Bøger i International Series on Actuarial Science serien

This book provides a coherent and readable introduction to quantitative and qualitative enterprise risk management. For risk professionals, undergraduates, and anyone preparing for ERM actuarial exams, it combines an in-depth presentation of key mathematical and statistical methods with abundant real-world cases, worked examples, and exercises.

This must-have manual provides solutions to all exercises in the authors' groundbreaking text, which is required reading for the SOA Exam MLC, and covers virtually the whole syllabus for the UK Subject CT5 exam. Over 200 solutions give insight as well as exam preparation. Companion spreadsheets are freely available online.

Actuaries modelling mortality have, until now, mostly used methods based on aggregate data. This book explains to practitioners how to build and test models based on the individual person, with plenty of example R code. Students will also find it helpful in preparation for their professional examinations.

This is a comprehensive reference source covering the key material on this subject, including both theory and practice. It is aimed at students and experienced reserving practitioners, but also recommended for anyone with an interest in understanding better how general insurers establish one of the most critical items in their accounts - the claims reserves.

Ideal for a first course in insurance risk theory. Includes numerous worked examples and exercises for which solutions are provided.

Three leaders in actuarial science give a modern perspective on life contingencies. Balancing rigour and intuition, and emphasizing applications, this modern text is ideal for university courses and actuarial exam preparation. This second edition includes brand new chapters and exercises, and will prepare students for the new-style MLC exam.

This book gives actuarial and finance students a foundation in multiple regression and time series, and discusses advanced statistical topics that are relevant to actuarial and financial practice. It uses statistical techniques to analyze real data in risk management and finance. No specific knowledge of these areas is presumed.

Covers the full range of qualitative and quantitative techniques needed to manage risk in a financial organisation.

This is the only book actuaries need to understand generalized linear models (GLMs) for insurance applications. GLMs are used in the insurance industry to support critical decisions. Until now, no text has introduced GLMs in this context or addressed the problems specific to insurance data. Using insurance data sets, this practical, rigorous book treats GLMs, covers all standard exponential family distributions, extends the methodology to correlated data structures, and discusses recent developments which go beyond the GLM. The issues in the book are specific to insurance data, such as model selection in the presence of large data sets and the handling of varying exposure times. Exercises and data-based practicals help readers to consolidate their skills, with solutions and data sets given on the companion website. Although the book is package-independent, SAS code and output examples feature in an appendix and on the website. In addition, R code and output for all the examples are provided on the website.

In classical life insurance mathematics the obligations of the insurance company towards the policy holders were calculated on artificial conservative assumptions on mortality and interest rates. However, this approach is being superseded by developments in international accounting and solvency standards coupled with other advances enabling a market-based valuation of risk, i.e., its price if traded in a free market. The book describes these approaches, and is the first to explain them in conjunction with more traditional methods. The various chapters address specific aspects of market-based valuation. The exposition integrates methods and results from financial and insurance mathematics, and is based on the entries in a life insurance company's market accounting scheme. The book will be of great interest and use to students and practitioners who need an introduction to this area, and who seek a practical yet sound guide to life insurance accounting and product development.

Predictive modeling involves the use of data to forecast future events. Building on the foundations developed in the first volume, Volume 2 examines applications of predictive modeling, focusing on property and casualty insurance, exposing readers to a variety of techniques in real-life contexts that demonstrate the value of predictive modeling.

This book is for actuaries and financial analysts developing their expertise in statistics and who wish to become familiar with concrete examples of predictive modeling.

This book presents a wide range of statistical and probabilistic topics to give students a firm foundation in actuarial concepts and their applications. It covers much of the international syllabuses for professional actuarial examinations in risk models, but goes much further with detailed case studies, exercises and help using R.

This practical introduction outlines methods for analysing actuarial and financial risk at a fairly elementary mathematical level suitable for graduate students, actuaries and other analysts in the industry who could use simulation as a problem solver. Numerous exercises with R-code illustrate the text.

A concise yet comprehensive guide to the mathematics of portfolio theory from a modelling perspective, with discussion of the assumptions, limitations and implementations of the models as well as the theory underlying them. Aimed at advanced undergraduates, this book can be used for self-study or as a course text.