DEVINEAU Laurent

In this paper, we discuss the impact of some mortality data anomalies on an internal model capturing longevity risk in the Solvency 2 framework. In particular, we are concerned with abnormal cohort effects such as those for generations 1919 and 1920, for which the period tables provided by the Human Mortality Database show particularly low and high mortality rates respectively. To provide corrected tables for the three countries of interest here (France, Italy and West Germany), we use the approach developed by Boumezoued (2016) for countries for which the method applies (France and Italy), and provide an extension of the method for West Germany as monthly fertility histories are not sufficient to cover the generations of interest. These mortality tables are crucial inputs to stochastic mortality models forecasting future scenarios, from which the extreme 0,5% longevity improvement can be extracted, allowing for the calculation of the Solvency Capital Requirement (SCR). More precisely, to assess the impact of such anomalies in the Solvency II framework, we use a simplified internal model based on three usual stochastic models to project mortality rates in the future combined with a closure table methodology for older ages. Correcting this bias obviously improves the data quality of the mortality inputs, which is of paramount importance today, and slightly decreases the capital requirement. Overall, the longevity risk assessment remains stable, as well as the selection of the stochastic mortality model. As a collateral gain of this data quality improvement, the more regular estimated parameters allow for new insights and a refined assessment regarding longevity risk.

In this paper, we discuss the impact of some mortality data anomalies on an internal model capturing longevity risk in the Solvency 2 framework. In particular, we are concerned with abnormal cohort effects such as those for generations 1919 and 1920, for which the period tables provided by the Human Mortality Database show particularly low and high mortality rates respectively. To provide corrected tables for the three countries of interest here (France, Italy and West Germany), we use the approach developed by Boumezoued (2016) for countries for which the method applies (France and Italy), and provide an extension of the method for West Germany as monthly fertility histories are not sufficient to cover the generations of interest. These mortality tables are crucial inputs to stochastic mortality models forecasting future scenarios, from which the extreme 0,5% longevity improvement can be extracted, allowing for the calculation of the Solvency Capital Requirement (SCR). More precisely, to assess the impact of such anomalies in the Solvency II framework, we use a simplified internal model based on three usual stochastic models to project mortality rates in the future combined with a closure table methodology for older ages. Correcting this bias obviously improves the data quality of the mortality inputs, which is of paramount importance today, and slightly decreases the capital requirement. Overall, the longevity risk assessment remains stable, as well as the selection of the stochastic mortality model. As a collateral gain of this data quality improvement, the more regular estimated parameters allow for new insights and a refined assessment regarding longevity risk.

This paper surveys the stochastic modelling of individual claims occurrence and development for reserving purposes in non-life (general) insurance. The paper revisits the continuous time stochastic modelling framework of Norberg (1993) and Hesselager (1994), and provides a consistent presentation of the modelling, inference, and forecasting (with simulation and closed-forms) of individual claims histories as well as aggregate quantities as the overall reserve for both RBNS and IBNR claims. Numerical illustrations are given based on real portfolio datasets, as well as comparisons with classical triangle-based methods.

This paper demonstrates the efficiency of using Edgeworth and Gram-Charlier expansions in the calibration of the Libor Market Model with Stochastic Volatility and Displaced Diffusion (DD-SV-LMM). Our approach brings together two research areas. first, the results regarding the SV-LMM since the work of Wu and Zhang (2006), especially on the moment generating function, and second the approximation of density distributions based on Edgeworth or Gram-Charlier expansions. By exploring the analytical tractability of moments up to fourth order, we are able to perform an adjustment of the reference Bachelier model with normal volatilities for skewness and kurtosis, and as a by-product to derive a smile formula relating the volatility to the moneyness with interpretable parameters. As a main conclusion, our numerical results show a 98% reduction in computational time for the DD-SV-LMM calibration process compared to the classical numerical integration method developed by Heston (1993).

The Solvency II Directive, submitted by the European Commission in 2009, came into force in January 2016. It is based on three pillars. The first pillar deals with quantitative requirements related to the calculation of the Solvency Capital Requirement. The second pillar deals with risk governance. The third pillar deals with required documents and information, market discipline. For life insurance, the quantitative requirements (Pillar I and part of Pillar II) introduce a high level of complexity. Indeed, in order to create a system adapted to the specificities of companies, the directive has introduced a framework for the valuation of the balance sheet of insurers that is very delicate to understand and use, the economic valuation. Because of this complexity, most European life insurers have, during their first years of implementing the directive, chosen to focus on pillar I knowing that the calculation of the capital requirement would be an essential part of the system. In this thesis, I have chosen to focus my work on the second pillar of the directive and more precisely on the Own Risk and Solvency Assessment (ORSA) process. This regulatory tool is in fact the second major source of complexity in Solvency II. It is a risk management process totally integrated in the company whose objective is to lead insurers to a better understanding of their risks. During my work, I tried to conceptualize and to propose operational implementations to answer the problems induced by the ORSA (calculation of the Global Solvency Requirement and Permanent Compliance). Finally, through a joint work with N. El Karoui, S. Loisel and J.-L. Prigent, we analyzed and exemplified some of the major dangers induced by economic valuation.

The one-year prediction error (one-year MSEP) proposed by Merz and Wüthrich has become a market-standard approach for the assessment of reserve volatilities for Solvency II purposes. However, this approach is declined in a univariate framework. Moreover, Braun proposed a closed-formed expression of the prediction error of several run-off portfolios at the ultimate horizon by taking into account their dependency. This article proposes an analytical expression of the one-year MSEP obtained by generalizing the modeling developed by Braun to the one-year horizon with an approach similar to Merz and Wüthrich. A full mathematical demonstration of the formula has been provided in this paper. A case study is presented to assess the dependency between commercial and motor liabilities businesses based on data coming from a major international insurer.