The book provides a sound mathematical base for life insurance mathematics and applies the underlying concepts to concrete examples. Moreover the models presented make it possible to model life insurance policies by means of Markov chains. Two chapters covering ALM and abstract valuation concepts on the background of Solvency II complete this volume. Numerous examples and a parallel treatment of discrete and continuous approaches help the reader to implement the theory directly in practice.
This book provides an overview of risk management in life insurance companies, focusing on two main aspects: a broad understanding of essential topics and the practical application of tools and techniques. The presentation is designed to convey theory in a clear yet rigorous manner. Initial chapters define and analyze valuation concepts, emphasizing the risks associated with assets and liabilities like bonds, shares, and insurance liabilities. Subsequent chapters explore risk appetite, key insurance processes, and their associated risks. This general overview is followed by detailed discussions on asset risks, insurance risks, and operational risks, highlighting model applications and risk reporting. The book also examines the intrinsic risks of specific insurance products and methods for their analysis. It concludes with a look at emerging risks and regulatory perspectives, including an analysis of the Solvency II standard model and the Swiss Solvency Test. Additionally, several mathematical appendices cover fundamental tools such as probability theory, stochastic processes, Markov chains, and a stochastic life insurance model. These appendices also address the mathematical formulation of valuation concepts like replicating portfolios and arbitrage-free pricing. The book is enriched with supportive tables and figures throughout.
