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Article

Testing the Least-Squares Monte Carlo Method for the Evaluation of Capital Requirements in Life Insurance

1
Department of Economics, Statistics and Finance, University of Calabria, Ponte Bucci Cubo 0 C, 87036 Rende (CS), Italy
2
Department of Economics and Statistics, University of Udine, Via Tomadini 30/A, 33100 Udine UD, Italy
*
Author to whom correspondence should be addressed.
Risks 2020, 8(2), 48; https://doi.org/10.3390/risks8020048
Submission received: 9 April 2020 / Revised: 4 May 2020 / Accepted: 13 May 2020 / Published: 18 May 2020
(This article belongs to the Special Issue Model Risk and Risk Measures)

Abstract

In this paper, we test the efficiency of least-squares Monte Carlo method to estimate capital requirements in life insurance. We choose a simplified Gaussian evaluation framework where closed-form formulas are available and allow us to obtain solid benchmarks. Extensive numerical experiments were conducted by considering different combinations of simulation runs and basis functions, and the corresponding results are illustrated.
Keywords: least squares Monte Carlo; Solvency capital requirements; value at risk least squares Monte Carlo; Solvency capital requirements; value at risk

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MDPI and ACS Style

Costabile, M.; Viviano, F. Testing the Least-Squares Monte Carlo Method for the Evaluation of Capital Requirements in Life Insurance. Risks 2020, 8, 48. https://doi.org/10.3390/risks8020048

AMA Style

Costabile M, Viviano F. Testing the Least-Squares Monte Carlo Method for the Evaluation of Capital Requirements in Life Insurance. Risks. 2020; 8(2):48. https://doi.org/10.3390/risks8020048

Chicago/Turabian Style

Costabile, Massimo, and Fabio Viviano. 2020. "Testing the Least-Squares Monte Carlo Method for the Evaluation of Capital Requirements in Life Insurance" Risks 8, no. 2: 48. https://doi.org/10.3390/risks8020048

APA Style

Costabile, M., & Viviano, F. (2020). Testing the Least-Squares Monte Carlo Method for the Evaluation of Capital Requirements in Life Insurance. Risks, 8(2), 48. https://doi.org/10.3390/risks8020048

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