Modelling Mortality with Actuarial Applications

Modelling Mortality with Actuarial Applications

Modern mortality modelling for actuaries and actuarial students, with example R code, to unlock the potential of individual data.

Author: Angus S. Macdonald

Publisher: Cambridge University Press

ISBN: 9781107045415

Category: Business & Economics

Page: 387

View: 959

Modern mortality modelling for actuaries and actuarial students, with example R code, to unlock the potential of individual data.
Categories: Business & Economics

Modelling Mortality with Actuarial Applications

Modelling Mortality with Actuarial Applications

INTERNATIONAL SERIES ON ACTUARIAL SCIENCE Editorial Board Christopher Daykin (Independent Consultant and Actuary) Angus Macdonald (Heriot-Watt University) The International Series on Actuarial Science, published by Cambridge University ...

Author: Angus S. Macdonald

Publisher: Cambridge University Press

ISBN: 9781108686334

Category: Mathematics

Page:

View: 656

Actuaries have access to a wealth of individual data in pension and insurance portfolios, but rarely use its full potential. This book will pave the way, from methods using aggregate counts to modern developments in survival analysis. Based on the fundamental concept of the hazard rate, Part I shows how and why to build statistical models, based on data at the level of the individual persons in a pension scheme or life insurance portfolio. Extensive use is made of the R statistics package. Smooth models, including regression and spline models in one and two dimensions, are covered in depth in Part II. Finally, Part III uses multiple-state models to extend survival models beyond the simple life/death setting, and includes a brief introduction to the modern counting process approach. Practising actuaries will find this book indispensable, and students will find it helpful when preparing for their professional examinations.
Categories: Mathematics

Actuarial Mathematics for Life Contingent Risks

Actuarial Mathematics for Life Contingent Risks

INTERNATIONAL SERIES ON ACTUARIAL SCIENCE Editorial Board Christopher Daykin (Independent Consultant and Actuary) Angus ... Recent titles include the following: Modelling Mortality with Actuarial Applications Angus S. Macdonald, ...

Author: David C. M. Dickson

Publisher: Cambridge University Press

ISBN: 9781108787406

Category: Business & Economics

Page:

View: 372

The substantially updated third edition of the popular Actuarial Mathematics for Life Contingent Risks is suitable for advanced undergraduate and graduate students of actuarial science, for trainee actuaries preparing for professional actuarial examinations, and for life insurance practitioners who wish to increase or update their technical knowledge. The authors provide intuitive explanations alongside mathematical theory, equipping readers to understand the material in sufficient depth to apply it in real-world situations and to adapt their results in a changing insurance environment. Topics include modern actuarial paradigms, such as multiple state models, cash-flow projection methods and option theory, all of which are required for managing the increasingly complex range of contemporary long-term insurance products. Numerous exam-style questions allow readers to prepare for traditional professional actuarial exams, and extensive use of Excel ensures that readers are ready for modern, Excel-based exams and for the actuarial work environment. The Solutions Manual (ISBN 9781108747615), available for separate purchase, provides detailed solutions to the text's exercises.
Categories: Business & Economics

Solutions Manual for Actuarial Mathematics for Life Contingent Risks

Solutions Manual for Actuarial Mathematics for Life Contingent Risks

INTERNATIONAL SERIES ON ACTUARIAL SCIENCE Editorial Board Christopher Daykin (Independent Consultant and Actuary) Angus Macdonald ... Mary R. Hardy & Howard R. Waters Modelling Mortality with Actuarial Applications Angus S. Macdonald, ...

Author: David C. M. Dickson

Publisher: Cambridge University Press

ISBN: 9781108804523

Category: Business & Economics

Page:

View: 756

This must-have manual provides detailed solutions to all of the 300 exercises in Dickson, Hardy and Waters' Actuarial Mathematics for Life Contingent Risks, 3 edition. This groundbreaking text on the modern mathematics of life insurance is required reading for the Society of Actuaries' (SOA) LTAM Exam. The new edition treats a wide range of newer insurance contracts such as critical illness and long-term care insurance; pension valuation material has been expanded; and two new chapters have been added on developing models from mortality data and on changing mortality. Beyond professional examinations, the textbook and solutions manual offer readers the opportunity to develop insight and understanding through guided hands-on work, and also offer practical advice for solving problems using straightforward, intuitive numerical methods. Companion Excel spreadsheets illustrating these techniques are available for free download.
Categories: Business & Economics

Quantitative Enterprise Risk Management

Quantitative Enterprise Risk Management

INTERNATIONAL. SERIES. ON. ACTUARIAL. SCIENCE. Editorial Board Christopher Daykin (Independent Consultant and Actuary) Angus ... Mary R. Hardy & Howard R. Waters Modelling Mortality with Actuarial Applications Angus S. Macdonald, ...

Author: Mary R. Hardy

Publisher: Cambridge University Press

ISBN: 9781009098465

Category: Business & Economics

Page: 689

View: 802

This relevant, readable text integrates quantitative and qualitative approaches, connecting key mathematical tools to real-world challenges.
Categories: Business & Economics

Predictive Modeling Applications in Actuarial Science

Predictive Modeling Applications in Actuarial Science

Proceedings of the Casualty Actuarial Society 37, 7–23. Banerjee, S., B. Carlin, and A. Gelfand ... Bayesian stochastic mortality modelling for two populations. ASTIN Bulletin 41(1), 29–59. ... Huebner International Series on Risk, ...

