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Modern mortality modelling for actuaries and actuarial students, with example R code, to unlock the potential of individual data.
The field of financial mathematics has developed tremendously over the past thirty years, and the underlying models that have taken shape in interest rate markets and bond markets, being much richer in structure than equity-derivative models, are particularly fascinating and complex. This book introduces the tools required for the arbitrage-free modelling of the dynamics of these markets. Andrew Cairns addresses not only seminal works but also modern developments. Refreshingly broad in scope, covering numerical methods, credit risk, and descriptive models, and with an approachable sequence of opening chapters, Interest Rate Models will make readers--be they graduate students, academics, or practitioners--confident enough to develop their own interest rate models or to price nonstandard derivatives using existing models. The mathematical chapters begin with the simple binomial model that introduces many core ideas. But the main chapters work their way systematically through all of the main developments in continuous-time interest rate modelling. The book describes fully the broad range of approaches to interest rate modelling: short-rate models, no-arbitrage models, the Heath-Jarrow-Morton framework, multifactor models, forward measures, positive-interest models, and market models. Later chapters cover some related topics, including numerical methods, credit risk, and model calibration. Significantly, the book develops the martingale approach to bond pricing in detail, concentrating on risk-neutral pricing, before later exploring recent advances in interest rate modelling where different pricing measures are important.
This open access book collects expert contributions on actuarial modelling and related topics, from machine learning to legal aspects, and reflects on possible insurance designs during an epidemic/pandemic. Starting by considering the impulse given by COVID-19 to the insurance industry and to actuarial research, the text covers compartment models, mortality changes during a pandemic, risk-sharing in the presence of low probability events, group testing, compositional data analysis for detecting data inconsistencies, behaviouristic aspects in fighting a pandemic, and insurers' legal problems, amongst others. Concluding with an essay by a practicing actuary on the applicability of the methods proposed, this interdisciplinary book is aimed at actuaries as well as readers with a background in mathematics, economics, statistics, finance, epidemiology, or sociology.
This book is ideal for practicing experts in particular actuaries in the field of property-casualty insurance, life insurance, reinsurance and insurance supervision, as well as teachers and students. It provides an exploration of Credibility Theory, covering most aspects of this topic from the simplest case to the most detailed dynamic model. The book closely examines the tasks an actuary encounters daily: estimation of loss ratios, claim frequencies and claim sizes.
This book presents the latest theories and methods of reliability and quality, with emphasis on reliability and quality in design and modelling. Each chapter is written by active researchers and professionals with international reputations, providing material which bridges the gap between theory and practice to trigger new practices and research challenges. The book therefore provides a state-of-the-art survey of reliability and quality in design and practices.
Actuarial Principles: Lifetables and Mortality Models explores the core of actuarial science: the study of mortality and other risks and applications. Including the CT4 and CT5 UK courses, but applicable to a global audience, this work lightly covers the mathematical and theoretical background of the subject to focus on real life practice. It offers a brief history of the field, why actuarial notation has become universal, and how theory can be applied to many situations. Uniquely covering both life contingency risks and survival models, the text provides numerous exercises (and their solutions), along with complete self-contained real-world assignments. - Provides detailed coverage of life contingency risks and survival models - Presents self-contained chapters with coverage of key topics from both practitioner and theoretical viewpoints - Includes numerous real world exercises that are accompanied by enlightening solutions - Covers useful background information on how and why the subject has evolved and developed
This book trains the next generation of scientists representing different disciplines to leverage the data generated during routine patient care. It formulates a more complete lexicon of evidence-based recommendations and support shared, ethical decision making by doctors with their patients. Diagnostic and therapeutic technologies continue to evolve rapidly, and both individual practitioners and clinical teams face increasingly complex ethical decisions. Unfortunately, the current state of medical knowledge does not provide the guidance to make the majority of clinical decisions on the basis of evidence. The present research infrastructure is inefficient and frequently produces unreliable results that cannot be replicated. Even randomized controlled trials (RCTs), the traditional gold standards of the research reliability hierarchy, are not without limitations. They can be costly, labor intensive, and slow, and can return results that are seldom generalizable to every patient population. Furthermore, many pertinent but unresolved clinical and medical systems issues do not seem to have attracted the interest of the research enterprise, which has come to focus instead on cellular and molecular investigations and single-agent (e.g., a drug or device) effects. For clinicians, the end result is a bit of a “data desert” when it comes to making decisions. The new research infrastructure proposed in this book will help the medical profession to make ethically sound and well informed decisions for their patients.
In light of recent evidence on the relationship of ozone to mortality and questions about its implications for benefit analysis, the Environmental Protection Agency asked the National Research Council to establish a committee of experts to evaluate independently the contributions of recent epidemiologic studies to understanding the size of the ozone-mortality effect in the context of benefit analysis. The committee was also asked to assess methods for estimating how much a reduction in short-term exposure to ozone would reduce premature deaths, to assess methods for estimating associated increases in life expectancy, and to assess methods for estimating the monetary value of the reduced risk of premature death and increased life expectancy in the context of health-benefits analysis. Estimating Mortality Risk Reduction and Economic Benefits from Controlling Ozone Air Pollution details the committee's findings and posits several recommendations to address these issues.
During the last 25 years, life expectancy at age 50 in the United States has been rising, but at a slower pace than in many other high-income countries, such as Japan and Australia. This difference is particularly notable given that the United States spends more on health care than any other nation. Concerned about this divergence, the National Institute on Aging asked the National Research Council to examine evidence on its possible causes. According to Explaining Divergent Levels of Longevity in High-Income Countries, the nation's history of heavy smoking is a major reason why lifespans in the United States fall short of those in many other high-income nations. Evidence suggests that current obesity levels play a substantial part as well. The book reports that lack of universal access to health care in the U.S. also has increased mortality and reduced life expectancy, though this is a less significant factor for those over age 65 because of Medicare access. For the main causes of death at older ages-cancer and cardiovascular disease-available indicators do not suggest that the U.S. health care system is failing to prevent deaths that would be averted elsewhere. In fact, cancer detection and survival appear to be better in the U.S. than in most other high-income nations, and survival rates following a heart attack also are favorable. Explaining Divergent Levels of Longevity in High-Income Countries identifies many gaps in research. For instance, while lung cancer deaths are a reliable marker of the damage from smoking, no clear-cut marker exists for obesity, physical inactivity, social integration, or other risks considered in this book. Moreover, evaluation of these risk factors is based on observational studies, which-unlike randomized controlled trials-are subject to many biases.