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

TL;DR

This paper develops a multilevel Monte-Carlo method for efficiently computing the Solvency Capital Requirement (SCR) using the standard formula, demonstrating improved efficiency and avoiding regression issues in stress test calculations.

Contribution

It provides new theoretical convergence results for MLMC in the context of maximum of conditional expectations and applies it to SCR calculation, comparing favorably with other methods.

Findings

MLMC is more computationally efficient than Least Square Monte-Carlo and Neural Networks.

MLMC avoids regression issues, beneficial for path-dependent balance sheet projections.

The method's effectiveness depends on portfolio allocation and stress test parameters.

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.