In the years since the publication of the best-selling first edition, the incorporation of ideas and theories from the rapidly growing field of financial economics has precipitated considerable development of thinking in the actuarial profession. Modern Actuarial Theory and Practice, Second Edition integrates those changes and presents an up-to-date, comprehensive overview of UK and international actuarial theory, practice and modeling. It describes all of the traditional areas of actuarial activity, but in a manner that highlights the fundamental principles of actuarial theory and practice as well as their economic, financial, and statistical foundations.

In the years since the publication of the best-selling first edition, the incorporation of ideas and theories from the rapidly growing field of financial economics has precipitated considerable development of thinking in the actuarial profession. Modern Actuarial Theory and Practice, Second Edition integrates those changes and presents an up-to-date, comprehensive overview of UK and international actuarial theory, practice and modeling. It describes all of the traditional areas of actuarial activity, but in a manner that highlights the fundamental principles of actuarial theory and practice as well as their economic, financial, and statistical foundations.

Modern Actuarial Risk Theory contains what every actuary needs to know about non-life insurance mathematics. It starts with the standard material like utility theory, individual and collective model and basic ruin theory. Other topics are risk measures and premium principles, bonus-malus systems, ordering of risks and credibility theory. It also contains some chapters about Generalized Linear Models, applied to rating and IBNR problems. As to the level of the mathematics, the book would fit in a bachelors or masters program in quantitative economics or mathematical statistics. This second and much expanded edition emphasizes the implementation of these techniques through the use of R. This free but incredibly powerful software is rapidly developing into the de facto standard for statistical computation, not just in academic circles but also in practice. With R, one can do simulations, find maximum likelihood estimators, compute distributions by inverting transforms, and much more.

Apart from standard actuarial theory, Modern Actuarial Risk Theory contains methods that are relevant for actuarial practice, for instance the rating of automobile insurance policies, premium principles and IBNR models, as well as generalized linear models with an eye on actuarial applications. Furthermore extensive introductions are given to credibility theory and ordering of risks. The book reflects the state of the art in actuarial risk theory. In addition to some chapters which are compatible with official material of actuarial education in North-America, Europe and other parts of the world, the book contains important material on topics that are relevant for recent insurance and actuarial developments including determining solvency measures, fair-value computations, reserving, ranking of risks, modelling dependencies and the use of generalized linear models. Basic ideas on risk measures in the framework of insurance premiums are also considered. The numerous exercises contained in Modern Actuarial Risk Theory, together with the hints for solving the more difficult ones and the numerical answers to many others, make the book useful as a textbook. Some important practical paradigms in insurance are presented in a way that is appealing to actuaries in their daily business. The mathematical background assumed is on a level such as acquired in the first stage of a bachelors program in quantitative economics or mathematical statistics.

R Programming for Actuarial Science Professional resource providing an introduction to R coding for actuarial and financial mathematics applications, with real-life examples R Programming for Actuarial Science provides a grounding in R programming applied to the mathematical and statistical methods that are of relevance for actuarial work. In R Programming for Actuarial Science, readers will find: Basic theory for each chapter to complement other actuarial textbooks which provide foundational theory in depth. Topics covered include compound interest, statistical inference, asset-liability matching, time series, loss distributions, contingencies, mortality models, and option pricing plus many more typically covered in university courses. More than 400 coding examples and exercises, most with solutions, to enable students to gain a better understanding of underlying mathematical and statistical principles. An overall basic to intermediate level of coverage in respect of numerous actuarial applications, and real-life examples included with every topic. Providing a highly useful combination of practical discussion and basic theory, R Programming for Actuarial Science is an essential reference for BSc/MSc students in actuarial science, trainee actuaries studying privately, and qualified actuaries with little programming experience, along with undergraduate students studying finance, business, and economics.

The book contains important material on topics that are relevant for recent insurance and actuarial developments including determining solvency measures, fair-value computations, reserving, ranking of risks, modelling dependencies and the use of generalized linear models. Numerous exercises and the hints for solving them make the book useful as a textbook. Practical paradigms in insurance are presented in a way that is appealing to actuaries in their daily business.

Understand Up-to-Date Statistical Techniques for Financial and Actuarial ApplicationsSince the first edition was published, statistical techniques, such as reliability measurement, simulation, regression, and Markov chain modeling, have become more prominent in the financial and actuarial industries. Consequently, practitioners and students must ac