The first book for a popular audience on the transformative, democratising technology of 'DeFi'.

After over a decade of Bitcoin, which has now moved beyond lore and hype into an increasingly robust star in the firmament of global assets, a new and more important question has arisen. What happens beyond Bitcoin? The answer is decentralised finance - 'DeFi'.

Tech and finance experts Steven Boykey Sidley and Simon Dingle argue that DeFi - which enables all manner of financial transactions to take place directly, person to person, without the involvement of financial institutions - will redesign the cogs and wheels in the engines of trust, and make the remarkable rise of Bitcoin look quaint by comparison. It will disrupt and displace fine and respectable companies, if not entire industries.

Sidley and Dingle explain how DeFi works, introduce the organisations and individuals that comprise the new industry, and identify the likely winners and losers in the coming revolution.

Written in a highly accessible style, A Factor Model Approach to Derivative Pricing lays a clear and structured foundation for the pricing of derivative securities based upon simple factor model related absence of arbitrage ideas. This unique and unifying approach provides for a broad treatment of topics and models, including equity, interest-rate, and credit derivatives, as well as hedging and tree-based computational methods, but without reliance on the heavy prerequisites that often accompany such topics. Key features A single fundamental absence of arbitrage relationship based on factor models is used to motivate all the results in the book A structured three-step procedure is used to guide the derivation of absence of arbitrage equations and illuminate core underlying concepts Brownian motion and Poisson process driven models are treated together, allowing for a broad and cohesive presentation of topics The final chapter provides a new approach to risk neutral pricing that introduces the topic as a seamless and natural extension of the factor model approach Whether being used as text for an intermediate level course in derivatives, or by researchers and practitioners who are seeking a better understanding of the fundamental ideas that underlie derivative pricing, readers will appreciate the book's ability to unify many disparate topics and models under a single conceptual theme. James A Primbs is an Associate Professor of Finance at the Mihaylo College of Business and Economics at California State University, Fullerton.

Customer and Business Analytics: Applied Data Mining for Business Decision Making Using R explains and demonstrates, via the accompanying open-source software, how advanced analytical tools can address various business problems. It also gives insight into some of the challenges faced when deploying these tools. Extensively classroom-tested, the text is ideal for students in customer and business analytics or applied data mining as well as professionals in small- to medium-sized organizations. The book offers an intuitive understanding of how different analytics algorithms work. Where necessary, the authors explain the underlying mathematics in an accessible manner. Each technique presented includes a detailed tutorial that enables hands-on experience with real data. The authors also discuss issues often encountered in applied data mining projects and present the CRISP-DM process model as a practical framework for organizing these projects. Showing how data mining can improve the performance of organizations, this book and its R-based software provide the skills and tools needed to successfully develop advanced analytics capabilities.

In order to make informed decisions, there are three important elements: intuition, trust, and analytics. Intuition is based on experiential learning and recent research has shown that those who rely on their "gut feelings" may do better than those who don't. Analytics, however, are important in a data-driven environment to also inform decision making. The third element, trust, is critical for knowledge sharing to take place. These three elements-intuition, analytics, and trust-make a perfect combination for decision making. This book gathers leading researchers who explore the role of these three elements in the process of decision-making.

The composition of portfolios is one of the most fundamental and important methods in financial engineering, used to control the risk of investments. This book provides a comprehensive overview of statistical inference for portfolios and their various applications. A variety of asset processes are introduced, including non-Gaussian stationary processes, nonlinear processes, non-stationary processes, and the book provides a framework for statistical inference using local asymptotic normality (LAN). The approach is generalized for portfolio estimation, so that many important problems can be covered. This book can primarily be used as a reference by researchers from statistics, mathematics, finance, econometrics, and genomics. It can also be used as a textbook by senior undergraduate and graduate students in these fields.

This book is about learning from data using the Generalized Additive Models for Location, Scale and Shape (GAMLSS). GAMLSS extends the Generalized Linear Models (GLMs) and Generalized Additive Models (GAMs) to accommodate large complex datasets, which are increasingly prevalent. In particular, the GAMLSS statistical framework enables flexible regression and smoothing models to be fitted to the data. The GAMLSS model assumes that the response variable has any parametric (continuous, discrete or mixed) distribution which might be heavy- or light-tailed, and positively or negatively skewed. In addition, all the parameters of the distribution (location, scale, shape) can be modelled as linear or smooth functions of explanatory variables. Key Features: Provides a broad overview of flexible regression and smoothing techniques to learn from data whilst also focusing on the practical application of methodology using GAMLSS software in R. Includes a comprehensive collection of real data examples, which reflect the range of problems addressed by GAMLSS models and provide a practical illustration of the process of using flexible GAMLSS models for statistical learning. R code integrated into the text for ease of understanding and replication. Supplemented by a website with code, data and extra materials. This book aims to help readers understand how to learn from data encountered in many fields. It will be useful for practitioners and researchers who wish to understand and use the GAMLSS models to learn from data and also for students who wish to learn GAMLSS through practical examples.

