A History of British Actuarial Thought PDF Download

Author: Craig Turnbull
Publisher: Springer
ISBN: 3319331833
Category : Business & Economics
Languages : en
Pages : 350

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Book Description
In the first book of its kind, Turnbull traces the development and implementation of actuarial ideas, from the conception of Equitable Life in the mid-18th century to the start of the 21st century. This book analyses the historical development of British actuarial thought in each of its three main practice areas of life assurance, pensions and general insurance. It discusses how new actuarial approaches were developed within each practice area, and how these emerging ideas interacted with each other and were often driven by common external factors such as shocks in the economic environment, new intellectual ideas from academia and developments in technology. A broad range of historically important actuarial topics are discussed such as the development of the blueprint for the actuarial management of with-profit business; historical developments in mortality modelling methods; changes in actuarial thinking on investment strategy for life and pensions business; changing perspectives on the objectives and methods for funding Defined Benefit pensions; the application of risk theory in general insurance reserving; the adoption of risk-based reserving and the Guaranteed Annuity Option crisis at the end of the 20th century. This book also provides an historical overview of some of the most important external contributions to actuarial thinking: in particular, the first century or so of modern thinking on probability and statistics, starting in the 1650s with Pascal and Fermat; and the developments in the field of financial economics over the third quarter of the twentieth century. This book identifies where historical actuarial thought heuristically anticipated some of the fundamental ideas of modern finance, and the challenges that the profession wrestled with in reconciling these ideas with traditional actuarial methods. Actuaries have played a profoundly influential role in the management of the United Kingdom’s most important long-term financial institutions over the last two hundred years. This book will be the first to chart the influence of the actuarial profession to modern day. It will prove a valuable resource for actuaries, actuarial trainees and students of actuarial science. It will also be of interest to academics and professionals in related financial fields such as accountants, statisticians, economists and investment managers.

Author: Nicola Bruton Bennetts
Publisher: University of Wales Press
ISBN: 178683619X
Category : History
Languages : en
Pages : 290

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Book Description
To meet William Morgan is to encounter the eighteenth-century world of finance, science and politics. Born in Bridgend in 1750, his heritage was Welsh but his influence extended far beyond national borders, and the legacy of his work continues to shape life in the twenty-first century. Aged only twenty-five and with no formal training, Morgan became actuary at the Equitable, which was then a fledgling life assurance company. Known today as ‘the father of the actuarial profession’, his pioneering work earned him the Copley Medal, the Royal Society’s most prestigious award. His interests covered a wider scientific field, and his papers on electrical experiments show that he unwittingly constructed the first X-ray tube. Politically radical, Morgan’s outspoken views put him at risk of imprisonment during Pitt’s Reign of Terror. This biography, using unpublished family letters, explores Morgan’s turbulent private life and covers his outstanding public achievements. ‘William spent 56 years at the Equitable Life Assurance Company, where he learnt how to understand and manage financial risk. In 1789, for his work on the mathematics of life assurance, he was awarded the Copley Medal, the Royal Society’s most prestigious decoration. Subsequent generations have hailed him as ‘the father of the actuarial profession’ – recognition of his having established many of the rules and standards on which the science is based.’ Read more about this on page 6 of the Booklaunch https://edition.pagesuite-professional.co.uk/html5/reader/production/default.aspx?pubname=&edid=eacd7c66-df5c-4335-86ee-cad05c826bda

Author: Arjen van der Heide
Publisher: Policy Press
ISBN: 1529221358
Category : Life insurance
Languages : en
Pages : 208

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Book Description
Insurance is an important - if still poorly understood - mechanism for dealing with a broad variety of risks associated with modern life. This book conducts an in-depth examination of one of the largest and longest-established private insurance industries in Europe: British life insurance. In doing so, it draws on over 40 oral history interviews to trace how the sector has changed since the 1970s, a period characterized by rampant financialization and neoliberalization. Combining insights from science and technology studies and economic sociology, this is an unprecedented study of the evolution of insurance practices and an invaluable contribution to our understanding of financial capitalism.

Author: Mark Kingwell
Publisher: Biblioasis
ISBN: 177196393X
Category : Philosophy
Languages : en
Pages : 137

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Book Description
With COVID-19 comes a heightened sense of everyday risk. How should a society manage, distribute, and conceive of it? As we cope with the lengthening effects of the global COVID-19 pandemic, considerations of everyday risk have been more pressing, and inescapable. In the past, everyone engaged in some degree of risky behaviour, from mundane realities like taking a shower or getting into a car to purposely thrill-seeking activities like rock-climbing or BASE jumping. Many activities that seemed high-risk, such as flying, were claimed basically safe. But risk was, and always has been, a fact of life. With new focus on the risks of even leaving the safety of our homes, it’s time for a deeper consideration of risk itself. How do we manage and distribute risks? How do we predict uncertain outcomes? If risk can never be completely eliminated, can it perhaps be controlled? At the heart of these questions—which govern everything from waking up each day to the abstract mathematics of actuarial science—lie philosophical issues of life, death, and danger. Mortality is the event-horizon of daily risk. How should we conceive of it?

