Economic Scenario Generators: a risk management tool for insurance

Pierre-Edouard Arrouy, Alexandre Boumezoued, Bernard Lapeyre () and Sophian Mehalla
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Pierre-Edouard Arrouy: Recherche et Développement, Milliman Paris - Milliman France
Alexandre Boumezoued: Recherche et Développement, Milliman Paris - Milliman France
Bernard Lapeyre: CERMICS - Centre d'Enseignement et de Recherche en Mathématiques et Calcul Scientifique - ENPC - École nationale des ponts et chaussées, MATHRISK - Mathematical Risk Handling - UPEM - Université Paris-Est Marne-la-Vallée - ENPC - École nationale des ponts et chaussées - Centre Inria de Paris - Inria - Institut National de Recherche en Informatique et en Automatique, ENPC - École nationale des ponts et chaussées
Sophian Mehalla: Recherche et Développement, Milliman Paris - Milliman France, CERMICS - Centre d'Enseignement et de Recherche en Mathématiques et Calcul Scientifique - ENPC - École nationale des ponts et chaussées, MATHRISK - Mathematical Risk Handling - UPEM - Université Paris-Est Marne-la-Vallée - ENPC - École nationale des ponts et chaussées - Centre Inria de Paris - Inria - Institut National de Recherche en Informatique et en Automatique

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Abstract: We present a risk management tool, named Economic Scenario Generator (ESG), used by insurance companies for simulating the global state of one or several economies described by key financial risk drivers. This tool is of particular use within the Solvency II framework, since insurance companies are required to value their balance-sheet from a market-consistent viewpoint. However, there is no observable price of insurance contracts hence the necessity of relying on ESGs to perform Monte Carlo simulations useful for valuation. As such, the calibration of Risk-Neutral models underlying this valuation is of particular interest as there is a strong requirement to match observable market prices. Furthermore, for a variety of applications, the insurance company has to value its balance-sheet over a set of different economic conditions, leading to the need of intensive re-calibrations of such models. In this paper, we first provide an overview of the key requirements from Solvency II and their practical implications for insurance valuation. We then describe the different use cases of ESGs. A particular attention is paid to Risk-Neutral interest rates models, specifically the Libor Market Model with a stochastic volatility. We discuss the complexity of its calibration and describe fast calibration methods based on approximations and expansions of the probability density function. Comparisons with more common method highlight the reduction in calibration time.

Keywords: Insurance; Libor Market Model; interest rate; stochastic volatility (search for similar items in EconPapers)
Date: 2022
New Economics Papers: this item is included in nep-rmg
Note: View the original document on HAL open archive server: https://hal.science/hal-03671943v2
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Published in MathematicS In Action, 2022, 11 (1), pp.43--60

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-03671943

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