Minimum capital requirement and portfolio allocation for non-life insurance: a semiparametric model with Conditional Value-at-Risk (CVaR) constraint

Alessandro Staino (), Emilio Russo (), Massimo Costabile () and Arturo Leccadito ()
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Alessandro Staino: University of Calabria
Emilio Russo: University of Calabria
Massimo Costabile: University of Calabria
Arturo Leccadito: University of Calabria

Computational Management Science, 2023, vol. 20, issue 1, No 12, 32 pages

Abstract: Abstract We present an optimization problem to determine the minimum capital requirement for a non-life insurance company. The optimization problem imposes a non-positive Conditional Value-at-Risk (CVaR) of the insurer’s net loss and a portfolio performance constraint. When expressing the optimization problem in a semiparametric form, we demonstrate its convexity for any integrable random variable representing the insurer’s liability. Furthermore, we prove that the function defining the CVaR constraint in the semiparametric formulation is continuously differentiable when the insurer’s liability has a continuous distribution. We use the Kelley-Cheney-Goldstein algorithm to solve the optimization problem in the semiparametric form and show its convergence. An empirical analysis is carried out by assuming three different liability distributions: a lognormal distribution, a gamma distribution, and a mixture of Erlang distributions with a common scale parameter. The numerical experiments show that the choice of the liability distribution plays a crucial role since marked differences emerge when comparing the mixture distribution with the other two distributions. In particular, the mixture distribution describes better the right tail of the empirical distribution of liabilities with respect to the other two distributions and implies higher capital requirements and different assets in the optimal portfolios.

Keywords: Non-life insurance; Capital requirement; Conditional Value-at-Risk; Convex optimization (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10287-023-00439-1

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