Journal Articles Insurance: Mathematics and Economics Year : 2021

Multilevel Monte-Carlo for computing the SCR with the standard formula and other stress tests

Abstract

This paper studies the multilevel Monte-Carlo estimator for the expectation of a maximum of conditional expectations. This problem arises naturally when considering many stress tests and appears in the calculation of the interest rate module of the standard formula for the SCR. We obtain theoretical convergence results that complements the recent work of Giles and Goda and gives some additional tractability through a parameter that somehow describes regularity properties around the maximum. We then apply the MLMC estimator to the calculation of the SCR at future dates with the standard formula for an ALM savings business on life insurance. We compare it with estimators obtained with Least Square Monte-Carlo or Neural Networks. We find that the MLMC estimator is computationally more efficient and has the main advantage to avoid regression issues, which is particularly significant in the context of projection of a balance sheet by an insurer due to the path dependency. Last, we discuss the potentiality of this numerical method and analyze in particular the effect of the portfolio allocation on the SCR at future~dates.
Fichier principal
Vignette du fichier
S0167668721000883.pdf (1.14 Mo) Télécharger le fichier
Origin Files produced by the author(s)

Dates and versions

hal-03026795 , version 1 (13-06-2023)

Licence

Identifiers

Cite

Aurélien Alfonsi, Adel Cherchali, José Arturo Infante Acevedo. Multilevel Monte-Carlo for computing the SCR with the standard formula and other stress tests. Insurance: Mathematics and Economics, 2021, ⟨10.1016/j.insmatheco.2021.05.005⟩. ⟨hal-03026795⟩
113 View
16 Download

Altmetric

Share

More