'Refreshingly clear and engaging' Tim Harford 'Delightful . . . full of unique insights' Prof Sir David Spiegelhalter There's no getting away from statistics. We encounter them every day. We are all users of statistics whether we like it or not. Do missed appointments really cost the NHS GBP1bn per year? What's the difference between the mean gender pay gap and the median gender pay gap? How can we work out if a claim that we use 42 billion single-use plastic straws per year in the UK is accurate? What did the Vote Leave campaign's GBP350m bus really mean? How can we tell if the headline 'Public pensions cost you GBP4,000 a year' is correct? Does snow really cost the UK economy GBP1bn per day? But how do we distinguish statistical fact from fiction? What can we do to decide whether a number, claim or news story is accurate? Without an understanding of data, we cannot truly understand what is going on in the world around us. Written by Anthony Reuben, the BBC's first head of statistics, Statistical is an accessible and empowering guide to challenging the numbers all around us.

Employers Can Reduce Their Employees' Health Care Costs by Thinking Out of The BoxEmployee health care costs have skyrocketed, especially for small business owners. But employers have options that medical entrepreneurs have crafted to provide all businesses with plans to improve their employees' wellness and reduce their costs. Thus, the cost of employee health care benefits can be reduced markedly by choosing one of numerous alternatives to traditional indemnity policies. The Finance of Health Care provides business decision makers with the information they need to match the optimal health care plan with the culture of their workforce. This book is a must guide for corporate executives and entrepreneurs who want to attract-and keep--the best employees in our competitive economy.

Despite numerous books on research methodology, many have failed to present a complete, hands-on, practical book to lead college classes or individuals through the research process. We are seeing more and more scientific papers from all research fields that fail to meet the basic criteria in terms of research methods, as well as the structure, writing style and presentation of results. This book aims to address this gap in the market by providing an authoritative, easy to follow guide to research methods and how to apply them. Qualitative Methods in Economics is focused not only on the research methods/techniques but also the methodology. The main objective of this book is to discuss qualitative methods and their use in economics and social science research. Chapters identify several of the research approaches commonly used in social studies, from the importance of the role of science through to the techniques of data collection. Using an example research paper to examine the methods used to present the research, the second half of this book breaks down how to present and format your results successfully. This book will be of use to students and researchers who want to improve their research methods and read up on the new and cutting edge advances in research methods, as well as those who like to study ways to improve the research process.

A classic text for accuracy and statistical precision. Statistics for Business and Economics enables readers to conduct serious analysis of applied problems rather than running simple "canned" applications. This text is also at a mathematically higher level than most business statistics texts and provides readers with the knowledge they need to become stronger analysts for future managerial positions. The eighth edition of this book has been revised and updated to provide readers with improved problem contexts for learning how statistical methods can improve their analysis and understanding of business and economics.

Economic evaluation has become an essential component of clinical trial design to show that new treatments and technologies offer value to payers in various healthcare systems. Although many books exist that address the theoretical or practical aspects of cost-effectiveness analysis, this book differentiates itself from the competition by detailing how to apply health economic evaluation techniques in a clinical trial context, from both academic and pharmaceutical/commercial perspectives. It also includes a special chapter for clinical trials in Cancer. Design & Analysis of Clinical Trials for Economic Evaluation & Reimbursement is not just about performing cost-effectiveness analyses. It also emphasizes the strategic importance of economic evaluation and offers guidance and advice on the complex factors at play before, during, and after an economic evaluation. Filled with detailed examples, the book bridges the gap between applications of economic evaluation in industry (mainly pharmaceutical) and what students may learn in university courses. It provides readers with access to SAS and STATA code. In addition, Windows-based software for sample size and value of information analysis is available free of charge-making it a valuable resource for students considering a career in this field or for those who simply wish to know more about applying economic evaluation techniques. The book includes coverage of trial design, case report form design, quality of life measures, sample sizes, submissions to regulatory authorities for reimbursement, Markov models, cohort models, and decision trees. Examples and case studies are provided at the end of each chapter. Presenting first-hand insights into how economic evaluations are performed from a drug development perspective, the book supplies readers with the foundation required to succeed in an environment where clinical trials and cost-effectiveness of new treatments are central. It also includes thought-provoking exercises for use in classroom and seminar discussions.

