Knowledge of risk models and the assessment of risk is a fundamental part of the training of actuaries and all who are involved in financial, pensions and insurance mathematics. This book provides students and others with a firm foundation in a wide range of statistical and probabilistic methods for the modelling of risk, including short-term risk modelling, model-based pricing, risk-sharing, ruin theory and credibility. It covers much of the international syllabuses for professional actuarial examinations in risk models, but goes into further depth, with worked examples, exercises and detailed case studies. The authors also use the statistical package R to demonstrate how simple code and functions can be used profitably in an actuarial context. The authors' engaging and pragmatic approach, balancing rigour and intuition and developed over many years of teaching the subject, makes this book ideal for self-study or for students taking courses in risk modelling.
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Roger J. Gray was a Senior Lecturer in the School of Mathematical and Computer Sciences at Heriot-Watt University, Edinburgh until his death in 2011.
Susan M. Pitts is a Senior Lecturer in the Statistical Laboratory at the University of Cambridge.
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Hardcover. Condition: Good-. 1st Edition. Hardcover, xiv + 393 pages, NOT ex-library. Neatly removed rear blank endpaper. Wrinkling on last pages, indicative of water-damage. Blank stickers inside the rear board covering the faint staining. Pages remain clean and bright with unmarked text, free of inscriptions and stamps, firmly bound. A long scratch on the lower outer page edges. Small scuff-marks along the edges of the boards, rubbed tips of board corners. Issued without a dust jacket. -- This textbook provides a comprehensive treatment of statistical and probabilistic methods essential to actuarial work in general insurance. The content spans several core areas: models for claim numbers and claim sizes, short-term risk models including compound distributions, model-based pricing and premium calculation principles, risk sharing through reinsurance and deductibles, and ruin theory for the classical risk model. The analysis of claim sizes includes a specific focus on heavy-tailed distributions and their impact on risk. A substantial chapter on credibility theory covers both Bayesian and empirical Bayesian approaches, including the Bühlmann and Bühlmann-Straub models, providing a theoretical basis for experience rating in insurance portfolios. The mathematical treatment assumes familiarity with probability theory, statistical inference and maximum likelihood estimation, though measure theory is not required. Each chapter contains numerous worked examples and exercises, with solutions provided in an appendix. Three extended case studies demonstrate how theoretical concepts apply to practical problems such as comparing premium-setting principles, analysing shared liabilities and optimising reinsurance arrangements. Throughout the text, the authors integrate computational methods using the R statistical programming environment, showing how code can be applied to actuarial calculations including the Panjer recursion algorithm and numerical methods for compound distributions. The coverage aligns with international professional actuarial examination syllabuses in risk models while extending into greater analytical depth, making this work suitable for advanced undergraduate and postgraduate courses in actuarial science as well as independent study by practitioners seeking to strengthen their theoretical foundations in general insurance mathematics. Seller Inventory # 013210
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