Assurance-vie | EPFL Graph Search

Une assurance vie est un contrat d'assurance et un produit d'épargne. En échange du paiement de cotisations périodiques ou non, l'assureur fait fructifier un capital, et s'engage à reverser de l'argent lorsque survient un événement lié à l'assuré : son décès – l'argent est versé au bénéficiaire du contrat –, ou sa survie à l'issue du contrat – l'argent est alors reversé à l'assuré. L'argent est reversé sous forme de capital en une fois ou de rente à échéances fixes et est proportionnel au montant épargné pendant le contrat, c'est à dire les cotisations ou primes versées auxquelles sont soustraites un certain nombre de frais de gestion. L'assureur tient un compte du capital placé, soit en devises (euros, par exemple), soit en unités de compte (actions, obligations, etc.). Le capital ainsi placé peut également être récupéré avant l'échéance. On distingue l'assurance-vie et l'assurance-décès (ou prévoyance) qui implique forcément le décès de l'assuré dans sa description générale. Dr

Dating Death: An Empirical Comparison of Medical Underwriters in the U.S. Life Settlements Market

Jiahua Xu

The value of a life settlement investment, manifested through a traded life insurance policy, is highly dependent on the insured's life expectancy (LE). LE estimation in life settlements relies heavily on medical underwriting. Employing different evaluation processes, underwriters rarely agree on LE estimates, leading to valuation disparities. We use the natural logarithm of the implied mortality multiplier (ln k) to compare the underwriting results of the four major U.S. medical underwriters (ITM, AVS, Fasano, and LSI). ln k is normalized in terms of gender, age, and smoking status, and is therefore a more suitable indicator than LE estimates for highlevel comparisons, especially when the compared groups have a heterogeneous make-up. Based on the analysis of life settlement samples from 2011 to 2016, we trace patterns of underwriters' ln k in both secondary and tertiary markets of life settlements, and investigate systematic differences in their estimation. Our results show that an underwriter can, relative to peers, act more conservatively (issuing longer LE estimates) for one cohort while more aggressively (issuing shorter LE estimates) for another. We also detect signs of intermediaries' cherry-picking behavior and discuss additional theories that shed light on the convoluted LE landscape.

2020

Essays on Capital Calculation in Insurance

Mathieu Jacques David Cambou

In order to be able to bear the risk they are taking, insurance companies have to set aside a certain amount of cushion that can guarantee the payment of liabilities, up to a dened probability, and thus to remain solvent in case of bad events. This amount is named capital. The calculation of capital is a complex problem. To be sustainable, capital must consider all possible risk sources that may lead to losses among assets and liabilities of the insurance company, and it must account for the likelihood and the eect of these bad (and usually extreme) events that could occur to the risk sources. Insurance companies build models and tools in order to perform this capital calculation. For that, they have to collect data, build statistical evidence, build mathematical models and tools in order to eciently and accurately derive capital. The papers exposed in this thesis deal with three major diculties. First, the uncertainty behind the choice of a specic model and the quantication of this uncertainty in terms of additional capital. The use of external scenarios, i.e. opinions on the likelihood of some events happening, allows to build a coherent methodology that make the cushion more robust against wrong model specication. Second, the computational complexity in using these models in an industrialized environment, and numerical methods available for increasing their computational eciency. Most of these models cannot provide an analytical formula of capital. Consequently, one has to approximate it via simulation methods. Considering the high number of risk sources and the complexity of insurance contracts, these methods can be slow to run before providing a reasonable accuracy. This often makes these models unusable in practical cases. Enhancements of classical simulation methods are presented in the aim of making these approximations faster to run for the same level of accuracy. Third, the lack of reliable data and the high complexity of problems with long time horizons, and statistical methods for identifying and building reliable proxies in such cases. A typical example is life-insurance contracts that imply being exposed to multiple risks sources over a long horizon. Such contracts can in fact be approximated wisely by proxies that can capture the risk over time.

EPFL2016

Essays on Capital Calculation in Insurance

Mathieu Jacques David Cambou

EPFL2016