Random set theory is a fascinating branch of mathematics that amalgamates techniques from topology, convex geometry, and probability theory. Social scientists routinely conduct empirical work with data and modelling assumptions that reveal a set to which the parameter of interest belongs, but not its exact value. Random set theory provides a coherent mathematical framework to conduct identification analysis and statistical inference in this setting and has become a fundamental tool in econometrics and finance. This is the first book dedicated to the use of the theory in econometrics, written to be accessible for readers without a background in pure mathematics. Molchanov and Molinari define the basics of the theory and illustrate the mathematical concepts by their application in the analysis of econometric models. The book includes sets of exercises to accompany each chapter as well as examples to help readers apply the theory effectively.

Principles of Copula Theory explores the state of the art on copulas and provides you with the foundation to use copulas in a variety of applications. Throughout the book, historical remarks and further readings highlight active research in the field, including new results, streamlined presentations, and new proofs of old results. After covering the essentials of copula theory, the book addresses the issue of modeling dependence among components of a random vector using copulas. It then presents copulas from the point of view of measure theory, compares methods for the approximation of copulas, and discusses the Markov product for 2-copulas. The authors also examine selected families of copulas that possess appealing features from both theoretical and applied viewpoints. The book concludes with in-depth discussions on two generalizations of copulas: quasi- and semi-copulas. Although copulas are not the solution to all stochastic problems, they are an indispensable tool for understanding several problems about stochastic dependence. This book gives you the solid and formal mathematical background to apply copulas to a range of mathematical areas, such as probability, real analysis, measure theory, and algebraic structures.

A unique and comprehensive source of information, this book is the only international publication providing economists, planners, policymakers and business people with worldwide statistics on current performance and trends in the manufacturing sector. The Yearbook is designed to facilitate international comparisons relating to manufacturing activity and industrial development and performance. It provides data which can be used to analyse patterns of growth and related long term trends, structural change and industrial performance in individual industries. Statistics on employment patterns, wages, consumption and gross output and other key indicators are also presented.

Quants, physicists working on Wall Street as quantitative analysts, have been widely blamed for triggering financial crises with their complex mathematical models. Their formulas were meant to allow Wall Street to prosper without risk. But in this penetrating insider's look at the recent economic collapse, Emanuel Derman--former head quant at Goldman Sachs--explains the collision between mathematical modeling and economics and what makes financial models so dangerous. Though such models imitate the style of physics and employ the language of mathematics, theories in physics aim for a description of reality--but in finance, models can shoot only for a very limited approximation of reality. Derman uses his firsthand experience in financial theory and practice to explain the complicated tangles that have paralyzed the economy. "Models.Behaving.Badly. "exposes Wall Street's love affair with models, and shows us why nobody will ever be able to write a model that can encapsulate human behavior.

The main objective of politicians is to maximise economic growth, which heavily drives political policy and decision-making. Critics of the maximisation of growth as the central aim of economic policy have argued that growth in itself is not necessarily a good thing, particularly for the environment; however, what would replace the system and how it would be measured are questions that have been rarely answered satisfactorily. First published in 1991, this book was the first to lay out an entirely new set of practical proposals for developing new economic measurement tools, with the aim of being sustainable, 'green' and human-centred. Victor Anderson proposes that a whole set of indicators, rather than a single one, should play all the roles that GNP (Gross National Product) is responsible for. With a detailed overview of the central debates between the advocates and opponents of continued economic growth and an analysis of the various proposals for modification, this title will be of particular value to students interested in the diversity of measurement tools and the notion that economies should also be evaluated by their social and environmental consequences.

A unique and comprehensive source of information, this book is the only international publication providing economists, planners, policymakers and business people with worldwide statistics on current performance and trends in the manufacturing sector. The Yearbook is designed to facilitate international comparisons relating to manufacturing activity and industrial development and performance. It provides data which can be used to analyse patterns of growth and related long term trends, structural change and industrial performance in individual industries. Statistics on employment patterns, wages, consumption and gross output and other key indicators are also presented.

Time Series: A First Course with Bootstrap Starter provides an introductory course on time series analysis that satisfies the triptych of (i) mathematical completeness, (ii) computational illustration and implementation, and (iii) conciseness and accessibility to upper-level undergraduate and M.S. students. Basic theoretical results are presented in a mathematically convincing way, and the methods of data analysis are developed through examples and exercises parsed in R. A student with a basic course in mathematical statistics will learn both how to analyze time series and how to interpret the results. The book provides the foundation of time series methods, including linear filters and a geometric approach to prediction. The important paradigm of ARMA models is studied in-depth, as well as frequency domain methods. Entropy and other information theoretic notions are introduced, with applications to time series modeling. The second half of the book focuses on statistical inference, the fitting of time series models, as well as computational facets of forecasting. Many time series of interest are nonlinear in which case classical inference methods can fail, but bootstrap methods may come to the rescue. Distinctive features of the book are the emphasis on geometric notions and the frequency domain, the discussion of entropy maximization, and a thorough treatment of recent computer-intensive methods for time series such as subsampling and the bootstrap. There are more than 600 exercises, half of which involve R coding and/or data analysis. Supplements include a website with 12 key data sets and all R code for the book's examples, as well as the solutions to exercises.

