERVALS; REGRESSION; SELECTION Multiple languages Business, Finance Multiple languages
URI:	http://kups.ub.uni-koeln.de/id/eprint/34208