Author: Richard H. Steckel
Publisher: Cambridge University Press
ISBN: 1108390048
Category : Social Science
Languages : en
Pages : 480

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Book Description
Using human skeletal remains, this volume traces health, workload and violence in the European population over the past 2,000 years. Health was surprisingly good for people who lived during the early Medieval Period. The Plague of Justinian of the sixth century was ultimately beneficial for health because the smaller population had relatively more resources that contributed to better living conditions. Increasing population density and inequality in the following centuries imposed an unhealthy diet - poor in protein - on the European population. With the onset of the Little Ice Age in the late Middle Ages, a further health decline ensued, which was not reversed until the nineteenth century. While some aspects of health declined, other attributes improved. During the early modern period, interpersonal violence (outside of warfare) declined possibly because stronger states and institutions were able to enforce compromise and cooperation. European health over the past two millennia was hence multifaceted in nature.

Author: Igor Tulchinsky
Publisher: MIT Press
ISBN: 026204773X
Category : Business & Economics
Languages : en
Pages : 232

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Book Description
The power of the ever-increasing tools and algorithms for prediction and their paradoxical effects on risk. The Age of Prediction is about two powerful, and symbiotic, trends: the rapid development and use of artificial intelligence and big data to enhance prediction, as well as the often paradoxical effects of these better predictions on our understanding of risk and the ways we live. Beginning with dramatic advances in quantitative investing and precision medicine, this book explores how predictive technology is quietly reshaping our world in fundamental ways, from crime fighting and warfare to monitoring individual health and elections. As prediction grows more robust, it also alters the nature of the accompanying risk, setting up unintended and unexpected consequences. The Age of Prediction details how predictive certainties can bring about complacency or even an increase in risks—genomic analysis might lead to unhealthier lifestyles or a GPS might encourage less attentive driving. With greater predictability also comes a degree of mystery, and the authors ask how narrower risks might affect markets, insurance, or risk tolerance generally. Can we ever reduce risk to zero? Should we even try? This book lays an intriguing groundwork for answering these fundamental questions and maps out the latest tools and technologies that power these projections into the future, sometimes using novel, cross-disciplinary tools to map out cancer growth, people’s medical risks, and stock dynamics.

Author: Antti Ilmanen
Publisher: John Wiley & Sons
ISBN: 1119860199
Category : Business & Economics
Languages : en
Pages : 310

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Book Description
Elevate your game in the face of challenging market conditions with this eye-opening guide to portfolio management Investing Amid Low Expected Returns: Making the Most When Markets Offer the Least provides an evidence-based blueprint for successful investing when decades of market tailwinds are turning into headwinds. For a generation, falling yields and soaring asset prices have boosted realized returns. However, this past windfall leaves retirement savers and investors now facing the prospect of record-low future expected returns. Emphasizing this pressing challenge, the book highlights the role that timeless investment practices – discipline, humility, and patience – will play in enabling investment success. It then assesses current investor practices and the body of empirical evidence to illuminate the building blocks for improving long-run returns in today’s environment and beyond. It concludes by reviewing how to put them together through effective portfolio construction, risk management, and cost control practices. In this book, readers will also find: The common investor responses so far to the low expected return challenge Extensive empirical evidence on the critical ingredients of an effective portfolio: major asset class premia, illiquidity premia, style premia, and alpha Discussions of the pros and cons of illiquid investments, factor investing, ESG investing, risk mitigation strategies, and market timing Coverage of the whole top-down investment process – throughout the book endorsing humility in tactical forecasting and boldness in diversification Ideal for institutional and active individual investors, Investing Amid Low Expected Returns is a timeless resource that enables investing with serenity even in harsher financial conditions.

Author: Sam Chen
Publisher: John Wiley & Sons
ISBN: 1119863376
Category : Business & Economics
Languages : en
Pages : 823