Author: Edward W. Frees

Publisher: Cambridge University Press

ISBN: 9781107029873

Category: Business & Economics

Page: 565

View: 573

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.
Categories: Business & Economics

Predictive Modeling Applications in Actuarial Science Volume 1 Predictive Modeling Techniques

Predictive Modeling Applications in Actuarial Science  Volume 1  Predictive Modeling Techniques

Proceedings of the Casualty Actuarial Society 37, 7–23. Banerjee, S., B. Carlin, and A. Gelfand ... Bayesian stochastic mortality modelling for two populations. ... Huebner International Series on Risk, Insurance, and Economic Security.

Author: Edward W. Frees

Publisher: Cambridge University Press

ISBN: 9781139992312

Category: Business & Economics

Page:

View: 397

Predictive modeling involves the use of data to forecast future events. It relies on capturing relationships between explanatory variables and the predicted variables from past occurrences and exploiting this to predict future outcomes. Forecasting future financial events is a core actuarial skill - actuaries routinely apply predictive-modeling techniques in insurance and other risk-management applications. This book is for actuaries and other financial analysts who are developing their expertise in statistics and wish to become familiar with concrete examples of predictive modeling. The book also addresses the needs of more seasoned practising analysts who would like an overview of advanced statistical topics that are particularly relevant in actuarial practice. Predictive Modeling Applications in Actuarial Science emphasizes lifelong learning by developing tools in an insurance context, providing the relevant actuarial applications, and introducing advanced statistical techniques that can be used by analysts to gain a competitive advantage in situations with complex data.
Categories: Business & Economics

Effective Statistical Learning Methods for Actuaries III

Effective Statistical Learning Methods for Actuaries III

This book reviews some of the most recent developments in neural networks, with a focus on applications in actuarial sciences and finance.

Author: Michel Denuit

Publisher: Springer Nature

ISBN: 9783030258276

Category: Business & Economics

Page: 250

View: 681

This book reviews some of the most recent developments in neural networks, with a focus on applications in actuarial sciences and finance. It simultaneously introduces the relevant tools for developing and analyzing neural networks, in a style that is mathematically rigorous yet accessible. Artificial intelligence and neural networks offer a powerful alternative to statistical methods for analyzing data. Various topics are covered from feed-forward networks to deep learning, such as Bayesian learning, boosting methods and Long Short Term Memory models. All methods are applied to claims, mortality or time-series forecasting. Requiring only a basic knowledge of statistics, this book is written for masters students in the actuarial sciences and for actuaries wishing to update their skills in machine learning. This is the third of three volumes entitled Effective Statistical Learning Methods for Actuaries. Written by actuaries for actuaries, this series offers a comprehensive overview of insurance data analytics with applications to P&C, life and health insurance. Although closely related to the other two volumes, this volume can be read independently.
Categories: Business & Economics

Computational Actuarial Science with R

Computational Actuarial Science with R

Daniel, J.W. (2004), Multi-state transition models with actuarial applications, ... methods in the graduation of mortality: Application to data from the Valencia region (Spain), International Statistical Review 74(2), 215–233.

Author: Arthur Charpentier

Publisher: CRC Press

ISBN: 9781466592599

Category: Business & Economics

Page: 652

View: 174

A Hands-On Approach to Understanding and Using Actuarial Models Computational Actuarial Science with R provides an introduction to the computational aspects of actuarial science. Using simple R code, the book helps you understand the algorithms involved in actuarial computations. It also covers more advanced topics, such as parallel computing and C/C++ embedded codes. After an introduction to the R language, the book is divided into four parts. The first one addresses methodology and statistical modeling issues. The second part discusses the computational facets of life insurance, including life contingencies calculations and prospective life tables. Focusing on finance from an actuarial perspective, the next part presents techniques for modeling stock prices, nonlinear time series, yield curves, interest rates, and portfolio optimization. The last part explains how to use R to deal with computational issues of nonlife insurance. Taking a do-it-yourself approach to understanding algorithms, this book demystifies the computational aspects of actuarial science. It shows that even complex computations can usually be done without too much trouble. Datasets used in the text are available in an R package (CASdatasets).
Categories: Business & Economics

Statistical Foundations of Actuarial Learning and its Applications

Statistical Foundations of Actuarial Learning and its Applications

Time-series forecasting of mortality rates using deep learning. ... AI in actuarial science - A review of recent advances - Part 2. ... Mind the gap - Safely incorporating deep learning models into the actuarial toolkit.

Author: Mario V. Wüthrich

Publisher: Springer Nature

ISBN: 9783031124099

Category: Mathematics

Page: 611

View: 345

This open access book discusses the statistical modeling of insurance problems, a process which comprises data collection, data analysis and statistical model building to forecast insured events that may happen in the future. It presents the mathematical foundations behind these fundamental statistical concepts and how they can be applied in daily actuarial practice. Statistical modeling has a wide range of applications, and, depending on the application, the theoretical aspects may be weighted differently: here the main focus is on prediction rather than explanation. Starting with a presentation of state-of-the-art actuarial models, such as generalized linear models, the book then dives into modern machine learning tools such as neural networks and text recognition to improve predictive modeling with complex features. Providing practitioners with detailed guidance on how to apply machine learning methods to real-world data sets, and how to interpret the results without losing sight of the mathematical assumptions on which these methods are based, the book can serve as a modern basis for an actuarial education syllabus.
Categories: Mathematics