Economic Time Series: Modeling and Seasonality is a focused resource on analysis of economic time series as pertains to modeling and seasonality, presenting cutting-edge research that would otherwise be scattered throughout diverse peer-reviewed journals. This compilation of 21 chapters showcases the cross-fertilization between the fields of time series modeling and seasonal adjustment, as is reflected both in the contents of the chapters and in their authorship, with contributors coming from academia and government statistical agencies. For easier perusal and absorption, the contents have been grouped into seven topical sections: Section I deals with periodic modeling of time series, introducing, applying, and comparing various seasonally periodic models Section II examines the estimation of time series components when models for series are misspecified in some sense, and the broader implications this has for seasonal adjustment and business cycle estimation Section III examines the quantification of error in X-11 seasonal adjustments, with comparisons to error in model-based seasonal adjustments Section IV discusses some practical problems that arise in seasonal adjustment: developing asymmetric trend-cycle filters, dealing with both temporal and contemporaneous benchmark constraints, detecting trading-day effects in monthly and quarterly time series, and using diagnostics in conjunction with model-based seasonal adjustment Section V explores outlier detection and the modeling of time series containing extreme values, developing new procedures and extending previous work Section VI examines some alternative models and inference procedures for analysis of seasonal economic time series Section VII deals with aspects of modeling, estimation, and forecasting for nonseasonal economic time series By presenting new methodological developments as well as pertinent empirical analyses and reviews of established methods, the book provides much that is stimulating and practically useful for the serious researcher and analyst of economic time series.

A thrilling behind-the-scenes exploration of how governments past and present have been led astray by bad data - and why it is so hard to measure things and to do it well.

Our politicians make vital decisions and declarations every day that rely on official data. But should all statistics be trusted? In BAD DATA, House of Commons Library statistician Georgina Sturge draws back the curtain on how governments of the past and present have been led astray by figures littered with inconsistency, guesswork and uncertainty.

Discover how a Hungarian businessman's bright idea caused half a million people to go missing from UK migration statistics. Find out why it's possible for two politicians to disagree over whether poverty has gone up or down, using the same official numbers, and for both to be right at the same time. And hear about how policies like ID cards, super-casinos and stopping ex-convicts from reoffending failed to live up to their promise because they were based on shaky data.

With stories that range from the troubling to the empowering to the downright absurd, BAD DATA reveals secrets from the usually closed-off world of policy-making. It also suggests how - once we understand the human story behind the numbers - we can make more informed choices about who to trust, and when.

This book analyzes the following four distinct, although not dissimilar, areas of social choice theory and welfare economics: nonstrategic choice, Harsanyi's aggregation theorems, distributional ethics and strategic choice. While for aggregation of individual ranking of social states, whether the persons behave strategically or non-strategically, the decision making takes place under complete certainty; in the Harsanyi framework uncertainty has a significant role in the decision making process. Another ingenious characteristic of the book is the discussion of ethical approaches to evaluation of inequality arising from unequal distributions of achievements in the different dimensions of human well-being. Given its wide coverage, combined with newly added materials, end-chapter problems and bibliographical notes, the book will be helpful material for students and researchers interested in this frontline area research. Its lucid exposition, along with non-technical and graphical illustration of the concepts, use of numerical examples, makes the book a useful text.

Formal Models of Domestic Politics offers a unified and accessible approach to canonical and important new models of politics. Intended for political science and economics students who have already taken a course in game theory, this new edition retains the widely appreciated pedagogic approach of the first edition. Coverage has been expanded to include a new chapter on nondemocracy; new material on valance and issue ownership, dynamic veto and legislative bargaining, delegation to leaders by imperfectly informed politicians, and voter competence; and numerous additional exercises. Political economists, comparativists, and Americanists will all find models in the text central to their research interests. This leading graduate textbook assumes no mathematical knowledge beyond basic calculus, with an emphasis placed on clarity of presentation. Political scientists will appreciate the simplification of economic environments to focus on the political logic of models; economists will discover many important models published outside of their discipline; and both instructors and students will value the classroom-tested exercises. This is a vital update to a classic text.

Social media has made charts, infographics and diagrams ubiquitous-and easier to share than ever. While such visualisations can better inform us, they can also deceive by displaying incomplete or inaccurate data, suggesting misleading patterns-or misinform by being poorly designed. Many of us are ill equipped to interpret the visuals that politicians, journalists, advertisers and even employers present each day, enabling bad actors to easily manipulate visuals to promote their own agendas. Public conversations are increasingly driven by numbers and to make sense of them, we must be able to decode and use visual information. By examining contemporary examples ranging from election-result infographics to global GDP maps and box-office record charts, How Charts Lie teaches us how to do just that.