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Book Description
An essential introduction to data analytics and Machine Learning techniques in the business sector In Financial Data Analytics with Machine Learning, Optimization and Statistics, a team consisting of a distinguished applied mathematician and statistician, experienced actuarial professionals and working data analysts delivers an expertly balanced combination of traditional financial statistics, effective machine learning tools, and mathematics. The book focuses on contemporary techniques used for data analytics in the financial sector and the insurance industry with an emphasis on mathematical understanding and statistical principles and connects them with common and practical financial problems. Each chapter is equipped with derivations and proofs—especially of key results—and includes several realistic examples which stem from common financial contexts. The computer algorithms in the book are implemented using Python and R, two of the most widely used programming languages for applied science and in academia and industry, so that readers can implement the relevant models and use the programs themselves. The book begins with a brief introduction to basic sampling theory and the fundamentals of simulation techniques, followed by a comparison between R and Python. It then discusses statistical diagnosis for financial security data and introduces some common tools in financial forensics such as Benford's Law, Zipf's Law, and anomaly detection. The statistical estimation and Expectation-Maximization (EM) & Majorization-Minimization (MM) algorithms are also covered. The book next focuses on univariate and multivariate dynamic volatility and correlation forecasting, and emphasis is placed on the celebrated Kelly's formula, followed by a brief introduction to quantitative risk management and dependence modelling for extremal events. A practical topic on numerical finance for traditional option pricing and Greek computations immediately follows as well as other important topics in financial data-driven aspects, such as Principal Component Analysis (PCA) and recommender systems with their applications, as well as advanced regression learners such as kernel regression and logistic regression, with discussions on model assessment methods such as simple Receiver Operating Characteristic (ROC) curves and Area Under Curve (AUC) for typical classification problems. The book then moves on to other commonly used machine learning tools like linear classifiers such as perceptrons and their generalization, the multilayered counterpart (MLP), Support Vector Machines (SVM), as well as Classification and Regression Trees (CART) and Random Forests. Subsequent chapters focus on linear Bayesian learning, including well-received credibility theory in actuarial science and functional kernel regression, and non-linear Bayesian learning, such as the Naïve Bayes classifier and the Comonotone-Independence Bayesian Classifier (CIBer) recently independently developed by the authors and used successfully in InsurTech. After an in-depth discussion on cluster analyses such as K-means clustering and its inversion, the K-nearest neighbor (KNN) method, the book concludes by introducing some useful deep neural networks for FinTech, like the potential use of the Long-Short Term Memory model (LSTM) for stock price prediction. This book can help readers become well-equipped with the following skills: To evaluate financial and insurance data quality, and use the distilled knowledge obtained from the data after applying data analytic tools to make timely financial decisions To apply effective data dimension reduction tools to enhance supervised learning To describe and select suitable data analytic tools as introduced above for a given dataset depending upon classification or regression prediction purpose The book covers the competencies tested by several professional examinations, such as the Predictive Analytics Exam offered by the Society of Actuaries, and the Institute and Faculty of Actuaries' Actuarial Statistics Exam. Besides being an indispensable resource for senior undergraduate and graduate students taking courses in financial engineering, statistics, quantitative finance, risk management, actuarial science, data science, and mathematics for AI, Financial Data Analytics with Machine Learning, Optimization and Statistics also belongs in the libraries of aspiring and practicing quantitative analysts working in commercial and investment banking.

Author: Vladimir Panov
Publisher: Springer
ISBN: 331965313X
Category : Mathematics
Languages : en
Pages : 506

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Book Description
This book brings together the latest findings in the area of stochastic analysis and statistics. The individual chapters cover a wide range of topics from limit theorems, Markov processes, nonparametric methods, acturial science, population dynamics, and many others. The volume is dedicated to Valentin Konakov, head of the International Laboratory of Stochastic Analysis and its Applications on the occasion of his 70th birthday. Contributions were prepared by the participants of the international conference of the international conference “Modern problems of stochastic analysis and statistics”, held at the Higher School of Economics in Moscow from May 29 - June 2, 2016. It offers a valuable reference resource for researchers and graduate students interested in modern stochastics.

Author: Aubrey Clayton
Publisher: Columbia University Press
ISBN: 0231553358
Category : Mathematics
Languages : en
Pages : 641

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Book Description
There is a logical flaw in the statistical methods used across experimental science. This fault is not a minor academic quibble: it underlies a reproducibility crisis now threatening entire disciplines. In an increasingly statistics-reliant society, this same deeply rooted error shapes decisions in medicine, law, and public policy with profound consequences. The foundation of the problem is a misunderstanding of probability and its role in making inferences from observations. Aubrey Clayton traces the history of how statistics went astray, beginning with the groundbreaking work of the seventeenth-century mathematician Jacob Bernoulli and winding through gambling, astronomy, and genetics. Clayton recounts the feuds among rival schools of statistics, exploring the surprisingly human problems that gave rise to the discipline and the all-too-human shortcomings that derailed it. He highlights how influential nineteenth- and twentieth-century figures developed a statistical methodology they claimed was purely objective in order to silence critics of their political agendas, including eugenics. Clayton provides a clear account of the mathematics and logic of probability, conveying complex concepts accessibly for readers interested in the statistical methods that frame our understanding of the world. He contends that we need to take a Bayesian approach—that is, to incorporate prior knowledge when reasoning with incomplete information—in order to resolve the crisis. Ranging across math, philosophy, and culture, Bernoulli’s Fallacy explains why something has gone wrong with how we use data—and how to fix it.