Jacques Janssen

Virus du SIDA, grippe aviaire (H5N1), grippe A (H1N1) la menace d'une pandémie appartient désormais à notre quotidien. Drame humain majeur, ce risque sanitaire entraînerait de lourdes conséquences financières pour les assureurs. À l'exception de la grippe espagnole de 1918, nous ne disposons que de peu de repères pour modéliser, appréhender et calculer l'impact d'une pandémie. Les nouvelles normes européennes Solvabilité II qui entrent en vigueur en 2013 donnent obligation aux assureurs de cartographier l'ensemble des risques. Dès lors, comment modéliser une pandémie ? Comment évaluer son coût ? Comment transférer tout ou partie de ce risque à un tiers (réassureur, marchés financiers via la titrisation) ? Quelle stratégie de couverture choisir ? Cet ouvrage modélise, pour la première fois, le risque de pandémie et apporte ainsi des réponses claires à l'ensemble de ces questions. Il constitue un guide essentiel pour les compagnies d'assurance.

Modélisation stochastique du risque de pandémie : stratégies de couverture et d'assurance

F. Marcotorchino

J. M. Proth

This book is the result of the fourth International Symposium on Data Analysis held on June 1985 at the Universite Libre de Bruxelles with the help'of the European Institute for Advanced Management. As the preceding ones, the organization of the Symposium started with a call for real life problems from which an International Com mittee selected six topics and asked for several solutions. These topics are : I) Multivariate and longitudinal data on growing children 2) Prehistoric assemblages and lithic artifacts from a small We- european area 3) A comparison of results of European elections 4) Classification of heterogeneous data related to microcomputers 5) Group technology in production management 6) Juvenile gelinquency They are covered by the S1X chapters of this book in the following systematic way : a) firstly, a presentation of the problem is given in the original context of the relevant discipline (Medicine, archaelogy, politics, marketing, production and education); b) Secondly, we present the solution found by people who presents the problem; c) thirdly, we find the other retained solutions among the most significative ones; v vi PREFACE d) finally, a short conclusion compares the different approaches. The diversity of the six selected problems clearly shows that Data Analysis can be used for solving a wide variety of problems. Moreover, the fact that each problem is approached by several dif ferent way - at least two - also shows that, in general, a "univer sal" statistical method does not exist.

The Ins and Outs of Solving Real Problems

Data Analysis

Jean-Marie Proth

J. F. Marcotorchino

Ins and Outs of Solving Problems

Data Analysis in Real Life Environment

Raimondo Manca

Aims to give to the reader the tools necessary to apply semi-Markov processes in real-life problems. The book is self-contained and, starting from a low level of probability concepts, gradually brings the reader to a deep knowledge of semi-Markov processes. Presents homogeneous and non-homogeneous semi-Markov processes, as well as Markov and semi-Markov rewards processes. The concepts are fundamental for many applications, but they are not as thoroughly presented in other books on the subject as they are here.

Applied Semi-Markov Processes

Everyone working in related fields from applied mathematicians to statisticians to actuaries and operations researchers will find this a brilliantly useful practical text. The book presents applications of semi-Markov processes in finance, insurance and reliability, using real-life problems as examples. After a presentation of the main probabilistic tools necessary for understanding of the book, the authors show how to apply semi-Markov processes in finance, starting from the axiomatic definition and continuing eventually to the most advanced financial tools.

Jacques Janssen

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